INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81073, 2017, 'bio', 'Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction', 'Yu, Kun-Hsing|Fitzpatrick, Michael R|Pappas, Luke|Chan, Warren|Kung, Jessica|Snyder, Michael', 'Biomedical Informatics Program, Stanford University, 1265 Welch Road, MSOB, X-215, MC 5479, Stanford, CA 94305-5479. Contact:khyu@stanford.edu , mpsnyder@stanford.edu.|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305-9025.|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305-9025.|Biomedical Informatics Program, Stanford University, 1265 Welch Road, MSOB, X-215, MC 5479, Stanford, CA 94305-5479. Contact:khyu@stanford.edu , mpsnyder@stanford.edu.|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305-9025.|Department of Genetics, Stanford University, 300 Pasteur Dr., M-344, Stanford, CA 94305-5120.', '-|-|-|-|-|-', 8, '-', 'Journal Article', '10.1093/bioinformatics/btx572', '28968749', 'Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans.'),
(81074, 2017, 'bio', 'A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network', 'Zhong, Yingli|Xuan, Ping|Wang, Xiao|Zhang, Tiangang|Li, Jianzhong|Liu, Yong|Zhang, Weixiong', 'School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.|Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.|School of Mathematical Science, Heilongjiang University, Harbin, 150080, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.|Department of Computer Science and Engineering, Department of Genetics, Washington University in St. Louis, Saint Louis, 63130, USA.', 'China|China|China|China|China|China|United States', 18, '-', 'Journal Article', '10.1093/bioinformatics/btx546', '28968753', 'Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem.'),
(81075, 2018, 'bio', 'Novel features and enhancements in BioBin, a tool for the biologically inspired binning and association analysis of rare variants', 'Basile, Anna O|Byrska-Bishop, Marta|Wallace, John|Frase, Alexander T|Ritchie, Marylyn D', 'Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA.|Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17822 USA.|Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17822 USA.|Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17822 USA.|Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17822 USA.', 'United States|United States|United States|United States|United States', 3, 'Algorithms|Genetic Association Studies|Genetic Variation|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btx559', '28968757', 'BioBin is an automated bioinformatics tool for the multi-level biological binning of sequence variants. Herein, we present a significant update to BioBin which expands the software to facilitate a comprehensive rare variant analysis and incorporates novel features and analysis enhancements.'),
(81076, 2018, 'bio', 'FMtree: a fast locating algorithm of FM-indexes for genomic data', 'Cheng, Haoyu|Wu, Ming|Xu, Yun', 'School of Computer Science, University of Science and Technology of China, Heifei, Anhui 230027, China.|School of Computer Science, University of Science and Technology of China, Heifei, Anhui 230027, China.|School of Computer Science, University of Science and Technology of China, Heifei, Anhui 230027, China.', 'China|China|China', 0, 'Algorithms|Animals|Genome|Genomics|Humans|Mice|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx596', '28968761', 'As a fundamental task in bioinformatics, searching for massive short patterns over a long text has been accelerated by various compressed full-text indexes. These indexes are able to provide similar searching functionalities to classical indexes, e.g. suffix trees and suffix arrays, while requiring less space. For genomic data, a well-known family of compressed full-text indexes, called FM-indexes, presents unmatched performance in practice. One major drawback of FM-indexes is that their locating operations, which report all occurrence positions of patterns in a given text, are not efficient, especially for the patterns with many occurrences.'),
(81077, 2018, 'bio', 'JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts', 'Chatzinakos, Chris|Lee, Donghyung|Webb, Bradley T|Vladimirov, Vladimir I|Kendler, Kenneth S|Bacanu, Silviu-Alin', 'Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.', 'United States|United States|United States|United States|United States|United States', 3, 'Gene Expression Profiling|Gene Expression Regulation|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btx509', '28968763', 'To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.'),
(81078, 2018, 'bio', 'CRISPR-RT: a web application for designing CRISPR-C2c2 crRNA with improved target specificity', 'Zhu, Houxiang|Richmond, Emily|Liang, Chun', 'Department of Biology,Miami University, Oxford, OH 45056, USA.|Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA.|Department of Biology, Miami University, Oxford, OH 45056, USA.', 'United States|United States|United States', 3, 'CRISPR-Cas Systems|Genetic Engineering|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btx580', '28968770', 'CRISPR-Cas systems have been successfully applied in genome editing. Recently, the CRISPR-C2c2 system has been reported as a tool for RNA editing. Here we describe CRISPR-RT (CRISPR RNA-Targeting), the first web application to help biologists design crRNAs with improved target specificity for the CRISPR-C2c2 system. CRISPR-RT allows users to set up a wide range of parameters, making it highly flexible for current and future research in CRISPR-based RNA editing. CRISPR-RT covers major model organisms and can be easily extended to cover other species. CRISPR-RT will empower researchers in RNA editing.'),
(81079, 2018, 'bio', 'Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism', 'Angione, Claudio', 'Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK.', 'United Kingdom', 0, 'Alternative Splicing|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Protein Isoforms|RNA Splicing|Software', 'Journal Article', '10.1093/bioinformatics/btx562', '28968777', 'Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoform expression levels alter the metabolic outcome cannot be modeled.'),
(81080, 2017, 'bio', 'A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations', 'Xiao, Qiu|Luo, Jiawei|Liang, Cheng|Cai, Jie|Ding, Pingjian', 'College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China. Contact:luojiawei@hnu.edu.cn , hnuyldf@hnu.edu.cn.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China. Contact:luojiawei@hnu.edu.cn , hnuyldf@hnu.edu.cn.|College of Information Science and Engineering, Shandong Normal University, Jinan, 250000, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China. Contact:luojiawei@hnu.edu.cn , hnuyldf@hnu.edu.cn.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China. Contact:luojiawei@hnu.edu.cn , hnuyldf@hnu.edu.cn.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx545', '28968779', 'MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.'),
(81081, 2018, 'bio', 'SMLocalizer, a GPU accelerated ImageJ plugin for single molecule localization microscopy', 'Bernhem, Kristoffer|Brismar, Hjalmar', 'Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, Stockholm, Sweden.|Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden', 0, 'Image Processing, Computer-Assisted|Single Molecule Imaging|Software', 'Journal Article', '10.1093/bioinformatics/btx553', '28968783', 'SMLocalizer combines the availability of ImageJ with the power of GPU processing for fast and accurate analysis of single molecule localization microscopy data. Analysis of 2D and 3D data in multiple channels is supported.'),
(81082, 2018, 'bio', 'CSAR: a contig scaffolding tool using algebraic rearrangements', 'Chen, Kun-Tze|Liu, Chia-Liang|Huang, Shang-Hao|Shen, Hsin-Ting|Shieh, Yi-Kung|Chiu, Hsien-Tai|Lu, Chin Lung', 'Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.|Department of Chemistry, National Cheng Kung University, Tainan City 70101, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.', '-|-|-|-|-|-|-', 4, 'Algorithms|Bacteria|Contig Mapping|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx543', '28968788', 'Advances in next generation sequencing have generated massive amounts of short reads. However, assembling genome sequences from short reads still remains a challenging task. Due to errors in reads and large repeats in the genome, many of current assembly tools usually produce just collections of contigs whose relative positions and orientations along the genome being sequenced are still unknown. To address this issue, a scaffolding process to order and orient the contigs of a draft genome is needed for completing the genome sequence. In this work, we propose a new scaffolding tool called CSAR that can efficiently and more accurately order and orient the contigs of a given draft genome based on a reference genome of a related organism. In particular, the reference genome required by CSAR is not necessary to be complete in sequence. Our experimental results on real datasets have shown that CSAR outperforms other similar tools such as Projector2, OSLay and Mauve Aligner in terms of average sensitivity, precision, F-score, genome coverage, NGA50 and running time.'),
(81083, 2018, 'bio', 'Mobi 20: an improved method to define intrinsic disorder, mobility and linear binding regions in protein structures', 'Piovesan, Damiano|Tosatto, Silvio C E', 'Department of Biomedical Sciences, University of Padua Viale G. Colombo 3, Padova 35121, Italy.|Department of Biomedical Sciences, University of Padua Viale G. Colombo 3, Padova 35121, Italy.', 'Italy|Italy', 0, 'Computational Biology|Databases, Protein|Models, Molecular|Molecular Sequence Annotation|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx592', '28968795', 'The structures contained in the Protein Data Bank (PDB) database are of paramount importance to define our knowledge of folded proteins. While providing mainly circumstantial evidence, PDB data is also increasingly used to define the lack of unique structure, represented by mobile regions and even intrinsic disorder (ID). However, alternative definitions are used by different authors and potentially limit the generality of the analyses being carried out.'),
(81084, 2018, 'bio', 'iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC', 'Liu, Bin|Yang, Fan|Huang, De-Shuang|Chou, Kuo-Chen', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.|The Gordon Life Science Institute, Boston, MA 02478, USA.', 'China|China|China|United States', 164, 'DNA, Bacterial|DNA-Directed RNA Polymerases|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|Genomics|Promoter Regions, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx579', '28968797', 'Being responsible for initiating transaction of a particular gene in genome, promoter is a short region of DNA. Promoters have various types with different functions. Owing to their importance in biological process, it is highly desired to develop computational tools for timely identifying promoters and their types. Such a challenge has become particularly critical and urgent in facing the avalanche of DNA sequences discovered in the postgenomic age. Although some prediction methods were developed, they can only be used to discriminate a specific type of promoters from non-promoters. None of them has the ability to identify the types of promoters. This is due to the facts that different types of promoters may share quite similar consensus sequence pattern, and that the promoters of same type may have considerably different consensus sequences.'),
(81085, 2018, 'bio', 'Enhanced guide-RNA design and targeting analysis for precise CRISPR genome editing of single and consortia of industrially relevant and non-model organisms', 'Mendoza, Brian J|Trinh, Cong T', 'Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN 37996, USA.|Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN 37996, USA.', 'United States|United States', 12, 'Bacteria|CRISPR-Cas Systems|Eukaryota|Gene Editing|Genomics|RNA, Guide|Software', 'Journal Article', '10.1093/bioinformatics/btx564', '28968798', 'Genetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions.'),
(81086, 2018, 'bio', 'Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing', 'Äijö, Tarmo|Müller, Christian L|Bonneau, Richard', 'Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA.|Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA.|Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA.', 'United States|United States|United States', 0, 'Bacteria|Gastrointestinal Microbiome|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Models, Statistical|RNA, Ribosomal, 16S|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx549', '28968799', 'The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.'),
(81087, 2017, 'bio', 'Ms2ldaorg: web-based topic modelling for substructure discovery in mass spectrometry', 'Wandy, Joe|Zhu, Yunfeng|van der Hooft, Justin J J|Daly, Rónán|Barrett, Michael P|Rogers, Simon', 'Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom. Contact:simon.rogers@glasgow.ac.uk.|Wellcome Centre for Molecular Parasitology, Glasgow, United Kingdom.|Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom. Contact:simon.rogers@glasgow.ac.uk.|Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom. Contact:simon.rogers@glasgow.ac.uk.|Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom. Contact:simon.rogers@glasgow.ac.uk.|School of Computing Science, University of Glasgow, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx582', '28968802', 'We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations.'),
(81088, 2017, 'bio', 'A Novel SCCA Approach via Truncated xe2x84x931-norm and Truncated Group Lasso for Brain Imaging Genetics', 'Du, Lei|Liu, Kefei|Zhang, Tuo|Yao, Xiaohui|Yan, Jingwen|Risacher, Shannon L|Han, Junwei|Guo, Lei|Saykin, Andrew J|Shen, Li|Alzheimerxe2x80x99s Disease Neuroimaging Initiative', 'School of Automation, Northwestern Polytechnical University, Xian, China. Contact:Li Shen: shenli@iu.edu and Lei Du: dulei@nwpu.edu.cn .|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|School of Automation, Northwestern Polytechnical University, Xian, China. Contact:Li Shen: shenli@iu.edu and Lei Du: dulei@nwpu.edu.cn .|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|School of Automation, Northwestern Polytechnical University, Xian, China. Contact:Li Shen: shenli@iu.edu and Lei Du: dulei@nwpu.edu.cn .|School of Automation, Northwestern Polytechnical University, Xian, China. Contact:Li Shen: shenli@iu.edu and Lei Du: dulei@nwpu.edu.cn .|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine,IN, USA.', 'China|India|China|India|India|India|China|China|India|India|India', 11, '-', 'Journal Article', '10.1093/bioinformatics/btx594', '28968815', 'Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the xe2x84x93 1 -norm or its variants to induce sparsity. The xe2x84x93 0 -norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem.'),
(81089, 2017, 'bio', 'SiNoPsis: Single Nucleotide Polymorphisms selection and promoter profiling', 'Boloc, Daniel|Rodríguez, Natalia|Gassó, Patricia|Abril, Josep F|Bernardo, Miquel|Lafuente, Amalia|Mas, Sergi', 'Dep. de Medicina, University of Barcelona, Barcelona, Catalonia, Spain. Contact: danielboloc@gmail.com|Dep. Fonaments Clínics, Unitat de Farmacologia, University of Barcelona, Barcelona, Catalonia, Spain.|Dep. Fonaments Clínics, Unitat de Farmacologia, University of Barcelona, Barcelona, Catalonia, Spain.|Dep. de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain.|Unitat Esquizofrènia, Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain.|Dep. Fonaments Clínics, Unitat de Farmacologia, University of Barcelona, Barcelona, Catalonia, Spain.|Dep. Fonaments Clínics, Unitat de Farmacologia, University of Barcelona, Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx570', '28968821', 'The selection of a Single Nucleotide Polymorphism (SNP) using bibliographic methods can be a very time-consuming task. Moreover, a SNP selected in this way may not be easily visualized in its genomic context by a standard user hoping to correlate it with other valuable information. Here we propose a web form built on top of Circos that can assist SNP-centred screening, based on their location in the genome and the regulatory modules they can disrupt. Its use may allow researchers to prioritize SNPs in genotyping and disease studies.'),
(81090, 2017, 'bio', 'DART - a fast and accurate RNA-seq mapper with a partitioning strategy', 'Lin, Hsin-Nan|Hsu, Wen-Lian', 'Institute of Information Science, Academia Sinica, Taipei, Taiwan. Contact:hsu@iis.sinica.edu.tw.|Institute of Information Science, Academia Sinica, Taipei, Taiwan. Contact:hsu@iis.sinica.edu.tw.', '-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btx558', '28968831', 'In recent years, the massively-parallel cDNA sequencing (RNA-Seq) technologies have become a powerful tool to provide high resolution measurement of expression and high sensitivity in detecting low abundance transcripts. However, RNA-seq data requires a huge amount of computational efforts. The very fundamental and critical step is to align each sequence fragment against the reference genome. Various de novo spliced RNA aligners have been developed in recent years. Though these aligners can handle spliced alignment and detect splice junctions, some challenges still remain to be solved. With the advances in sequencing technologies and the ongoing collection of sequencing data in the ENCODE project, more efficient alignment algorithms are highly demanded. Most read mappers follow the conventional seed-and-extend strategy to deal with inexact matches for sequence alignment. However, the extension is much more time consuming than the seeding step.'),
(81091, 2018, 'bio', 'A comprehensive assessment of long intrinsic protein disorder from the DisProt database', 'Necci, Marco|Piovesan, Damiano|Dosztányi, Zsuzsanna|Tompa, Peter|Tosatto, Silvio C E', 'Department of Biomedical Sciences, University of Padua, 35131 Padova, Italy.|Department of Biomedical Sciences, University of Padua, 35131 Padova, Italy.|Agricoltural Sciences, University of Udine, 33100 Udine, Italy.|Fondazione Edmund Mach, 38010 S. Michele allAdige, Italy.|Department of Biomedical Sciences, University of Padua, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy', 24, 'Computational Biology|Databases, Protein|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx590', '28968848', 'Intrinsic disorder (ID), i.e. the lack of a unique folded conformation at physiological conditions, is a common feature for many proteins, which requires specialized biochemical experiments that are not high-throughput. Missing X-ray residues from the PDB have been widely used as a proxy for ID when developing computational methods. This may lead to a systematic bias, where predictors deviate from biologically relevant ID. Large benchmarking sets on experimentally validated ID are scarce. Recently, the DisProt database has been renewed and expanded to include manually curated ID annotations for several hundred new proteins. This provides a large benchmark set which has not yet been used for training ID predictors.'),
(81092, 2017, 'bio', 'Gearing up to handle the mosaic nature of life in the quest for orthologs', 'Forslund, Kristoffer|Pereira, Cecile|Capella-Gutierrez, Salvador|Sousa da Silva, Alan|Altenhoff, Adrian|Huerta-Cepas, Jaime|Muffato, Matthieu|Patricio, Mateus|Vandepoele, Klaas|Ebersberger, Ingo|Blake, Judith|Fernández Breis, Jesualdo Tomás|Quest for Orthologs Consortium|Boeckmann, Brigitte|Gabaldón, Toni|Sonnhammer, Erik|Dessimoz, Christophe|Lewis, Suzanna', 'Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida at Gainesville, Gainesville, FL, USA.|Spanish National Bioinformatics Institute (INB), Spanish National Cancer Research Centre, Madrid, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|Dept. of Computer Science, ETH Zurich, Zurich, Switzerland.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|Center for Plant Systems Biology, VIB & Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.|Applied Bioinformatics, Goethe-Universitet Frankfurt, Frankfurt, Germany.|Jackson Laboratory, Bar Harbor, ME, USA.|University of Murcia, Murcia, Spain.|University of Murcia, Murcia, Spain.|Swiss-Prot Group, Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.|Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.|Stockholm Bioinformatics Center, Dept. of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Solna, Sweden.|Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.|Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA.', 'Germany|United Kingdom|Spain|United Kingdom|Switzerland|Germany|United Kingdom|United Kingdom|Belgium|Germany|United Kingdom|Spain|Spain|Switzerland|Spain|Sweden|Swi', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx542', '28968857', 'The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated, and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families, and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting.'),
(81093, 2018, 'bio', 'PySCeSToolbox: a collection of metabolic pathway analysis tools', 'Christensen, Carl D|Hofmeyr, Jan-Hendrik S|Rohwer, Johann M', 'Department of Biochemistry, Laboratory for Molecular Systems Biology, Stellenbosch University, South Africa.|Department of Biochemistry, Laboratory for Molecular Systems Biology, Stellenbosch University, South Africa.|Department of Biochemistry, Laboratory for Molecular Systems Biology, Stellenbosch University, South Africa.', 'South Africa|South Africa|South Africa', 0, 'Computational Biology|Enzymes|Kinetics|Metabolic Networks and Pathways|Models, Biological|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx567', '28968872', 'PySCeSToolbox is an extension to the Python Simulator for Cellular Systems (PySCeS) that includes tools for performing generalized supply-demand analysis, symbolic metabolic control analysis, and a framework for investigating the kinetic and thermodynamic aspects of enzyme-catalyzed reactions. Each tool addresses a different aspect of metabolic behaviour, control, and regulation, the tools complement each other and can be used in conjunction to better understand higher level system behaviour.'),
(81094, 2017, 'bio', 'Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim)', 'Yermanos, Alexander|Greiff, Victor|Krautler, Nike Julia|Menzel, Ulrike|Dounas, Andreas|Miho, Enkelejda|Oxenius, Annette|Stadler, Tanja|Reddy, Sai T', 'Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.|Institute of Microbiology.|Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.|Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.|Institute of Microbiology.|Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.', 'Switzerland|Switzerland|-|Switzerland|Switzerland|Switzerland|-|Switzerland|Switzerland', 11, 'Animals|Antibodies|B-Lymphocytes|Cell Lineage|Computer Simulation|Evolution, Molecular|Female|Genes, Immunoglobulin|High-Throughput Nucleotide Sequencing|Mice|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/btx533', '28968873', 'The evolution of antibody repertoires represents a hallmark feature of adaptive B-cell immunity. Recent advancements in high-throughput sequencing have dramatically increased the resolution to which we can measure the molecular diversity of antibody repertoires, thereby offering for the first time the possibility to capture the antigen-driven evolution of B cells. However, there does not exist a repertoire simulation framework yet that enables the comparison of commonly utilized phylogenetic methods with regard to their accuracy in inferring antibody evolution.'),
(81095, 2018, 'bio', 'High dimensional classification with combined adaptive sparse PLS and logistic regression', 'Durif, Ghislain|Modolo, Laurent|Michaelsson, Jakob|Mold, Jeff E|Lambert-Lacroix, Sophie|Picard, Franck', 'LBBE, UMR CNRS 5558, Université Lyon 1, F-69622 Villeurbanne, France.|LBBE, UMR CNRS 5558, Université Lyon 1, F-69622 Villeurbanne, France.|Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.|Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.|UMR 5525 Université Grenoble Alpes/CNRS/TIMC-IMAG, F-38041 Grenoble, France.|LBBE, UMR CNRS 5558, Université Lyon 1, F-69622 Villeurbanne, France.', 'France|France|Sweden|Sweden|France|France', 0, 'Calibration|Genomics|Least-Squares Analysis|Logistic Models|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx571', '28968879', 'The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to unstable and non convergent methods due to inappropriate computational frameworks. We hereby propose a computationally stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS).'),
(81096, 2018, 'bio', 'Applying family analyses to electronic health records to facilitate genetic research', 'Huang, Xiayuan|Elston, Robert C|Rosa, Guilherme J|Mayer, John|Ye, Zhan|Kitchner, Terrie|Brilliant, Murray H|Page, David|Hebbring, Scott J|Stegle, Oliver', 'Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.|Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Animal Science, University of Wisconsin-Madison, Madison, WI 53706, USA.|Biomedical Informatics Research Center.|Biomedical Informatics Research Center.|Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.|Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.|Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.|Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Chromosome Mapping|Databases, Factual|Electronic Health Records|Female|Genetic Association Studies|Genetic Diseases, Inborn|Genetic Research|Genome, Human|Humans|Male|Middle Aged|Pedigree', 'Journal Article', '10.1093/bioinformatics/btx569', '28968884', 'Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary.'),
(81097, 2017, 'bio', 'pulseR: Versatile computational analysis of RNA turnover from metabolic labeling experiments', 'Uvarovskii, Alexey|Dieterich, Christoph', 'Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg.|Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg.', '-|-', 5, 'Computational Biology|Gene Expression Profiling|Models, Biological|Models, Statistical|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx368', '29028260', 'Metabolic labelling of RNA is a well-established and powerful method to estimate RNA synthesis and decay rates. The pulseR R package simplifies the analysis of RNA-seq count data that emerge from corresponding pulse-chase experiments.'),
(81098, 2017, 'bio', 'Panoptes: web-based exploration of large scale genome variation data', 'Vauterin, Paul|Jeffery, Ben|Miles, Alistair|Amato, Roberto|Hart, Lee|Wright, Ian|Kwiatkowski, Dominic', 'MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.|MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.|MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.|Malaria Programme, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10xe2x80x891SA, UK.|MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.|MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.|MRC Centre for Genomics and Global Health, University of Oxford, Oxford OX3xe2x80x897BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Anopheles|Genetic Variation|Genomics|Internet|Metadata|Plasmodium falciparum|Plasmodium vivax|Sequence Analysis, DNA|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btx410', '29028261', 'The size and complexity of modern large-scale genome variation studies demand novel approaches for exploring and sharing the data. In order to unlock the potential of these data for a broad audience of scientists with various areas of expertise, a unified exploration framework is required that is accessible, coherent and user-friendly.'),
(81099, 2017, 'bio', 'TSIS: an R package to infer alternative splicing isoform switches for time-series data', 'Guo, Wenbin|Calixto, Cristiane P G|Brown, John W S|Zhang, Runxuan', 'Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland DD2xe2x80x895DA, UK.|Plant Sciences Division, School of Life Sciences, University of Dundee, Invergowrie, Dundee, Scotland DD2xe2x80x895DA, UK.|Plant Sciences Division, School of Life Sciences, University of Dundee, Invergowrie, Dundee, Scotland DD2xe2x80x895DA, UK.|Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland DD2xe2x80x895DA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Alternative Splicing|Computational Biology|Protein Isoforms|Software', 'Journal Article', '10.1093/bioinformatics/btx411', '29028262', 'An alternative splicing isoform switch is where a pair of transcript isoforms reverse their relative expression abundances in response to external or internal stimuli. Although computational methods are available to study differential alternative splicing, few tools for detection of isoform switches exist and these are based on pairwise comparisons. Here, we provide the TSIS R package, which is the first tool for detecting significant transcript isoform switches in time-series data. The main steps of TSIS are to search for the isoform switch points in the time-series, characterize the switches and filter the results with user input parameters. All the functions are integrated into a Shiny App for ease of implementation of the analysis.'),
(81100, 2017, 'bio', 'Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning', 'Dozmorov, Mikhail G', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA.', 'United States', 7, 'Epigenomics|Guidelines as Topic|Humans|Machine Learning|Molecular Sequence Annotation|Software', 'Comparative Study', '10.1093/bioinformatics/btx414', '29028263', 'One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data.'),
(81101, 2017, 'bio', 'TeachEnG: a Teaching Engine for Genomics', 'Kim, Minji|Kim, Yeonsung|Qian, Lei|Song, Jun S', 'Department of Electrical and Computer Engineering.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Mathematics and Computer Science, Fisk University, Nashville, TN 37208, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Genomics|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx447', '29028264', 'Bioinformatics is a rapidly growing field that has emerged from the synergy of computer science, statistics and biology. Given the interdisciplinary nature of bioinformatics, many students from diverse fields struggle with grasping bioinformatic concepts only from classroom lectures. Interactive tools for helping students reinforce their learning would be thus desirable. Here, we present an interactive online educational tool called TeachEnG (acronym for Teaching Engine for Genomics) for reinforcing key concepts in sequence alignment and phylogenetic tree reconstruction. Our instructional games allow students to align sequences by hand, fill out the dynamic programming matrix in the Needleman-Wunsch global sequence alignment algorithm, and reconstruct phylogenetic trees via the maximum parsimony, Unweighted Pair Group Method with Arithmetic mean (UPGMA) and Neighbor-Joining algorithms. With an easily accessible interface and instant visual feedback, TeachEnG will help promote active learning in bioinformatics.'),
(81102, 2017, 'bio', 'StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data', 'Stavrovskaya, Elena D|Niranjan, Tejasvi|Fertig, Elana J|Wheelan, Sarah J|Favorov, Alexander V|Mironov, Andrey A', 'Department of Bioengineering and Bioinformatics, Moscow State University, Moscow 119992, Russia.|Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.|Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.|Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.|Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.|Department of Bioengineering and Bioinformatics, Moscow State University, Moscow 119992, Russia.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Chromatin Immunoprecipitation|Epigenomics|Gene Expression Regulation|Genome, Human|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx379', '29028265', 'Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81103, 2017, 'bio', 'CircPro: an integrated tool for the identification of circRNAs with protein-coding potential', 'Meng, Xianwen|Chen, Qi|Zhang, Peijing|Chen, Ming', 'Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.', 'China|China|China|China', 28, 'Eukaryota|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|MCF-7 Cells|RNA|RNA Splicing|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx446', '29028266', 'Circular RNAs (circRNAs), a novel class of endogenous RNAs, are widespread in eukaryotic cells. Emerging roles in diverse biological processes suggest that circRNA is a promising key player in RNA world. Most circRNAs are generated through back-splicing of pre-mRNAs, forming a covalently closed loop structure with no 5 caps or 3 polyadenylated tails. In addition, most circRNAs were not associated with translating ribosomes, therefore, circRNAs were deemed to be noncoding. However, the latest research findings revealed that some circRNAs could generate proteins in vivo, which expands the landscape of transcriptome and proteome. To gain insights into the new area of circRNA translation, we introduce an integrated tool capable of detecting circRNAs with protein-coding potential from high-throughput sequencing data.'),
(81104, 2017, 'bio', 'VariantTools: an extensible framework for developing and testing variant callers', 'Lawrence, Michael|Gentleman, Robert', 'Department of Bioinformatics, Genentech, South San Francisco, CA, USA.|23andMe, Mountain View, CA, USA.', 'United States|United States', 4, 'Genomics|Genotyping Techniques|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx450', '29028267', 'Variant calling is the complex task of separating real polymorphisms from errors. The appropriate strategy will depend on characteristics of the sample, the sequencing methodology and on the questions of interest.'),
(81105, 2017, 'bio', 'RINspector: a Cytoscape app for centrality analyses and DynaMine flexibility prediction', 'Brysbaert, Guillaume|Lorgouilloux, Kevin|Vranken, Wim|Lensink, Marc F', 'University of Lille, CNRS UMR8576 UGSF, F-59000 Lille, France.|University of Lille, CNRS UMR8576 UGSF, F-59000 Lille, France.|Interuniversity Institute of Bioinformatics in Brussels (IB)2, Structural Biology Brussels, VUB, Centre for Structural Biology, VIB, Brussels, Belgium.|University of Lille, CNRS UMR8576 UGSF, F-59000 Lille, France.', 'France|France|Belgium|France', 7, '-', 'Journal Article', '10.1093/bioinformatics/btx586', '29028877', 'Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments.'),
(81106, 2018, 'bio', 'SNP genotyping and parameter estimation in polyploids using low-coverage sequencing data', 'Blischak, Paul D|Kubatko, Laura S|Wolfe, Andrea D', 'Department of Evolution, Ecology, and Organismal Biology.|Department of Evolution, Ecology, and Organismal Biology.|Department of Evolution, Ecology, and Organismal Biology.', '-|-|-', 15, 'Alleles|Animals|Eukaryota|Genetics, Population|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Inbreeding|Polymorphism, Single Nucleotide|Polyploidy|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx587', '29028881', 'Genotyping and parameter estimation using high throughput sequencing data are everyday tasks for population geneticists, but methods developed for diploids are typically not applicable to polyploid taxa. This is due to their duplicated chromosomes, as well as the complex patterns of allelic exchange that often accompany whole genome duplication (WGD) events. For WGDs within a single lineage (autopolyploids), inbreeding can result from mixed mating and/or double reduction. For WGDs that involve hybridization (allopolyploids), alleles are typically inherited through independently segregating subgenomes.'),
(81107, 2018, 'bio', 'Protein-protein interaction specificity is captured by contact preferences and interface composition', 'Nadalin, Francesca|Carbone, Alessandra', 'Sorbonne Universités, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, 75005 Paris, France.|Sorbonne Universités, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, 75005 Paris, France.', 'France|France', 8, 'Computational Biology|Molecular Docking Simulation|Protein Interaction Mapping|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btx584', '29028884', 'Large-scale computational docking will be increasingly used in future years to discriminate protein-protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein-protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations.'),
(81108, 2017, 'bio', 'HoTResDB: Host Transcriptional Response Database for Viral Hemorrhagic Fevers', 'Lo, Jonathan|Zhang, Deric|Speranza, Emily|Negron, Jose A|Connor, John H', 'Bioinformatics Program, Boston University, 24 Cummington Mall, Boston MA, 02215, US.|Bioinformatics Program, Boston University, 24 Cummington Mall, Boston MA, 02215, US.|Bioinformatics Program, Boston University, 24 Cummington Mall, Boston MA, 02215, US.|Bioinformatics Program, Boston University, 24 Cummington Mall, Boston MA, 02215, US.|Bioinformatics Program, Boston University, 24 Cummington Mall, Boston MA, 02215, US.', '-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx599', '29028885', 'High-throughput screening of the host transcriptional response to various viral infections provides a wealth of data but utilization of microarray and next generation sequencing (NGS) data for analysis can be difficult. The Host Transcriptional Response DataBase (HoTResDB), allows visitors to access already processed microarray and NGS data from non-human primate models of viral hemorrhagic fever to better understand the host transcriptional response.'),
(81109, 2018, 'bio', 'Identification of cancer driver genes in focal genomic aberrations from whole-exome sequencing data', 'Jang, Ho|Lee, Hyunju', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 500-712, South Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 500-712, South Korea.', '-|-', 2, 'Adenocarcinoma|Adenocarcinoma of Lung|Genomics|Glioblastoma|High-Throughput Nucleotide Sequencing|Humans|Lung Neoplasms|Oncogenes|Software|Whole Exome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx620', '29028887', 'Whole-exome sequencing (WES) data have been used for identifying copy number aberrations in cancer cells. Nonetheless, the use of WES is still challenging for identification of focal aberrant regions in multiple samples that may contain cancer driver genes. In this study, we developed a wavelet-based method for identifying focal genomic aberrant regions in the WES data from cancer cells (WIFA-X). When we applied WIFA-X to glioblastoma multiforme and lung adenocarcinoma datasets, WIFA-X outperformed other approaches on identifying cancer driver genes.'),
(81110, 2018, 'bio', 'RRDB: a comprehensive and non-redundant benchmark for RNA-RNA docking and scoring', 'Yan, Yumeng|Huang, Sheng-You', 'School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, Peoples Republic of China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, Peoples Republic of China.', 'China|China', 2, 'Algorithms|Benchmarking|Computational Biology|Molecular Docking Simulation|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx615', '29028888', 'With the discovery of more and more noncoding RNAs and their versatile functions, RNA-RNA interactions have received increased attention. Therefore, determination of their complex structures is valuable to understand the molecular mechanism of the interactions. Given the high cost of experimental methods, computational approaches like molecular docking have played an important role in the determination of complex structures, in which a benchmark is critical for the development of docking algorithms.'),
(81111, 2018, 'bio', 'Segway 20: Gaussian mixture models and minibatch training', 'Chan, Rachel C W|Libbrecht, Maxwell W|Roberts, Eric G|Bilmes, Jeffrey A|Noble, William Stafford|Hoffman, Michael M', 'Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.|Department of Computer Science and Engineering.|Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.|Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.', 'Canada|-|Canada|-|-|Canada', 0, 'Eukaryota|Genomics|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx603', '29028889', 'Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.'),
(81112, 2018, 'bio', 'MALDIrppa: quality control and robust analysis for mass spectrometry data', 'Palarea-Albaladejo, Javier|Mclean, Kevin|Wright, Frank|Smith, David G E', 'Biomathematics and Statistics Scotland, JCMB, The Kings Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK.|Proteomics Facility Services, Moredun Research Institute, Pentland Science Park, Bush Loan, Penicuik, Mid Lothian, EH26 0PZ, UK.|Biomathematics and Statistics Scotland, JCMB, The Kings Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK.|Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh, EH14 4AS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Computational Biology|High-Throughput Screening Assays|Mass Spectrometry|Quality Control|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx628', '29028890', 'This R package helps to implement a robust approach to deal with mass spectrometry (MS) data. It is aimed at alleviating reproducibility issues and pernicious effects of deviating signals on both data pre-processing and downstream data analysis. Based on robust statistical methods, it facilitates the identification and filtering of low-quality mass spectra and atypical peak profiles as well as monitoring and data handling through pre-processing, which extends existing computational tools for high-throughput data.'),
(81113, 2018, 'bio', 'On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures', 'Marxc3xadn-Lxc3xb3pez, Manuel Alejandro|Planas-Iglesias, Joan|Aguirre-Plans, Joaquim|Bonet, Jaume|Garcia-Garcia, Javier|Fernandez-Fuentes, Narcis|Oliva, Baldo', 'Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain.|Division of Metabolic and Vascular Health, University of Warwick, Coventry CV4?7AL, UK.|Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain.|Laboratory of Protein Design and Immunoenginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland.|Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23?3DA, UK.|Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain.', 'Spain|United Kingdom|Spain|Switzerland|Spain|United Kingdom|Spain', 6, 'Computational Biology|Molecular Docking Simulation|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx616', '29028891', 'The characterization of the protein-protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein-protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures.'),
(81114, 2018, 'bio', 'Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis', 'Weber, Nick|Liou, David|Dommer, Jennifer|MacMenamin, Philip|Quiñones, Mariam|Misner, Ian|Oler, Andrew J|Wan, Joe|Kim, Lewis|Coakley McCarthy, Meghan|Ezeji, Samuel|Noble, Karlynn|Hurt, Darrell E', 'Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 17, 'Cloud Computing|Computational Biology|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx617', '29028892', 'Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.'),
(81115, 2018, 'bio', 'Canvas SPW: calling de novo copy number variants in pedigrees', 'Ivakhno, Sergii|Roller, Eric|Colombo, Camilla|Tedder, Philip|Cox, Anthony J', 'Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10?1XL, UK.|Illumina Inc., San Diego, CA 92122, USA.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10?1XL, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10?1XL, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10?1XL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'DNA Copy Number Variations|Genomics|Humans|Pedigree|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx618', '29028893', 'Whole genome sequencing is becoming a diagnostics of choice for the identification of rare inherited and de novo copy number variants in families with various pediatric and late-onset genetic diseases. However, joint variant calling in pedigrees is hampered by the complexity of consensus breakpoint alignment across samples within an arbitrary pedigree structure.'),
(81116, 2018, 'bio', 'AQUa: an adaptive framework for compression of sequencing quality scores with random access functionality', 'Paridaens, Tom|Van Wallendael, Glenn|De Neve, Wesley|Lambert, Peter', 'Department of Electronics and Information Systems, IDLab, Ghent University - IMEC, Ghent, Belgium.|Department of Electronics and Information Systems, IDLab, Ghent University - IMEC, Ghent, Belgium.|Department of Electronics and Information Systems, IDLab, Ghent University - IMEC, Ghent, Belgium.|Department of Electronics and Information Systems, IDLab, Ghent University - IMEC, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 1, 'Algorithms|Data Compression|Escherichia coli|Genomics|High-Throughput Nucleotide Sequencing|Humans|Metadata|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx607', '29028894', 'The past decade has seen the introduction of new technologies that significantly lowered the cost of genome sequencing. As a result, the amount of genomic data that must be stored and transmitted is increasing exponentially. To mitigate storage and transmission issues, we introduce a framework for lossless compression of quality scores.'),
(81117, 2017, 'bio', 'CCmiR: A computational approach for competitive and cooperative microRNA binding prediction', 'Ding, Jun|Li, Xiaoman|Hu, Haiyan', 'Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.|Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, 32816, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx606', '29028895', 'The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.'),
(81118, 2018, 'bio', 'PyDREAM: high-dimensional parameter inference for biological models in python', 'Shockley, Erin M|Vrugt, Jasper A|Lopez, Carlos F', 'Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN 37212, USA.|Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA 92697-2175, USA.|Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN 37212, USA.', 'United States|United States|United States', 14, 'Algorithms|Calibration|Computational Biology|Markov Chains|Models, Biological|Monte Carlo Method|Software|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btx626', '29028896', 'Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.'),
(81119, 2018, 'bio', 'Sipros Ensemble improves database searching and filtering for complex metaproteomics', 'Guo, Xuan|Li, Zhou|Yao, Qiuming|Mueller, Ryan S|Eng, Jimmy K|Tabb, David L|Hervey, William Judson|Pan, Chongle', 'Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Department of Microbiology, Oregon State University, Corvallis, OR 97331, USA.|Proteomics Resource, University of Washington, Seattle, WA 98195, USA.|DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.|Naval Research Laboratory, Center for Bio/Molecular Science & Engineering (Code 6910), Washington, DC, 20375, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.', 'United States|United States|United States|United States|United States|South Africa|United States|United States', 5, 'Algorithms|Metagenomics|Microbiota|Proteomics|Search Engine|Software', 'Journal Article', '10.1093/bioinformatics/btx601', '29028897', 'Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics.'),
(81120, 2018, 'bio', 'diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data', 'Lareau, Caleb A|Aryee, Martin J', 'Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.', 'United States|United States', 8, 'Chromatin|Chromatin Immunoprecipitation|Epigenomics|Humans|K562 Cells|MCF-7 Cells|Molecular Sequence Annotation|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx623', '29028898', 'The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state.'),
(81121, 2017, 'bio', 'Phandango: an interactive viewer for bacterial population genomics', 'Hadfield, James|Croucher, Nicholas J|Goater, Richard J|Abudahab, Khalil|Aanensen, David M|Harris, Simon R', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Department of Infectious Disease Epidemiology, Imperial College London, London, UK.|Centre for Genomic Pathogen Surveillance, Wellcome Trust Genome Campus, Cambridge, UK.|Centre for Genomic Pathogen Surveillance, Wellcome Trust Genome Campus, Cambridge, UK.|Department of Infectious Disease Epidemiology, Imperial College London, London, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 91, '-', 'Journal Article', '10.1093/bioinformatics/btx610', '29028899', 'Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesising and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner.'),
(81122, 2018, 'bio', 'A collaborative filtering-based approach to biomedical knowledge discovery', 'Lever, Jake|Gakkhar, Sitanshu|Gottlieb, Michael|Rashnavadi, Tahereh|Lin, Santina|Siu, Celia|Smith, Maia|Jones, Martin R|Krzywinski, Martin|Jones, Steven J M|Wren, Jonathan', 'Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 7, 'Data Mining|Publications|Software', 'Journal Article', '10.1093/bioinformatics/btx613', '29028901', 'The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers.'),
(81123, 2018, 'bio', 'graphkernels: R and Python packages for graph comparison', 'Sugiyama, Mahito|Ghisu, M Elisabetta|Llinares-Lxc3xb3pez, Felipe|Borgwardt, Karsten', 'National Institute of Informatics, Chiyoda-ku, Tokyo 101-8430, Japan.|D-BSSE, ETH Zxc3xbcrich.|D-BSSE, ETH Zxc3xbcrich.|D-BSSE, ETH Zxc3xbcrich.', 'Japan|-|-|-', 13, 'Computational Biology|Software', 'Journal Article', '10.1093/bioinformatics/btx602', '29028902', 'Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in Cxe2x80x89++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples.'),
(81124, 2018, 'bio', 'Quality of biological images, reconstructed using localization microscopy data', 'Ruszczycki, Blazej|Bernas, Tytus', 'D-BSSE, ETH Zxc3xbcrich.|D-BSSE, ETH Zxc3xbcrich.', '-|-', 2, 'Animals|Dentate Gyrus|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Neurons|Rats|Signal-To-Noise Ratio|Software', 'Journal Article', '10.1093/bioinformatics/btx597', '29028905', 'Fluorescence localization microscopy is extensively used to study the details of spatial architecture of subcellular compartments. This modality relies on determination of spatial positions of fluorophores, labeling an extended biological structure, with precision exceeding the diffraction limit. Several established models describe influence of pixel size, signal-to-noise ratio and optical resolution on the localization precision. The labeling density has been also recognized as important factor affecting reconstruction fidelity of the imaged biological structure. However, quantitative data on combined influence of sampling and localization errors on the fidelity of reconstruction are scarce. It should be noted that processing localization microscopy data is similar to reconstruction of a continuous (extended) non-periodic signal from a non-uniform, noisy point samples. In two dimensions the problem may be formulated within the framework of matrix completion. However, no systematic approach has been adopted in microscopy, where images are typically rendered by representing localized molecules with Gaussian distributions (widths determined by localization precision).'),
(81125, 2018, 'bio', 'OncomiR: an online resource for exploring pan-cancer microRNA dysregulation', 'Wong, Nathan W|Chen, Yuhao|Chen, Shuai|Wang, Xiaowei', 'Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.|Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.', 'United States|United States|United States|United States', 49, 'Computational Biology|Disease Progression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Internet|MicroRNAs|Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx627', '29028907', 'Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer. Using combined miRNA-seq, RNA-seq and clinical data from The Cancer Genome Atlas, we systematically performed statistical analyses to identify dysregulated miRNAs that are associated with tumor development and progression in most major cancer types. Additional analyses further identified potential miRNA-gene target interactions in tumors. These results are stored in a backend database and presented through a web server interface. Moreover, through a backend bioinformatics pipeline, OncomiR can also perform dynamic analysis with custom miRNA selections for in-depth characterization of miRNAs in cancer.'),
(81126, 2018, 'bio', 'The systematic analysis of ultraconserved genomic regions in the budding yeast', 'Yang, Zhi-Kai|Gao, Feng', 'Department of Physics, Tianjin University, Tianjin 300072, China.|Department of Physics, Tianjin University, Tianjin 300072, China.', 'China|China', 1, 'Base Sequence|Conserved Sequence|Genes, Fungal|Genome, Fungal|Genomics|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx619', '29028909', 'In the evolution of species, a kind of special sequences, termed ultraconserved sequences (UCSs), have been inherited without any change, which strongly suggests those sequences should be crucial for the species to survive or adapt to the environment. However, the UCSs are still regarded as mysterious genetic sequences so far. Here, we present a systematic study of ultraconserved genomic regions in the budding yeast based on the publicly available genome sequences, in order to reveal their relationship with the adaptability or fitness advantages of the budding yeast.'),
(81127, 2018, 'bio', 'Genome-wide pre-miRNA discovery from few labeled examples', 'Yones, C|Stegmayer, G|Milone, D H|Sahinalp, Cenk', 'Research Institute for Signals, Systems and Computational Intelligence, sinc(i), UNL-CONICET. Ciudad Universitaria, 4to piso FICH, Santa Fe 3000, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), UNL-CONICET. Ciudad Universitaria, 4to piso FICH, Santa Fe 3000, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), UNL-CONICET. Ciudad Universitaria, 4to piso FICH, Santa Fe 3000, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), UNL-CONICET. Ciudad Universitaria, 4to piso FICH, Santa Fe 3000, Argentina.', 'Argentina|Argentina|Argentina|Argentina', 5, 'Animals|Anopheles|Arabidopsis|Caenorhabditis elegans|Computational Biology|Eukaryota|Genome|Genomics|MicroRNAs|Nucleic Acid Conformation|Sequence Analysis, DNA|Sequence Analysis, RNA|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx612', '29028911', 'Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples. Secondly, it is very difficult to build a good set of negative examples for representing the full spectrum of non-miRNA sequences. Thirdly, in any genome, there is a huge class imbalance (1: 10xe2x80x89000) that is well-known for particularly affecting supervised classifiers.'),
(81128, 2017, 'bio', 'Detecting presence of mutational signatures in cancer with confidence', 'Huang, Xiaoqing|Wojtowicz, Damian|Przytycka, Teresa M', 'National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA.|National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA.|National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA.', 'United States|United States|United States', 17, '-', 'Journal Article', '10.1093/bioinformatics/btx604', '29028923', 'Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genomes mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patients mutational profile into signatures but also establish the accuracy of such decomposition.'),
(81129, 2018, 'bio', 'Structure-based prediction of protein- peptide binding regions using Random Forest', 'Taherzadeh, Ghazaleh|Zhou, Yaoqi|Liew, Alan Wee-Chung|Yang, Yuedong', 'School of Information and Communication Technology.|School of Information and Communication Technology.|School of Information and Communication Technology.|School of Information and Communication Technology.', '-|-|-|-', 0, 'Computational Biology|Humans|Machine Learning|Peptides|Protein Binding|Protein Domains|Protein Tyrosine Phosphatase, Non-Receptor Type 4|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btx614', '29028926', 'Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processes, functional mechanisms, and drug discovery. Protein-peptide interactions can be analyzed by studying the structures of protein-peptide complexes. However, only a small portion has known complex structures and experimental determination of protein-peptide interaction is costly and inefficient. Thus, predicting peptide-binding sites computationally will be useful to improve efficiency and cost effectiveness of experimental studies. Here, we established a machine learning method called SPRINT-Str (Structure-based prediction of protein-Peptide Residue-level Interaction) to use structural information for predicting protein-peptide binding residues. These predicted binding residues are then employed to infer the peptide-binding site by a clustering algorithm.'),
(81130, 2018, 'bio', 'Tumor origin detection with tissue-specific miRNA and DNA methylation markers', 'Tang, Wei|Wan, Shixiang|Yang, Zhen|Teschendorff, Andrew E|Zou, Quan', 'School of Computer Science and Technology.|School of Computer Science and Technology.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China.|School of Computer Science and Technology.', '-|-|China|China|-', 97, 'Computational Biology|CpG Islands|DNA Methylation|DNA, Neoplasm|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Male|MicroRNAs|Neoplasms|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx622', '29028927', 'A clear identification of the primary site of tumor is of great importance to the next targeted site-specific treatments and could efficiently improve patients overall survival. Even though many classifiers based on gene expression had been proposed to predict the tumor primary, only a few studies focus on using DNA methylation (DNAm) profiles to develop classifiers, and none of them compares the performance of classifiers based on different profiles.'),
(81131, 2018, 'bio', 'DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier', 'Kulmanov, Maxat|Khan, Mohammed Asif|Hoehndorf, Robert|Wren, Jonathan', 'Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 64, 'Animals|Bacteria|Computational Biology|Eukaryota|Gene Ontology|Humans|Protein Interaction Maps|Proteins|Sequence Analysis, Protein|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx624', '29028931', 'A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40xe2x80x89000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem.'),
(81132, 2017, 'bio', 'DeepLoc: prediction of protein subcellular localization using deep learning', 'Almagro Armenteros, Jose Juan|Sønderby, Casper Kaae|Sønderby, Søren Kaae|Nielsen, Henrik|Winther, Ole', 'Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx548', '29028934', '-'),
(81133, 2018, 'bio', 'Towards unified quality verification of synthetic count data with countsimQC', 'Soneson, Charlotte|Robinson, Mark D', 'Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.|Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.', 'Switzerland|Switzerland', 0, 'Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx631', '29028961', 'Statistical tools for biological data analysis are often evaluated using synthetic data, designed to mimic the features of a specific type of experimental data. The generalizability of such evaluations depends on how well the synthetic data reproduce the main characteristics of the experimental data, and we argue that an assessment of this similarity should accompany any synthetic dataset used for method evaluation. We describe countsimQC, which provides a straightforward way to generate a stand-alone report that shows the main characteristics of (e.g. RNA-seq) count data and can be provided alongside a publication as verification of the appropriateness of any utilized synthetic data.'),
(81134, 2018, 'bio', 'ZCCHC17 is a master regulator of synaptic gene expression in Alzheimers disease', 'Tomljanovic, Zeljko|Patel, Mitesh|Shin, William|Califano, Andrea|Teich, Andrew F', 'Department of Pathology and Cell Biology.|Department of Pathology and Cell Biology.|Department of Systems Biology, Columbia University, New York, NY 10032, USA.|Department of Systems Biology, Columbia University, New York, NY 10032, USA.|Department of Pathology and Cell Biology.', 'United States|United States|United States|United States|United States', 0, 'Alzheimer Disease|Animals|Brain|Gene Expression Profiling|Gene Expression Regulation|Male|Neurons|Nuclear Proteins|Rats|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx608', '29028963', 'In an effort to better understand the molecular drivers of synaptic and neurophysiologic dysfunction in Alzheimers disease (AD), we analyzed neuronal gene expression data from human AD brain tissue to identify master regulators of synaptic gene expression.'),
(81135, 2017, 'bio', 'TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks', 'Chua, Huey Eng|Bhowmick, Sourav S|Zheng, Jie', 'School of Computer Science and Engineering, Nanyang Technological University, Singapore.|School of Computer Science and Engineering, Nanyang Technological University, Singapore.|School of Computer Science and Engineering, Nanyang Technological University, Singapore.', 'Singapore|Singapore|Singapore', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx600', '29028982', 'Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, a user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types.'),
(81136, 2018, 'bio', 'A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data', 'Sanchez-Castillo, M|Blanco, D|Tienda-Luna, I M|Carrion, M C|Huang, Yufei', 'Department of Applied Physics.|Department of Applied Physics.|Department of Electrical Engineering, University of Granada, Granada, Spain.|Department of Applied Physics.|Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX, USA.', 'United States|United States|Spain|United States|United States', 22, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Mice|Models, Biological|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btx605', '29028984', 'Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81137, 2018, 'bio', 'A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs', 'Kim, Sun Ah|Cho, Chang-Sung|Kim, Suh-Ryung|Bull, Shelley B|Yoo, Yun Joo', 'Department of Statistics.|Department of Mathematics Education, Seoul National University, Seoul 08826, South Korea.|Department of Mathematics Education, Seoul National University, Seoul 08826, South Korea.|Prosserman Centre for Health Research, The Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto M5T 3L9, Canada.|Department of Mathematics Education, Seoul National University, Seoul 08826, South Korea.', '-|-|-|Canada|-', 5, 'Algorithms|Asian Continental Ancestry Group|Genetics, Population|Genome, Human|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx609', '29028986', 'Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation.'),
(81138, 2018, 'bio', 'In silico structural modeling of multiple epigenetic marks on DNA', 'Krawczyk, Konrad|Demharter, Samuel|Knapp, Bernhard|Deane, Charlotte M|Minary, Peter', 'Department of Computer Science, Oxford University, OX1xe2x80x893QD Oxford, UK.|Department of Computer Science, Oxford University, OX1?3QD Oxford, UK.|Department of Statistics, Oxford University, OX1xe2x80x893LB Oxford, UK.|Department of Statistics, Oxford University, OX1?3LB Oxford, UK.|Department of Computer Science, Oxford University, OX1?3QD Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Computational Biology|Computer Simulation|DNA|DNA Methylation|Epigenomics|Models, Molecular|Nucleic Acid Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx516', '29028987', 'The code together with examples and tutorials are available from http://www.cs.ox.ac.uk/mosaics.'),
(81139, 2018, 'bio', 'QuASAR-MPRA: accurate allele-specific analysis for massively parallel reporter assays', 'Kalita, Cynthia A|Moyerbrailean, Gregory A|Brown, Christopher|Wen, Xiaoquan|Luca, Francesca|Pique-Regi, Roger', 'Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA.|Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA.|Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.|Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA.|Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Alleles|Allelic Imbalance|Computational Biology|Gene Expression Regulation|Genome, Human|Humans|Polymorphism, Single Nucleotide|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btx598', '29028988', 'The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets.'),
(81140, 2018, 'bio', 'diffuStats: an R package to compute diffusion-based scores on biological networks', 'Picart-Armada, Sergio|Thompson, Wesley K|Buil, Alfonso|Perera-Lluna, Alexandre', 'B2SLab, Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona 08028, Spain.|Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Roskilde 4000, Denmark.|Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Roskilde 4000, Denmark.|B2SLab, Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona 08028, Spain.', 'Spain|Denmark|Denmark|Spain', 0, 'Computational Biology|Metabolic Networks and Pathways|Protein Interaction Maps|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx632', '29029016', 'Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritizing novel candidates in the area of study. There are several choices in conceiving the diffusion process-involving the graph kernel, the score definitions and the presence of a posterior statistical normalization-which have an impact on the results. This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalized scores, that eases the computation of the scores and their benchmarking for an optimal choice.'),
(81141, 2018, 'bio', 'DiversePathsJ: diverse shortest paths for bioimage analysis', 'Uhlmann, Virginie|Haubold, Carsten|Hamprecht, Fred A|Unser, Michael', 'École polytechnique fédérale de Lausanne (EPFL), Biomedical Imaging Group, 1015 Lausanne, Switzerland.|Heidelberg University, Interdisziplinären Zentrums für Wissenschaftliches Rechnen/Heidelberg Collaboratory for Image Processing (IWR/HCI), 69120 Heidelberg, Germany.|Heidelberg University, Interdisziplinären Zentrums für Wissenschaftliches Rechnen/Heidelberg Collaboratory for Image Processing (IWR/HCI), 69120 Heidelberg, Germany.|École polytechnique fédérale de Lausanne (EPFL), Biomedical Imaging Group, 1015 Lausanne, Switzerland.', 'Switzerland|Germany|Germany|Switzerland', 0, 'Algorithms|Animals|Bacteria|Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btx621', '29029024', 'We introduce a formulation for the general task of finding diverse shortest paths between two end-points. Our approach is not linked to a specific biological problem and can be applied to a large variety of images thanks to its generic implementation as a user-friendly ImageJ/Fiji plugin. It relies on the introduction of additional layers in a Viterbi path graph, which requires slight modifications to the standard Viterbi algorithm rules. This layered graph construction allows for the specification of various constraints imposing diversity between solutions.'),
(81142, 2017, 'bio', 'On expert curation and scalability: UniProtKB/Swiss-Prot as a case study', 'Poux, Sylvain|Arighi, Cecilia N|Magrane, Michele|Bateman, Alex|Wei, Chih-Hsuan|Lu, Zhiyong|Boutet, Emmanuel|Bye-A-Jee, Hema|Famiglietti, Maria Livia|Roechert, Bernd|UniProt Consortium, The', 'Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Protein Information Resource, University of Delaware, Newark, DE 19711, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10xe2x80x891SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10xe2x80x891SD, UK.|National Center for Biotechnology Information (NCBI), US National Library of Medicine, Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), US National Library of Medicine, Bethesda, MD 20894, USA.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10xe2x80x891SD, UK.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.', 'Switzerland|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Switzerland|United Kingdom|Switzerland|Switzerland|Switzerland', 0, 'Data Curation|Data Mining|Databases, Protein|Humans|Knowledge Bases|PubMed|Review Literature as Topic|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btx439', '29036270', 'Biological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, their ability to keep up with the growth of biomedical literature is under scrutiny. Using UniProtKB/Swiss-Prot as a case study, we address this concern via multiple literature triage approaches.'),
(81143, 2017, 'bio', 'Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer', 'Baker, Simon|Ali, Imran|Silins, Ilona|Pyysalo, Sampo|Guo, Yufan|Hxc3xb6gberg, Johan|Stenius, Ulla|Korhonen, Anna', 'Computer Laboratory.|Institute of Environmental Medicine, Karolinska Institutet, SE 171xe2x80x8977 Stockholm, Sweden.|Institute of Environmental Medicine, Karolinska Institutet, SE 171xe2x80x8977 Stockholm, Sweden.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3xe2x80x899DA, UK.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3xe2x80x899DA, UK.|Institute of Environmental Medicine, Karolinska Institutet, SE 171xe2x80x8977 Stockholm, Sweden.|Institute of Environmental Medicine, Karolinska Institutet, SE 171xe2x80x8977 Stockholm, Sweden.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3xe2x80x899DA, UK.', 'United Kingdom|Sweden|Sweden|United Kingdom|United Kingdom|Sweden|Sweden|United Kingdom', 0, 'Biomarkers|Computational Biology|Data Mining|Databases, Factual|Humans|Neoplasms|Publications|Reproducibility of Results|Review Literature as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btx454', '29036271', 'To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research. This time-demanding task can be supported by emerging computational approaches based on text mining which offer a great opportunity to organize and retrieve the desired information efficiently from sizable databases. One way to organize existing knowledge on cancer is to utilize the widely accepted framework of the Hallmarks of Cancer. These hallmarks refer to the alterations in cell behaviour that characterize the cancer cell.'),
(81144, 2017, 'bio', 'Lep-MAP3: robust linkage mapping even for low-coverage whole genome sequencing data', 'Rastas, Pasi', 'Department of Zoology, Butterfly Genetics Group, University of Cambridge, Cambridge, UK.', 'United Kingdom', 0, 'Algorithms|Chromosome Mapping|Genetic Linkage|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx494', '29036272', 'Accurate and dense linkage maps are useful in family-based linkage and association studies, quantitative trait locus mapping, analysis of genome synteny and other genomic data analyses. Moreover, linkage mapping is one of the best ways to detect errors in de novo genome assemblies, as well as to orient and place assembly contigs within chromosomes. A small mapping cross of tens of individuals will detect many errors where distant parts of the genome are erroneously joined together. With more individuals and markers, even more local errors can be detected and more contigs can be oriented. However, the tools that are currently available for constructing linkage maps are not well suited for large, possible low-coverage, whole genome sequencing datasets.'),
(81145, 2017, 'bio', 'SCooP: an accurate and fast predictor of protein stability curves as a function of temperature', 'Pucci, Fabrizio|Kwasigroch, Jean Marc|Rooman, Marianne', 'Department of BioModeling BioInformatics and BioProcesses, Universitxc3xa9 Libre de Bruxelles and Interuniversity Institute of Bioinformatics in Brussels, Triumph Bld, 1050 Brussels, Belgium.|Department of BioModeling BioInformatics and BioProcesses, Universitxc3xa9 Libre de Bruxelles and Interuniversity Institute of Bioinformatics in Brussels, Triumph Bld, 1050 Brussels, Belgium.|Department of BioModeling BioInformatics and BioProcesses, Universitxc3xa9 Libre de Bruxelles and Interuniversity Institute of Bioinformatics in Brussels, Triumph Bld, 1050 Brussels, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Protein Engineering|Protein Folding|Protein Stability|Proteins|Reproducibility of Results|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx417', '29036273', 'The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy xcex94G(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors.'),
(81146, 2017, 'bio', 'A robust DF-REML framework for variance components estimation in genetic studies', 'Lourenço, V M|Rodrigues, P C|Pires, A M|Piepho, H-P', 'Centre for Mathematics and Applications (CMA) and Department of Mathematics, FCT - NOVA University of Lisbon, Lisbon, Portugal.|Department of Statistics, Federal University of Bahia, Bahia, Brazil.|Center for Computational and Stochastic Mathematics (CEMAT) and Department of Mathematics, IST - University of Lisbon, Lisbon, Portugal.|Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany.', 'Portugal|Brazil|Portugal|Germany', 4, 'Analysis of Variance|Animals|Breeding|Computer Simulation|Genetic Association Studies|Humans|Likelihood Functions|Linear Models|Models, Genetic|Monte Carlo Method|Phenotype|Polymorphism, Single Nucleotide', 'Comparative Study', '10.1093/bioinformatics/btx457', '29036274', 'In genetic association studies, linear mixed models (LMMs) are used to test for associations between phenotypes and candidate single nucleotide polymorphisms (SNPs). These same models are also used to estimate heritability, which is central not only to evolutionary biology but also to the prediction of the response to selection in plant and animal breeding, as well as the prediction of disease risk in humans. However, when one or more of the underlying assumptions are violated, the estimation of variance components may be compromised and therefore so may the estimates of heritability and any other functions of these. Considering that datasets obtained from real life experiments are prone to several sources of contamination, which usually induce the violation of the assumption of the normality of the errors, a robust derivative-free restricted-maximum likelihood framework (DF-REML) together with a robust coefficient of determination are proposed for the LMM in the context of genetic studies of continuous traits.'),
(81147, 2017, 'bio', 'PySeqLab: an open source Python package for sequence labeling and segmentation', 'Allam, Ahmed|Krauthammer, Michael', 'Department of Pathology, Yale School of Medicine, New Haven, CT 06511, USA.|Department of Pathology, Yale School of Medicine, New Haven, CT 06511, USA.', 'United States|United States', 1, 'Algorithms|Models, Statistical|Natural Language Processing|Neural Networks (Computer)|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx451', '29036277', 'Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data.'),
(81148, 2017, 'bio', 'powsimR: power analysis for bulk and single cell RNA-seq experiments', 'Vieth, Beate|Ziegenhain, Christoph|Parekh, Swati|Enard, Wolfgang|Hellmann, Ines', 'Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, 82152 Munich, Germany.|Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, 82152 Munich, Germany.|Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, 82152 Munich, Germany.|Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, 82152 Munich, Germany.|Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, 82152 Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Gene Expression Profiling|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx435', '29036287', 'Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.'),
(81149, 2017, 'bio', 'VICTOR: genome-based phylogeny and classification of prokaryotic viruses', 'Meier-Kolthoff, Jan P|Göker, Markus', 'Department of Bioinformatics, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, 38124 Braunschweig, Germany.|Department of Bioinformatics, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, 38124 Braunschweig, Germany.', 'Germany|Germany', 88, 'Archaea|Bacteria|Computer Simulation|Genomics|Phylogeny|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx440', '29036289', 'Bacterial and archaeal viruses are crucial for global biogeochemical cycles and might well be game-changing therapeutic agents in the fight against multi-resistant pathogens. Nevertheless, it is still unclear how to best use genome sequence data for a fast, universal and accurate taxonomic classification of such viruses.'),
(81150, 2017, 'bio', 'Phylotyper: in silico predictor of gene subtypes', 'Whiteside, Matthew D|Gannon, Victor P J|Laing, Chad R', 'National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, AB T1J 3Z4, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, AB T1J 3Z4, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, AB T1J 3Z4, Canada.', 'Canada|Canada|Canada', 2, 'Bacteria|Bacterial Infections|Biological Evolution|Computer Simulation|Disease Outbreaks|Genomics|Humans|Models, Genetic|Phenotype|Phylogeny|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx459', '29036291', 'Whole genome sequencing (WGS) is being adopted in public health for improved surveillance and outbreak analysis. In public health, subtyping has been used to infer phenotypes and distinguish bacterial strain groups. In silico tools that predict subtypes from sequences data are needed to transition historical data to WGS-based protocols. Phylotyper is a novel solution for in silico subtype prediction from gene sequences. Designed for incorporation into WGS pipelines, it is a general prediction tool that can be applied to different subtype schemes. Phylotyper uses phylogeny to model the evolution of the subtype and infer subtypes for unannotated sequences. The phylogenic framework in Phylotyper improves accuracy over approaches based solely on sequence similarity and provides useful contextual feedback.'),
(81151, 2017, 'bio', 'RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality', 'Borrel, Alexandre|Fourches, Denis', 'Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA.|Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA.', 'United States|United States', 0, 'Computational Biology|Models, Molecular|Molecular Conformation|Software|Virtual Reality', 'Journal Article', '10.1093/bioinformatics/btx485', '29036294', 'There is a growing interest for the broad use of Augmented Reality (AR) and Virtual Reality (VR) in the fields of bioinformatics and cheminformatics to visualize complex biological and chemical structures. AR and VR technologies allow for stunning and immersive experiences, offering untapped opportunities for both research and education purposes. However, preparing 3D models ready to use for AR and VR is time-consuming and requires a technical expertise that severely limits the development of new contents of potential interest for structural biologists, medicinal chemists, molecular modellers and teachers.'),
(81152, 2017, 'bio', 'myVCF: a desktop application for high-throughput mutations data management', 'Pietrelli, Alessandro|Valenti, Luca', 'Internal Medicine and Metabolic Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico, Milano, Italy.|Internal Medicine and Metabolic Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico, Milano, Italy.', 'Italy|Italy', 3, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Knowledge Management|Mutation|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx475', '29036298', 'Next-generation sequencing technologies have become the most powerful tool to discover genetic variants associated with human diseases. Although the dramatic reductions in the costs facilitate the use in the wet-lab and clinics, the huge amount of data generated renders their management by non-expert researchers and physicians extremely difficult. Therefore, there is an urgent need of novel approaches and tools aimed at getting the end-users closer to the sequencing data, facilitating the access by non-bioinformaticians, and to speed-up the functional interpretation of genetic variants. We developed myVCF, a standalone, easy-to-use desktop application, which is based on a browser interface and is suitable for Windows, Mac and UNIX systems. myVCF is an efficient platform that is able to manage multiple sequencing projects created from VCF files within the system, stores genetic variants and samples genotypes from an annotated VCF files into a SQLite database, implements a flexible search engine for data exploration, allowing to query for chromosomal region, gene, single variant or dbSNP ID. Besides, myVCF generates a summary statistics report about mutations distribution across samples and across the genome/exome by aggregating the information within the VCF file. In summary, the myVCF platform allows end-users without strong programming and bioinformatics skills to explore, query, visualize and export mutations data in a simple and straightforward way.'),
(81153, 2018, 'bio', 'Irys Extract', 'Arielly, Rani|Ebenstein, Yuval', 'School of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 69978, Israel.|School of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel', 3, 'Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btx437', '29036307', 'Irys Extract is a software tool for generating genomic information from data collected by the BioNano Genomics Irys platform. The tool allows the user easy access to the raw data in the form of cropped images and genetically aligned intensity profiles. The latter are also made compatible with the BED format for using with popular genomic browsers such as the UCSC Genome Browser.'),
(81154, 2018, 'bio', 'DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data', 'Sun, Zhe|Wang, Ting|Deng, Ke|Wang, Xiao-Feng|Lafyatis, Robert|Ding, Ying|Hu, Ming|Chen, Wei', 'Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.|Division of Pulmonary Medicine, Allergy and Immunology and Department of Pediatrics, Childrens Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA.|Center for Statistical Science, Tsinghua University, Beijing, China.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.|Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.|Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.', 'United States|United States|China|United States|United States|United States|United States|United States', 10, 'Cluster Analysis|Gene Expression Profiling|Humans|Scleroderma, Systemic|Sequence Analysis, RNA|Single-Cell Analysis|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx490', '29036318', 'Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored.'),
(81155, 2017, 'bio', 'Detect differentially methylated regions using non-homogeneous hidden Markov model for methylation array data', 'Shen, Linghao|Zhu, Jun|Robert Li, Shuo-Yen|Fan, Xiaodan', 'Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York University, New York, NY, USA.|University of Electronic Science and Technology of China, Sichuan, China.|Department of Statistics, The Chinese University of Hong Kong, Hong Kong.', 'Hong Kong|United States|China|Hong Kong', 0, 'Bayes Theorem|DNA Methylation|Markov Chains|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btx467', '29036320', 'DNA methylation is an important epigenetic mechanism in gene regulation and the detection of differentially methylated regions (DMRs) is enthralling for many disease studies. There are several aspects that we can improve over existing DMR detection methods: (i) methylation statuses of nearby CpG sites are highly correlated, but this fact has seldom been modelled rigorously due to the uneven spacing, (ii) it is practically important to be able to handle both paired and unpaired samples, and (iii) the capability to detect DMRs from a single pair of samples is demanded.'),
(81156, 2017, 'bio', 'proFIA: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry', 'Delabrixc3xa8re, Alexis|Hohenester, Ulli M|Colsch, Benoit|Junot, Christophe|Fenaille, Franxc3xa7ois|Thxc3xa9venot, Etienne A', 'CEA, LIST, Laboratory for Data Analysis and Systems Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France.|CEA, DRF/JOLIOT/SPI, Laboratoire dEtude du Mxc3xa9tabolisme des Mxc3xa9dicaments, MetaboHUB, Gif-Sur-Yvette F-91191, France.|CEA, DRF/JOLIOT/SPI, Laboratoire dEtude du Mxc3xa9tabolisme des Mxc3xa9dicaments, MetaboHUB, Gif-Sur-Yvette F-91191, France.|CEA, DRF/JOLIOT/SPI, Laboratoire dEtude du Mxc3xa9tabolisme des Mxc3xa9dicaments, MetaboHUB, Gif-Sur-Yvette F-91191, France.|CEA, DRF/JOLIOT/SPI, Laboratoire dEtude du Mxc3xa9tabolisme des Mxc3xa9dicaments, MetaboHUB, Gif-Sur-Yvette F-91191, France.|CEA, LIST, Laboratory for Data Analysis and Systems Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France.', 'France|France|France|France|France|France', 7, 'Flow Injection Analysis|Mass Spectrometry|Metabolomics|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx458', '29036359', 'Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS) is a promising approach for high-throughput metabolomics. FIA-HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only.'),
(81157, 2017, 'bio', 'Gating mass cytometry data by deep learning', 'Li, Huamin|Shaham, Uri|Stanton, Kelly P|Yao, Yi|Montgomery, Ruth R|Kluger, Yuval', 'Applied Mathematics Program.|Department of Statistics, Yale University, 51 Prospect Street, New Haven, CT 06511, USA.|Department of Pathology and Yale Cancer Center.|Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA.|Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA.|Applied Mathematics Program.', 'United States|United States|United States|United States|United States|United States', 0, 'Blood Cells|Calibration|Cell Separation|Computational Biology|Flow Cytometry|Humans|Machine Learning|Reference Standards|Reproducibility of Results|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btx448', '29036374', 'Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies.'),
(81158, 2017, 'bio', 'MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction', 'Wang, Duolin|Zeng, Shuai|Xu, Chunhui|Qiu, Wangren|Liang, Yanchun|Joshi, Trupti|Xu, Dong', 'Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.', 'China|United States|United States|United States|China|United States|China', 62, 'Machine Learning|Neural Networks (Computer)|Phosphoproteins|Phosphorylation|Protein Kinases|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx496', '29036382', 'Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction.'),
(81159, 2017, 'bio', 'Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data', 'Brunius, Carl|Pedersen, Anders|Malmodin, Daniel|Karlsson, B Gxc3xb6ran|Andersson, Lars I|Tybring, Gunnel|Landberg, Rikard', 'Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412xe2x80x8996 Gothenburg, Sweden.|Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden.|Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden.|Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden.|Department of Laboratory Medicine.|Department of Medical Epidemiology and Biostatistics.|Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412xe2x80x8996 Gothenburg, Sweden.', 'Sweden|Sweden|Sweden|Sweden|-|-|Sweden', 7, 'Adult|Blood Specimen Collection|Data Accuracy|Female|Humans|Magnetic Resonance Spectroscopy|Male|Metabolomics|Middle Aged|Models, Statistical|Plasma|Selection Bias|Temperature|Time Factors', 'Journal Article', '10.1093/bioinformatics/btx442', '29036400', 'Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.'),
(81160, 2017, 'bio', 'The value of prior knowledge in machine learning of complex network systems', 'Ferranti, Dana|Krane, David|Craft, David', 'Radiation Oncology, Massachusetts General Hospital, Boston 02114, MA, USA.|Radiation Oncology, Massachusetts General Hospital, Boston 02114, MA, USA.|Radiation Oncology, Massachusetts General Hospital, Boston 02114, MA, USA.', 'United States|United States|United States', 0, 'Computational Biology|Drug Discovery|Humans|Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx438', '29036404', 'Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available.'),
(81161, 2017, 'bio', 'FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects', 'Kim, Emma E|Lee, Seunghoon|Lee, Cue Hyunkyu|Oh, Hyunjung|Song, Kyuyoung|Han, Buhm', 'Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Korea.|Department of Chemistry, Seoul National University, Seoul 151-747, Korea.|Department of Convergence Medicine.|Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul 138-736, Korea.|Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul 138-736, Korea.|Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Korea.', '-|-|-|-|-|-', 0, 'Computational Biology|Genome-Wide Association Study|Genotype|Humans|Meta-Analysis as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btx463', '29036405', 'In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level.'),
(81162, 2017, 'bio', 'SPRINT: an SNP-free toolkit for identifying RNA editing sites', 'Zhang, Feng|Lu, Yulan|Yan, Sijia|Xing, Qinghe|Tian, Weidong', 'State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development.|The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute.|Childrens Hospital of Fudan University, Shanghai 201102, China.|Childrens Hospital of Fudan University, Shanghai 201102, China.|State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development.', '-|-|China|China|-', 20, 'Animals|Databases, Genetic|Genome|Mice|RNA|RNA Editing|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx473', '29036410', 'RNA editing generates post-transcriptional sequence alterations. Detection of RNA editing sites (RESs) typically requires the filtering of SNVs called from RNA-seq data using an SNP database, an obstacle that is difficult to overcome for most organisms.'),
(81163, 2018, 'bio', 'An accurate algorithm for the detection of DNA fragments from dilution pool sequencing experiments', 'Bansal, Vikas', 'Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.', 'United States', 0, 'Algorithms|Data Accuracy|Genome, Human|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx436', '29036419', 'The short read lengths of current high-throughput sequencing technologies limit the ability to recover long-range haplotype information. Dilution pool methods for preparing DNA sequencing libraries from high molecular weight DNA fragments enable the recovery of long DNA fragments from short sequence reads. These approaches require computational methods for identifying the DNA fragments using aligned sequence reads and assembling the fragments into long haplotypes. Although a number of computational methods have been developed for haplotype assembly, the problem of identifying DNA fragments from dilution pool sequence data has not received much attention.'),
(81164, 2017, 'bio', 'LRCstats, a tool for evaluating long reads correction methods', 'La, Sean|Haghshenas, Ehsan|Chauve, Cedric', 'Department of Mathematics.|School of Computing Science, MADD-Gen program, Simon Fraser University, Burnaby, BC V5A1S6, Canada.|Department of Mathematics.', '-|Canada|-', 2, 'Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx489', '29036421', 'Third-generation sequencing (TGS) platforms that generate long reads, such as PacBio and Oxford Nanopore technologies, have had a dramatic impact on genomics research. However, despite recent improvements, TGS reads suffer from high-error rates and the development of read correction methods is an active field of research. This motivates the need to develop tools that can evaluate the accuracy of noisy long reads correction tools.'),
(81165, 2017, 'bio', 'Motif independent identification of potential RNA G-quadruplexes by G4RNA screener', 'Garant, Jean-Michel|Perreault, Jean-Pierre|Scott, Michelle S', 'RNA Group/Groupe ARN, Dxc3xa9partement de Biochimie, Facultxc3xa9 de Mxc3xa9decine des Sciences de la Santxc3xa9, Pavillon de Recherche Appliquxc3xa9e au Cancer, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.|RNA Group/Groupe ARN, Dxc3xa9partement de Biochimie, Facultxc3xa9 de Mxc3xa9decine des Sciences de la Santxc3xa9, Pavillon de Recherche Appliquxc3xa9e au Cancer, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.|RNA Group/Groupe ARN, Dxc3xa9partement de Biochimie, Facultxc3xa9 de Mxc3xa9decine des Sciences de la Santxc3xa9, Pavillon de Recherche Appliquxc3xa9e au Cancer, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.', 'Canada|Canada|Canada', 23, 'Databases, Nucleic Acid|G-Quadruplexes|Neural Networks (Computer)|Nucleotide Motifs|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx498', '29036425', 'G-quadruplex structures in RNA molecules are known to have regulatory impacts in cells but are difficult to locate in the genome. The minimal requirements for G-quadruplex folding in RNA (Gxe2x89xa53N1-7xe2x80x89Gxe2x89xa53N1-7xe2x80x89Gxe2x89xa53N1-7xe2x80x89Gxe2x89xa53) is being challenged by observations made on specific examples in recent years. The definition of potential G-quadruplex sequences has major repercussions on the observation of the structure since it introduces a bias. The canonical motif only describes a sub-population of the reported G-quadruplexes. To address these issues, we propose an RNA G-quadruplex prediction strategy that does not rely on a motif definition.'),
(81166, 2017, 'bio', 'Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology', 'Cang, Zixuan|Wei, Guo-Wei', 'Department of Mathematics.|Department of Mathematics.', '-|-', 0, 'Algorithms|Computational Biology|Mutagenesis|Mutation|Protein Folding|Protein Stability|Proteins|Software|Structural Homology, Protein|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx460', '29036440', 'Site directed mutagenesis is widely used to understand the structure and function of biomolecules. Computational prediction of mutation impacts on protein stability offers a fast, economical and potentially accurate alternative to laboratory mutagenesis. Most existing methods rely on geometric descriptions, this work introduces a topology based approach to provide an entirely new representation of mutation induced protein stability changes that could not be obtained from conventional techniques.'),
(81167, 2018, 'bio', 'A nonparametric significance test for sampled networks', 'Elliott, Andrew|Leicht, Elizabeth|Whitmore, Alan|Reinert, Gesine|Reed-Tsochas, Felix', 'CABDyN Complexity Centre, Saxc3xafd Business School, University of Oxford, Oxford OX1 1HP, UK.|CABDyN Complexity Centre, Saxc3xafd Business School, University of Oxford, Oxford OX1 1HP, UK.|e-Therapeutics plc, Long Hanborough, OX29 8LN, UK.|Department of Statistics, University of Oxford, Oxford, UK.|CABDyN Complexity Centre, Saxc3xafd Business School, University of Oxford, Oxford OX1xe2x80x891HP, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Computational Biology|Humans|Monte Carlo Method|Parkinson Disease|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx419', '29036452', 'Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinsons disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation.'),
(81168, 2017, 'bio', 'Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies', 'Wang, Meng|Ji, Zhanglong|Wang, Shuang|Kim, Jihoon|Yang, Hai|Jiang, Xiaoqian|Ohno-Machado, Lucila', 'Department of Genetics, Stanford University, Stanford, CA 94305, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.|Department of Biomedical Informatics, UC San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States|United States', 4, 'Child|Family|Genetic Privacy|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Parents|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx470', '29036461', 'Inappropriate disclosure of human genomes may put the privacy of study subjects and of their family members at risk. Existing privacy-preserving mechanisms for Genome-Wide Association Studies (GWAS) mainly focus on protecting individual information in case-control studies. Protecting privacy in family-based studies is more difficult. The transmission disequilibrium test (TDT) is a powerful family-based association test employed in many rare disease studies. It gathers information about families (most frequently involving parents, affected children and their siblings). It is important to develop privacy-preserving approaches to disclose TDT statistics with a guarantee that the risk of family re-identification stays below a pre-specified risk threshold. Re-identification in this context means that an attacker can infer that the presence of a family in a study.'),
(81169, 2017, 'bio', 'SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms', 'Xia, Yuchao|Liu, Yun|Deng, Minghua|Xi, Ruibin', 'School of Mathematical Sciences.|School of Mathematical Sciences.|School of Mathematical Sciences.|School of Mathematical Sciences.', '-|-|-|-', 0, 'Algorithms|Genome, Human|Genomic Structural Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx455', '29036467', 'Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection.'),
(81170, 2017, 'bio', 'Forecasting residue-residue contact prediction accuracy', 'Wozniak, P P|Konopka, B M|Xu, J|Vriend, G|Kotulska, M', 'Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.|Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.|Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, GA 6525, Nijmegen, The Netherlands.|Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.', 'Poland|Poland|United States|Netherlands|Poland', 3, 'Algorithms|Computational Biology|Data Accuracy|Models, Molecular|Mutation|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx416', '29036497', 'Apart from meta-predictors, most of todays methods for residue-residue contact prediction are based entirely on Direct Coupling Analysis (DCA) of correlated mutations in multiple sequence alignments (MSAs). These methods are on average xe2x88xbc40% correct for the 100 strongest predicted contacts in each protein. The end-user who works on a single protein of interest will not know if predictions are either much more or much less correct than 40%, which is especially a problem if contacts are predicted to steer experimental research on that protein.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81171, 2017, 'bio', 'VCF-Explorer: filtering and analysing whole genome VCF files', 'Akgün, Mete|Demirci, Hüseyin', 'The Scientific and Technological Research Council of Turkey (TÜBITAK), Gebze, Kocaeli 41470, Turkey.|The Scientific and Technological Research Council of Turkey (TÜBITAK), Gebze, Kocaeli 41470, Turkey.', 'Turkey|Turkey', 1, 'Computational Biology|Genomics|Genotype|High-Throughput Nucleotide Sequencing|Software|User-Computer Interface|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx422', '29036499', 'The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies. In this work we present VCF-Explorer, a variant analysis software capable of handling large files. Memory efficiency and avoiding computationally costly pre-processing step enable to carry out the analysis to be performed with ordinary computers. VCF-Explorer provides an easy to use environment where users can define various types of queries based on variant and sample genotype level annotations. VCF-Explorer can be run in different environments and computational platforms ranging from a standard laptop to a high performance server.'),
(81172, 2017, 'bio', 'ggseqlogo: a versatile R package for drawing sequence logos', 'Wagih, Omar', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom', 0, 'Genomics|Position-Specific Scoring Matrices|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx469', '29036507', 'Sequence logos have become a crucial visualization method for studying underlying sequence patterns in the genome. Despite this, there remains a scarcity of software packages that provide the versatility often required for such visualizations. ggseqlogo is an R package built on the ggplot2 package that aims to address this issue. ggseqlogo offers native illustration of publication-ready DNA, RNA and protein sequence logos in a highly customizable fashion with features including multi-logo plots, qualitative and quantitative colour schemes, annotation of logos and integration with other plots. The package is intuitive to use and seamlessly integrates into R analysis pipelines.'),
(81173, 2017, 'bio', 'ScanGEO: parallel mining of high-throughput gene expression data', 'Koeppen, Katja|Stanton, Bruce A|Hampton, Thomas H', 'Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.', 'United States|United States|United States', 11, 'Data Mining|Gene Expression Profiling|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx452', '29036513', 'Current options to mine publicly available gene expression data deposited in NCBIs gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command.'),
(81174, 2017, 'bio', 'DynaPho: a web platform for inferring the dynamics of time-series phosphoproteomics', 'Hsu, Chia-Lang|Wang, Jian-Kai|Lu, Pei-Chun|Huang, Hsuan-Cheng|Juan, Hsueh-Fen', 'Department of Life Science.|Genome and Systems Biology Degree Program, National Taiwan University, Taipei 106, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.|Department of Life Science.', '-|-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/btx443', '29036526', 'Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries.'),
(81175, 2017, 'bio', 'SPATKIN: a simulator for rule-based modeling of biomolecular site dynamics on surfaces', 'Kochanczyk, Marek|Hlavacek, William S|Lipniacki, Tomasz', 'Institute of Fundamental Technological Research, Warsaw 02-106, Poland.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Institute of Fundamental Technological Research, Warsaw 02-106, Poland.', 'Poland|United States|Poland', 3, 'Algorithms|Computational Biology|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btx456', '29036531', 'Rule-based modeling is a powerful approach for studying biomolecular site dynamics. Here, we present SPATKIN, a general-purpose simulator for rule-based modeling in two spatial dimensions. The simulation algorithm is a lattice-based method that tracks Brownian motion of individual molecules and the stochastic firing of rule-defined reaction events. Because rules are used as event generators, the algorithm is network-free, meaning that it does not require to generate the complete reaction network implied by rules prior to simulation. In a simulation, each molecule (or complex of molecules) is taken to occupy a single lattice site that cannot be shared with another molecule (or complex). SPATKIN is capable of simulating a wide array of membrane-associated processes, including adsorption, desorption and crowding. Models are specified using an extension of the BioNetGen language, which allows to account for spatial features of the simulated process.'),
(81176, 2017, 'bio', 'gVolante for standardizing completeness assessment of genome and transcriptome assemblies', 'Nishimura, Osamu|Hara, Yuichiro|Kuraku, Shigehiro', 'Phyloinformatics Unit, RIKEN Center for Life Science Technologies, Kobe, Hyogo 650-0047, Japan.|Phyloinformatics Unit, RIKEN Center for Life Science Technologies, Kobe, Hyogo 650-0047, Japan.|Phyloinformatics Unit, RIKEN Center for Life Science Technologies, Kobe, Hyogo 650-0047, Japan.', 'Japan|Japan|Japan', 55, 'Gene Expression Profiling|Genomics|Reference Standards|Software', 'Journal Article', '10.1093/bioinformatics/btx445', '29036533', 'Along with the increasing accessibility to comprehensive sequence information, such as whole genomes and transcriptomes, the demand for assessing their quality has been multiplied. To this end, metrics based on sequence lengths, such as N50, have become a standard, but they only evaluate one aspect of assembly quality. Conversely, analyzing the coverage of pre-selected reference protein-coding genes provides essential content-based quality assessment, but the currently available pipelines for this purpose, CEGMA and BUSCO, do not have a user-friendly interface to serve as a uniform environment for assembly completeness assessment.'),
(81177, 2017, 'bio', 'pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites', 'Cheng, Xiang|Zhao, Shu-Guang|Lin, Wei-Zhong|Xiao, Xuan|Chou, Kuo-Chen', 'College of Information Science and Technology, Donghua University, Shanghai, China.|College of Information Science and Technology, Donghua University, Shanghai, China.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|The Gordon Life Science Institute, Boston, MA 02478, USA.', 'China|China|China|China|United States', 142, 'Amino Acid Sequence|Animals|Computational Biology|Intracellular Space|Protein Transport|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx476', '29036535', 'Cells are deemed the basic unit of life. However, many important functions of cells as well as their growth and reproduction are performed via the protein molecules located at their different organelles or locations. Facing explosive growth of protein sequences, we are challenged to develop fast and effective method to annotate their subcellular localization. However, this is by no means an easy task. Particularly, mounting evidences have indicated proteins have multi-label feature meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Unfortunately, most of the existing computational methods can only be used to deal with the single-label proteins. Although the iLoc-Animal predictor developed recently is quite powerful that can be used to deal with the animal proteins with multiple locations as well, its prediction quality needs to be improved, particularly in enhancing the absolute true rate and reducing the absolute false rate.'),
(81178, 2017, 'bio', 'wft4galaxy: a workflow testing tool for galaxy', 'Piras, Marco Enrico|Pireddu, Luca|Zanetti, Gianluigi', 'Data Intensive Computing, CRS4 (Center of Advanced Studies, Research and Development in Sardinia), Pula 09010, Italy.|Data Intensive Computing, CRS4 (Center of Advanced Studies, Research and Development in Sardinia), Pula 09010, Italy.|Data Intensive Computing, CRS4 (Center of Advanced Studies, Research and Development in Sardinia), Pula 09010, Italy.', 'Italy|Italy|Italy', 1, 'Automation|Computational Biology|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx461', '29036536', 'Workflow managers for scientific analysis provide a high-level programming platform facilitating standardization, automation, collaboration and access to sophisticated computing resources. The Galaxy workflow manager provides a prime example of this type of platform. As compositions of simpler tools, workflows effectively comprise specialized computer programs implementing often very complex analysis procedures. To date, no simple way to automatically test Galaxy workflows and ensure their correctness has appeared in the literature.'),
(81179, 2017, 'bio', 'CrocoBLAST: Running BLAST efficiently in the age of next-generation sequencing', 'Tristxc3xa3o Ramos, Ravi Josxc3xa9|de Azevedo Martins, Allan Cxc3xa9zar|da Silva Delgado, Gabrielle|Ionescu, Crina-Maria|xc3x9crmxc3xa9nyi, Turxc3xa1n Peter|Silva, Rosane|Koca, Jaroslav', 'CEITEC-Central European Institute of Technology, Masaryk University.|Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-092, Brazil.|Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-092, Brazil.|CEITEC-Central European Institute of Technology, Masaryk University.|Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-092, Brazil.|Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-092, Brazil.|CEITEC-Central European Institute of Technology, Masaryk University.', '-|Brazil|Brazil|-|Brazil|Brazil|-', 0, 'Escherichia coli|Genomics|High-Throughput Nucleotide Sequencing|Microbiota|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx465', '29036546', 'CrocoBLAST is a tool for dramatically speeding up BLAST+ execution on any computer. Alignments that would take days or weeks with NCBI BLAST+ can be run overnight with CrocoBLAST. Additionally, CrocoBLAST provides features critical for NGS data analysis, including: results identical to those of BLAST+, compatibility with any BLAST+ version, real-time information regarding calculation progress and remaining run time, access to partial alignment results, queueing, pausing, and resuming BLAST+ calculations without information loss.'),
(81180, 2017, 'bio', 'Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models', 'Chan, Siu H J|Cai, Jingyi|Wang, Lin|Simons-Senftle, Margaret N|Maranas, Costas D', 'Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16801, USA.|Beijing Key Lab of Bioprocess, College of Life and Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16801, USA.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16801, USA.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16801, USA.', 'United States|China|United States|United States|United States', 25, 'Archaea|Bacteria|Biomass|Computational Biology|Computer Simulation|Fungi|Genome|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btx453', '29036557', 'In a genome-scale metabolic model, the biomass produced is defined to have a molecular weight (MW) of 1xe2x80x89g mmol-1. This is critical for correctly predicting growth yields, contrasting multiple models and more importantly modeling microbial communities. However, the standard is rarely verified in the current practice and the chemical formulae of biomass components such as proteins, nucleic acids and lipids are often represented by undefined side groups (e.g. X, R).'),
(81181, 2017, 'bio', 'MethRaFo: MeDIP-seq methylation estimate using a Random Forest Regressor', 'Ding, Jun|Bar-Joseph, Ziv', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States', 5, 'DNA Methylation|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx449', '29036558', 'Profiling of genome wide DNA methylation is now routinely performed when studying development, cancer and several other biological processes. Although Whole genome Bisulfite Sequencing provides high-quality methylation measurements at the resolution of nucleotides, it is relatively costly and so several studies have used alternative methods for such profiling. One of the most widely used low cost alternatives is MeDIP-Seq. However, MeDIP-Seq is biased for CpG enriched regions and thus its results need to be corrected in order to determine accurate methylation levels.'),
(81182, 2017, 'bio', 'nandb-number and brightness in R with a novel automatic detrending algorithm', 'Nolan, Rory|Alvarez, Luis A J|Elegheert, Jonathan|Iliopoulou, Maro|Jakobsdottir, G Maria|Rodriguez-Muxc3xb1oz, Marina|Aricescu, A Radu|Padilla-Parra, Sergi', 'Wellcome Trust Centre for Human Genetics.|Wellcome Trust Centre for Human Genetics.|Department of Structural Biology, University of Oxford, Oxford OX3xe2x80x897BN, UK.|Wellcome Trust Centre for Human Genetics.|Wellcome Trust Centre for Human Genetics.|Wellcome Trust Centre for Human Genetics.|Department of Structural Biology, University of Oxford, Oxford OX3xe2x80x897BN, UK.|Wellcome Trust Centre for Human Genetics.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Algorithms|Animals|COS Cells|Cercopithecus aethiops|Humans|Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btx434', '29036562', 'An R package for performing number and brightness image analysis, with the implementation of a novel automatic detrending algorithm.'),
(81183, 2017, 'bio', 'ComplexViewer: visualization of curated macromolecular complexes', 'Combe, Colin W|Sivade, Marine Dumousseau|Hermjakob, Henning|Heimbach, Joshua|Meldal, Birgit H M|Micklem, Gos|Orchard, Sandra|Rappsilber, Juri', 'Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9?3BF, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, CB10?1SD, Cambridge, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, CB10?1SD, Cambridge, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2xe2x80x891QR, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, CB10?1SD, Cambridge, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2xe2x80x891QR, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, CB10?1SD, Cambridge, UK.|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9xe2x80x893BF, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Computational Biology|Macromolecular Substances|Models, Biological|Protein Binding|Protein Interaction Domains and Motifs|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btx497', '29036573', 'Proteins frequently function as parts of complexes, assemblages of multiple proteins and other biomolecules, yet network visualizations usually only show proteins as parts of binary interactions. ComplexViewer visualizes interactions with more than two participants and thereby avoids the need to first expand these into multiple binary interactions. Furthermore, if binding regions between molecules are known then these can be displayed in the context of the larger complex.'),
(81184, 2017, 'bio', 'BiobankUniverse: automatic matchmaking between datasets for biobank data discovery and integration', 'Pang, Chao|Kelpin, Fleur|van Enckevort, David|Eklund, Niina|Silander, Kaisa|Hendriksen, Dennis|de Haan, Mark|Jetten, Jonathan|de Boer, Tommy|Charbon, Bart|Holub, Petr|Hillege, Hans|Swertz, Morris A', 'Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.|Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Biobanking and BioMolecular Resources Research Infrastructure (BBMRI-ERIC), Graz, Austria.|Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Finland|Finland|Netherlands|Netherlands|Netherlands|Netherlands|', 2, 'Algorithms|Computational Biology|Databases, Factual|Software', 'Journal Article', '10.1093/bioinformatics/btx478', '29036577', 'Biobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions.'),
(81185, 2017, 'bio', 'HybridSim-VS: a web server for large-scale ligand-based virtual screening using hybrid similarity recognition techniques', 'Shang, Jinling|Dai, Xi|Li, Yecheng|Pistolozzi, Marco|Wang, Ling', 'School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China.', 'China|China|China|China|China', 14, 'Drug Design|Drug Evaluation, Preclinical|Internet|Ligands|Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx418', '29036579', 'Molecular-similarity searches based on two-dimensional (2D) fingerprint and three-dimensional (3D) shape represent two widely used ligand-based virtual screening (VS) methods in computer-aided drug design. 2D fingerprint-based VS utilizes the binary fragment information on a known ligand, whereas 3D shape-based VS takes advantage of geometric information for predefined features from a 3D conformation. Given their different advantages, it would be desirable to hybridize 2D fingerprint and 3D shape molecular-similarity approaches in drug discovery. Here, we presented a general hybrid molecular-similarity protocol, referred to as HybridSim, obtained by combining the 2D fingerprint- and 3D shape-based similarity search methods and evaluated its performance on 595,036 actives and decoys for 40 pharmaceutically relevant targets available in the Directory of Useful Decoys Enhanced (DUD-E). Our results showed that HybridSim significantly improved the overall performance in 40 VS projects as compared with using only 2D fingerprint and 3D shape methods. Furthermore, HybridSim-VS, the first online platform using the proposed HybridSim method coupled with 17,839,945 screenable and purchasable compounds, was developed to provide large-scale and proficient VS capabilities to experts and nonexperts in the field.'),
(81186, 2017, 'bio', 'cgHeliParm: analysis of dsDNA helical parameters for coarse-grained MARTINI molecular dynamics simulations', 'Faustino, Ignacio|Marrink, S J', 'Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, 9747 AG Groningen, The Netherlands.|Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, 9747 AG Groningen, The Netherlands.', 'Netherlands|Netherlands', 1, 'Base Pairing|Computational Biology|DNA|Molecular Conformation|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btx444', '29036584', 'We introduce cgHeliParm, a python program that provides the conformational analysis of Martini-based coarse-grained double strand DNA molecules. The software calculates the helical parameters such as base, base pair and base pair step parameters. cgHeliParm can be used for the analysis of coarse grain Martini molecular dynamics trajectories without transformation into atomistic models.'),
(81187, 2018, 'bio', 'A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures', 'Liu, Xinan|Yu, Ye|Liu, Jinpeng|Elliott, Corrine F|Qian, Chen|Liu, Jinze', 'Department of Computer Science, University of Kentucky, Lexington, KY, USA.|Department of Computer Science,University of Kentucky, Lexington, KY, USA.|Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY, USA.|Department of Computer Science, University of Kentucky, Lexington, KY, USA.|Department of Computer Engineering, UC Santa Cruz, Santa Cruz, CA, USA.|Department of Computer Science, University of Kentucky, Lexington, KY, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Genome, Microbial|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx432', '29036588', 'Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Although many algorithms have been developed to date, they suffer significant memory and/or computational costs. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand.'),
(81188, 2017, 'bio', 'PLACNETw: a web-based tool for plasmid reconstruction from bacterial genomes', 'Vielva, Luis|de Toro, María|Lanza, Val F|de la Cruz, Fernando', 'Departamento de Ingeniería de Comunicaciones, Universidad de Cantabria, Santander, Spain.|Plataforma de Genómica y Bioinformática, Centro de Investigación Biomédica de La Rioja (CIBIR), Logroño, Spain.|Departamento de Microbiología, Instituto de Investigación Sanitaria Ramón y Cajal (IRYCIS), Madrid, Spain.|Departamento de Biología Molecular, Universidad de Cantabria and Instituto de Biomedicina y Biotecnología de Cantabria (Universidad de Cantabria-CSIC), Santander, Spain.', 'Spain|Spain|Spain|Spain', 28, 'Bacteria|Gene Transfer, Horizontal|Genome, Bacterial|Genomics|Internet|Plasmids|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx462', '29036591', 'PLACNET is a graph-based tool for reconstruction of plasmids from next generation sequence pair-end datasets. PLACNET graphs contain two types of nodes (assembled contigs and reference genomes) and two types of edges (scaffold links and homology to references). Manual pruning of the graphs is a necessary requirement in PLACNET, but this is difficult for users without solid bioinformatic background. PLACNETw, a webtool based on PLACNET, provides an interactive graphic interface, automates BLAST searches, and extracts the relevant information for decision making. It allows a user with domain expertise to visualize the scaffold graphs and related information of contigs as well as reference sequences, so that the pruning operations can be done interactively from a personal computer without the need for additional tools. After successful pruning, each plasmid becomes a separate connected component subgraph. The resulting data are automatically downloaded by the user.'),
(81189, 2017, 'bio', 'DelPhiForce web server: electrostatic forces and energy calculations and visualization', 'Li, Lin|Jia, Zhe|Peng, Yunhui|Chakravorty, Arghya|Sun, Lexuan|Alexov, Emil', 'Department of Physics, Clemson University, Clemson, SC 29631, USA.|Department of Physics, Clemson University, Clemson, SC 29631, USA.|Department of Physics, Clemson University, Clemson, SC 29631, USA.|Department of Physics, Clemson University, Clemson, SC 29631, USA.|Department of Physics, Clemson University, Clemson, SC 29631, USA.|Department of Physics, Clemson University, Clemson, SC 29631, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|DNA|Internet|Models, Molecular|Protein Binding|Protein Conformation|Proteins|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx495', '29036596', 'Electrostatic force is an essential component of the total force acting between atoms and macromolecules. Therefore, accurate calculations of electrostatic forces are crucial for revealing the mechanisms of many biological processes. We developed a DelPhiForce web server to calculate and visualize the electrostatic forces at molecular level. DelPhiForce web server enables modeling of electrostatic forces on individual atoms, residues, domains and molecules, and generates an output that can be visualized by VMD software. Here we demonstrate the usage of the server for various biological problems including protein-cofactor, domain-domain, protein-protein, protein-DNA and protein-RNA interactions.'),
(81190, 2018, 'bio', 'Faucet: streaming de novo assembly graph construction', 'Rozov, Roye|Goldshlager, Gil|Halperin, Eran|Shamir, Ron', 'Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.|Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.|Departments of Computer Science, Anesthesiology and Perioperative Medicine, University of California Los Angeles, CA, USA.|Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.', 'Israel|United States|United States|Israel', 0, 'Algorithms|Genomics|Humans|Metagenome|Microbiota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx471', '29036597', 'We present Faucet, a two-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased.'),
(81191, 2017, 'bio', 'DeepLoc: prediction of protein subcellular localization using deep learning', 'Almagro Armenteros, José Juan|Sønderby, Casper Kaae|Sønderby, Søren Kaae|Nielsen, Henrik|Winther, Ole', 'Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.|The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.|The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.|The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 168, 'Computational Biology|Eukaryota|Eukaryotic Cells|Machine Learning|Models, Biological|Molecular Sequence Annotation|Neural Networks (Computer)|Protein Transport|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx431', '29036616', 'The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only.'),
(81192, 2017, 'bio', 'Using uncertainty to link and rank evidence from biomedical literature for model curation', 'Zerva, Chrysoula|Batista-Navarro, Riza|Day, Philip|Ananiadou, Sophia', 'National Centre for Text Mining, School of Computer Science.|National Centre for Text Mining, School of Computer Science.|Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|National Centre for Text Mining, School of Computer Science.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Mining|Machine Learning|Publications|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btx466', '29036627', 'In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models.'),
(81193, 2017, 'bio', 'GLASS: assisted and standardized assessment of gene variations from Sanger sequence trace data', 'Pal, Karol|Bystry, Vojtech|Reigl, Tomas|Demko, Martin|Krejci, Adam|Touloumenidou, Tasoula|Stalika, Evangelia|Tichy, Boris|Ghia, Paolo|Stamatopoulos, Kostas|Pospisilova, Sarka|Malcikova, Jitka|Darzentas, Nikos', 'CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Hematology Department and HCT Unit, G. Papanicolaou Hospital.|Institute of Applied Biosciences, Center for Research and Technology Hellas, Thessaloniki, Greece.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Division of Molecular Oncology, Department of Onco-Hematology, IRCCS San Raffaele Scientific Institute, Università Vita-Salute San Raffaele, Milan, Italy.|Institute of Applied Biosciences, Center for Research and Technology Hellas, Thessaloniki, Greece.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.', '-|-|-|-|-|-|Greece|-|Italy|Greece|-|-|-', 0, 'Alternative Splicing|Genotyping Techniques|Humans|Polymorphism, Genetic|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btx423', '29036643', 'Sanger sequencing is still being employed for sequence variant detection by many laboratories, especially in a clinical setting. However, chromatogram interpretation often requires manual inspection and in some cases, considerable expertise.'),
(81194, 2017, 'bio', 'MFIB: a repository of protein complexes with mutual folding induced by binding', 'Fichó, Erzsébet|Reményi, István|Simon, István|Mészáros, Bálint', 'Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary.|Institute of Enzymology, RCNS, Hungarian Academy of Sciences, Momentum Membrane Protein Bioinformatics Research Group, Budapest H-1117, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary.', 'Hungary|Hungary|Hungary|Hungary', 20, 'Computational Biology|Databases, Factual|Humans|Intrinsically Disordered Proteins|Protein Binding|Protein Folding', 'Journal Article', '10.1093/bioinformatics/btx486', '29036655', 'It is commonplace that intrinsically disordered proteins (IDPs) are involved in crucial interactions in the living cell. However, the study of protein complexes formed exclusively by IDPs is hindered by the lack of data and such analyses remain sporadic. Systematic studies benefited other types of protein-protein interactions paving a way from basic science to therapeutics, yet these efforts require reliable datasets that are currently lacking for synergistically folding complexes of IDPs.'),
(81195, 2018, 'bio', 'Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm', 'Takeda, Taikai|Hamada, Michiaki|Hancock, John', 'Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Humans|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx643', '29040374', 'Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies have examined the number of states suitable for representing sequence data or improving alignment accuracy.'),
(81196, 2018, 'bio', 'AEGS: identifying aberrantly expressed gene sets for differential variability analysis', 'Guan, Jinting|Chen, Moliang|Ye, Congting|Cai, James J|Ji, Guoli', 'Department of Automation, Xiamen University.|Department of Automation, Xiamen University.|College of Environment and Ecology, Xiamen University, Xiamen 361102, China.|Department of Veterinary Integrative Biosciences.|Department of Automation, Xiamen University.', '-|-|China|-|-', 0, 'Cloud Computing|Gene Expression Profiling|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx646', '29040376', 'In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g. the changes in expression variance) between groups may reveal underlying genetic heterogeneity and undetected interactions, which has great implications in many fields of biology. An easy-to-use tool for DV analysis is needed.'),
(81197, 2018, 'bio', 'Generalized correlation measure using count statistics for gene expression data with ordered samples', 'Wang, Y X Rachel|Liu, Ke|Theusch, Elizabeth|Rotter, Jerome I|Medina, Marisa W|Waterman, Michael S|Huang, Haiyan|Stegle, Oliver', 'School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.|Department of Statistics, University of California, Berkeley, CA 94720, USA.|Childrens Hospital Oakland Research Institute, Oakland, CA 94609, USA.|The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.|Childrens Hospital Oakland Research Institute, Oakland, CA 94609, USA.|Molecular and Computational Biology, University of Southern California, CA 90089, USA.|Department of Statistics, University of California, Berkeley, CA 94720, USA.|Department of Statistics, University of California, Berkeley, CA 94720, USA.', 'Australia|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Phenotype|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx641', '29040382', 'Capturing association patterns in gene expression levels under different conditions or time points is important for inferring gene regulatory interactions. In practice, temporal changes in gene expression may result in complex association patterns that require more sophisticated detection methods than simple correlation measures. For instance, the effect of regulation may lead to time-lagged associations and interactions local to a subset of samples. Furthermore, expression profiles of interest may not be aligned or directly comparable (e.g. gene expression profiles from two species).'),
(81198, 2018, 'bio', 'SELANSI: a toolbox for simulation of stochastic gene regulatory networks', 'Pájaro, Manuel|Otero-Muras, Irene|Vázquez, Carlos|Alonso, Antonio A', 'BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208 Vigo, Spain.|BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208 Vigo, Spain.|Department of Mathematics, University of A Coruña Campus Elviña s/n, 15071 A Coruña, Spain.|BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208 Vigo, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Computational Biology|Computer Simulation|Gene Regulatory Networks|Kinetics|Software|Stochastic Processes|Synthetic Biology|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx645', '29040384', 'Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort.'),
(81199, 2018, 'bio', 'Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data', 'Serra, Angela|Coretto, Pietro|Fratello, Michele|Tagliaferri, Roberto|Stegle, Oliver', 'NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.|STATLAB, Department of Economics and Statistics, University of Salerno, Fisciano (Sa), 84084, Italy.|Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Second University of Napoli, Piazza Luigi Miraglia, 2 Napoli 80138, Italy.|NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.|NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.', 'Italy|Italy|Italy|Italy|Italy', 3, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Neoplasms|Sensitivity and Specificity|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx642', '29040390', 'Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes.'),
(81200, 2018, 'bio', 'Compound image segmentation of published biomedical figures', 'Li, Pengyuan|Jiang, Xiangying|Kambhamettu, Chandra|Shatkay, Hagit', 'Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.', 'United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Computer Graphics|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btx611', '29040394', 'Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images.'),
(81201, 2018, 'bio', 'MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes', 'Pericard, Pierre|Dufresne, Yoann|Couderc, Loïc|Blanquart, Samuel|Touzet, Hélène', 'CRIStAL (UMR CNRS 9189, Université Lille 1).|CRIStAL (UMR CNRS 9189, Université Lille 1).|CRIStAL (UMR CNRS 9189, Université Lille 1).|CRIStAL (UMR CNRS 9189, Université Lille 1).|CRIStAL (UMR CNRS 9189, Université Lille 1).', '-|-|-|-|-', 6, 'Algorithms|Gastrointestinal Microbiome|High-Throughput Nucleotide Sequencing|Humans|Metagenome|Metagenomics|Phylogeny|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx644', '29040406', 'Advances in the sequencing of uncultured environmental samples, dubbed metagenomics, raise a growing need for accurate taxonomic assignment. Accurate identification of organisms present within a community is essential to understanding even the most elementary ecosystems. However, current high-throughput sequencing technologies generate short reads which partially cover full-length marker genes and this poses difficult bioinformatic challenges for taxonomy identification at high resolution.'),
(81202, 2018, 'bio', 'Machine learning accelerates MD-based binding pose prediction between ligands and proteins', 'Terayama, Kei|Iwata, Hiroaki|Araki, Mitsugu|Okuno, Yasushi|Tsuda, Koji', 'Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.|Foundation for Biomedical Research and Innovation, Hyogo 650-0047, Japan.|RIKEN Advanced Institute for Computational Science, Hyogo 650-0047, Japan.|RIKEN Advanced Institute for Computational Science, Hyogo 650-0047, Japan.|Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Drug Discovery|Ligands|Machine Learning|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btx638', '29040432', 'Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81203, 2018, 'bio', 'An omnibus test for differential distribution analysis of microbiome sequencing data', 'Chen, Jun|King, Emily|Deek, Rebecca|Wei, Zhi|Yu, Yue|Grill, Diane|Ballman, Karla|Stegle, Oliver', 'Division of Biomedical Statistics and Informatics.|Division of Biomedical Statistics and Informatics.|Department of Biostatistics, Columbia University, New York, NY 10032, USA.|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Division of Biomedical Statistics and Informatics.|Division of Biomedical Statistics and Informatics.|Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA.|Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA.', 'United States|United States|United States|Jersey|United States|United States|United States|United States', 17, 'Arthritis, Rheumatoid|Humans|Inflammatory Bowel Diseases|Metagenomics|Microbiota|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btx650', '29040451', 'One objective of human microbiome studies is to identify differentially abundant microbes across biological conditions. Previous statistical methods focus on detecting the shift in the abundance and/or prevalence of the microbes and treat the dispersion (spread of the data) as a nuisance. These methods also assume that the dispersion is the same across conditions, an assumption which may not hold in presence of sample heterogeneity. Moreover, the widespread outliers in the microbiome sequencing data make existing parametric models not overly robust. Therefore, a robust and powerful method that allows covariate-dependent dispersion and addresses outliers is still needed for differential abundance analysis.'),
(81204, 2018, 'bio', 'Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques', 'Niu, Jinglong|Shi, Yan|Cai, Maolin|Cao, Zhixin|Wang, Dandan|Zhang, Zhaozhi|Zhang, Xiaohua Douglas', 'School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.|School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.|School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.|Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China.|Faculty of Health Sciences, University of Macau, Taipa, Macau.|Department of Statistical Science, Duke University, Durham, NC 27708, USA.|Faculty of Health Sciences, University of Macau, Taipa, Macau.', 'China|China|China|China|United States|United States|United States', 23, 'Aged|Aged, 80 and over|Algorithms|Female|Humans|Image Processing, Computer-Assisted|Male|Respiratory Sounds|Sputum|Trachea', 'Journal Article', '10.1093/bioinformatics/btx652', '29040453', 'Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach.'),
(81205, 2018, 'bio', 'UC2 search: using unique connectivity of uncharged compounds for metabolite annotation by database searching in mass spectrometry-based metabolomics', 'Sakurai, Nozomu|Narise, Takafumi|Sim, Joon-Soo|Lee, Chang-Muk|Ikeda, Chiaki|Akimoto, Nayumi|Kanaya, Shigehiko|Stegle, Oliver', 'Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Jeonju 54874, Korea.|Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Jeonju 54874, Korea.|Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan.', 'Japan|Japan|-|-|Japan|Japan|Japan|Japan', 4, 'Databases, Protein|Humans|Mass Spectrometry|Metabolomics|Molecular Weight|Software', 'Journal Article', '10.1093/bioinformatics/btx649', '29040459', 'For metabolite annotation in metabolomics, variations in the registered states of compounds (charged molecules and multiple components, such as salts) and their redundancy among compound databases could be the cause of misannotations and hamper immediate recognition of the uniqueness of metabolites while searching by mass values measured using mass spectrometry. We developed a search system named UC2 (Unique Connectivity of Uncharged Compounds), where compounds are tentatively neutralized into uncharged states and stored on the basis of their unique connectivity of atoms after removing their stereochemical information using the first block in the hash of the IUPAC International Chemical Identifier, by which false-positive hits are remarkably reduced, both charged and uncharged compounds are properly searched in a single query and records having a unique connectivity are compiled in a single search result.'),
(81206, 2018, 'bio', 'SAGE2: parallel human genome assembly', 'Molnar, Michael|Haghshenas, Ehsan|Ilie, Lucian', 'Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Animals|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx648', '29045591', 'De novo genome assembly of next-generation sequencing data is a fundamental problem in bioinformatics. There are many programs that assemble small genomes, but very few can assemble whole human genomes. We present a new algorithm for parallel overlap graph construction, which is capable of assembling human genomes and improves upon the current state-of-the-art in genome assembly.'),
(81207, 2018, 'bio', 'A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease', 'Han, Lichy|Maciejewski, Mateusz|Brockel, Christoph|Gordon, William|Snapper, Scott B|Korzenik, Joshua R|Afzelius, Lovisa|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA.|Inflammation & Immunology, Pfizer Inc., Cambridge, MA 02139, USA.|Hills Pet Nutrition, Topeka, KS 66617, USA.|Inflammation & Immunology, Pfizer Inc., Cambridge, MA 02139, USA.|Division of Gastroenterology, Hepatology and Nutrition, Boston Childrens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Department of Gastroenterology, Hepatology and Endoscopy, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Inflammation & Immunology, Pfizer Inc., Cambridge, MA 02139, USA.|Department of Genetics.', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Adult|Child|Colitis, Ulcerative|Computational Biology|Crohn Disease|Diagnosis, Differential|Disease Progression|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Models, Biological|Protein Interaction Maps|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx651', '29048458', 'Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohns disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC.'),
(81208, 2018, 'bio', 'SliceMap: an algorithm for automated brain region annotation', 'Barbier, Michaël|Bottelbergs, Astrid|Nuydens, Rony|Ebneth, Andreas|De Vos, Winnok H|Murphy, Robert', 'Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2610 Wilrijk, Belgium.|Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium.|Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium.|Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2610 Wilrijk, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2610 Wilrijk, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 3, 'Algorithms|Animals|Brain|Computational Biology|Disease Models, Animal|Image Processing, Computer-Assisted|Mice|Mice, Transgenic|Software|Tauopathies', 'Journal Article', '10.1093/bioinformatics/btx658', '29048461', 'Many neurodegenerative disorders, such as Alzheimers Disease, pertain to or spread from specific sites of the brain. Hence, accurate disease staging or therapy assessment in transgenic model mice demands automated analysis of selected brain regions. To address this need, we have developed an algorithm, termed SliceMap, that enables contextual quantification by mapping anatomical information onto microtome-cut brain slices. For every newly acquired high-resolution image of a brain slice, the algorithm performs a coarse congealing-based registration to a library of pre-annotated reference slices. A subset of optimally matching reference slices is then used for refined, elastic registration. Morphotextural metrics are used to measure registration performance and to automatically detect poorly cut slices. We have implemented our method as a plugin for FIJI image analysis freeware, and we have used it to regionally quantify tau pathology in brain slices from a tauopathy (P301S) mouse model. By enabling region-based quantification, our method contributes to a more accurate assessment of neurodegenerative disease development.'),
(81209, 2018, 'bio', 'PyBEL: a computational framework for Biological Expression Language', 'Hoyt, Charles Tapley|Konotopez, Andrej|Ebeling, Christian|Wren, Jonathan', 'Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany.', 'Germany|Germany|Germany|Germany', 5, 'Alzheimer Disease|Computational Biology|Humans|Metabolic Networks and Pathways|Models, Biological|Programming Languages|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx660', '29048466', 'Biological Expression Language (BEL) assembles knowledge networks from biological relations across multiple modes and scales. Here, we present PyBEL, a software package for parsing, validating, converting, storing, querying, and visualizing networks encoded in BEL.'),
(81210, 2017, 'bio', 'Identification of copy number variations and translocations in cancer cells from Hi-C data', 'Chakraborty, Abhijit|Ay, Ferhat', 'La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92129.|La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92129.', '-|-', 20, '-', 'Journal Article', '10.1093/bioinformatics/btx664', '29048467', 'Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities (e.g., copy number variations (CNVs) and translocations) altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g., Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing.'),
(81211, 2018, 'bio', 'Cancer progression models and fitness landscapes: a many-to-many relationship', 'Diaz-Uriarte, Ramon', 'Department of Biochemistry, Universidad Autónoma de Madrid, Instituto de Investigaciones Biomédicas Alberto Sols (UAM-CSIC), Madrid 28029, Spain.', 'Spain', 5, 'Computational Biology|Disease Progression|Epistasis, Genetic|Humans|Models, Genetic|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx663', '29048486', 'The identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to identify these constraints, and return Directed Acyclic Graphs (DAGs) of restrictions where arrows indicate dependencies or constraints. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes-e.g. those with reciprocal sign epistasis-cannot be represented by CPMs.'),
(81212, 2018, 'bio', 'Krait: an ultrafast tool for genome-wide survey of microsatellites and primer design', 'Du, Lianming|Zhang, Chi|Liu, Qin|Zhang, Xiuyue|Yue, Bisong|Hancock, John', 'Institute for Advanced Study, Chengdu University, Chengdu, Sichuan 610106, China.|Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646099, China.|College of Life Sciences and Food Engineering, Yibin University, Yibin, Sichuan 644000, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.', 'China|China|China|China|China|China', 20, 'Bacteria|Eukaryota|Genomics|Humans|Microsatellite Repeats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx665', '29048524', 'Microsatellites are found to be related with various diseases and widely used in population genetics as genetic markers. However, it remains a challenge to identify microsatellite from large genome and screen microsatellites for primer design from a huge result dataset. Here, we present Krait, a robust and flexible tool for fast investigation of microsatellites in DNA sequences. Krait is designed to identify all types of perfect or imperfect microsatellites on a whole genomic sequence, and is also applicable to identification of compound microsatellites. Primer3 was seamlessly integrated into Krait so that users can design primer for microsatellite amplification in an efficient way. Additionally, Krait can export microsatellite results in FASTA or GFF3 format for further analysis and generate statistical report as well as plotting.'),
(81213, 2017, 'bio', 'iSmaRT: a toolkit for a comprehensive analysis of small RNA-Seq data', 'Panero, Riccardo|Rinaldi, Antonio|Memoli, Domenico|Nassa, Giovanni|Ravo, Maria|Rizzo, Francesca|Tarallo, Roberta|Milanesi, Luciano|Weisz, Alessandro|Giurato, Giorgio', 'Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.|Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, Sichuan 610065, China.', 'China|China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx647', '29059283', '-'),
(81214, 2018, 'bio', 'PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy', 'Song, Jiangning|Li, Fuyi|Leier, André|Marquez-Lago, Tatiana T|Akutsu, Tatsuya|Haffari, Gholamreza|Chou, Kuo-Chen|Webb, Geoffrey I|Pike, Robert N|Hancock, John', 'Monash Centre for Data Science, Faculty of Information Technology.|Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Monash Centre for Data Science, Faculty of Information Technology.|Gordon Life Science Institute, Boston, MA 02478, USA.|Monash Centre for Data Science, Faculty of Information Technology.|ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, VIC 3800, Australia.|ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, VIC 3800, Australia.', 'United States|United States|United States|United States|Japan|United States|United States|United States|Australia|Australia', 67, 'Computational Biology|Computer Simulation|Data Accuracy|Peptide Hydrolases|Proteolysis|Sequence Analysis, Protein|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btx670', '29069280', 'Proteases are enzymes that specifically cleave the peptide backbone of their target proteins. As an important type of irreversible post-translational modification, protein cleavage underlies many key physiological processes. When dysregulated, proteases actions are associated with numerous diseases. Many proteases are highly specific, cleaving only those target substrates that present certain particular amino acid sequence patterns. Therefore, tools that successfully identify potential target substrates for proteases may also identify previously unknown, physiologically relevant cleavage sites, thus providing insights into biological processes and guiding hypothesis-driven experiments aimed at verifying protease-substrate interaction. In this work, we present PROSPERous, a tool for rapid in silico prediction of protease-specific cleavage sites in substrate sequences. Our tool is based on logistic regression models and uses different scoring functions and their pairwise combinations to subsequently predict potential cleavage sites. PROSPERous represents a state-of-the-art tool that enables fast, accurate and high-throughput prediction of substrate cleavage sites for 90 proteases.'),
(81215, 2018, 'bio', 'Chromatin accessibility prediction via a hybrid deep convolutional neural network', 'Liu, Qiao|Xia, Fei|Yin, Qijin|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China.', 'China|United States|China|China', 15, 'Chromatin|Genome, Human|Genomics|Humans|Neural Networks (Computer)|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx679', '29069282', 'A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of causative genetic variants has become essential in both genomic and genetic studies.'),
(81216, 2018, 'bio', 'Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates', 'Thijssen, Bram|Dijkstra, Tjeerd M H|Heskes, Tom|Wessels, Lodewyk F A', 'Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen 72076, Germany.|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen 6500 GL, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.', 'Netherlands|Germany|Netherlands|Netherlands', 1, 'Bayes Theorem|Computational Biology|Models, Biological|Saccharomycetales', 'Journal Article', '10.1093/bioinformatics/btx666', '29069283', 'Computational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined.'),
(81217, 2018, 'bio', 'ARCS: scaffolding genome drafts with linked reads', 'Yeo, Sarah|Coombe, Lauren|Warren, Renxc3xa9 L|Chu, Justin|Birol, Inanxc3xa7', 'Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 26, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx675', '29069293', 'Sequencing of human genomes is now routine, and assembly of shotgun reads is increasingly feasible. However, assemblies often fail to inform about chromosome-scale structure due to a lack of linkage information over long stretches of DNA-a shortcoming that is being addressed by new sequencing protocols, such as the GemCode and Chromium linked reads from 10xe2x80x89xc3x97xe2x80x89Genomics.'),
(81218, 2018, 'bio', 'PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine', 'Rawi, Reda|Mall, Raghvendra|Kunji, Khalid|Shen, Chen-Hsiang|Kwong, Peter D|Chuang, Gwo-Yu', 'Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.', 'United States|Qatar|Qatar|United States|United States|United States', 21, 'Algorithms|Computational Biology|Proteins|Reproducibility of Results|Sequence Analysis, Protein|Software|Solubility', 'Journal Article', '10.1093/bioinformatics/btx662', '29069295', 'Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought.'),
(81219, 2018, 'bio', 'xMWAS: a data-driven integration and differential network analysis tool', 'Uppal, Karan|Ma, Chunyu|Go, Young-Mi|Jones, Dean P|Wren, Jonathan', 'Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA.|Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA.|Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA.|Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA.|Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA.', 'United States|United States|United States|United States|United States', 29, 'Animals|Gene Expression Regulation|Influenza A Virus, H1N1 Subtype|Metabolic Networks and Pathways|Mice|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx656', '29069296', 'Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data, however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.'),
(81220, 2018, 'bio', 'SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer', 'Beccuti, Marco|Cordero, Francesca|Arigoni, Maddalena|Panero, Riccardo|Amparore, Elvio G|Donatelli, Susanna|Calogero, Raffaele A', 'Department of Computer Sciences, University of Torino, 10124 Turin, Italy.|Department of Computer Sciences, University of Torino, 10124 Turin, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, 10124 Turin, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, 10124 Turin, Italy.|Department of Computer Sciences, University of Torino, 10124 Turin, Italy.|Department of Computer Sciences, University of Torino, 10124 Turin, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, 10124 Turin, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 8, 'Chromatin Immunoprecipitation|Computational Biology|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx674', '29069297', 'Short reads sequencing technology has been used for more than a decade now. However, the analysis of RNAseq and ChIPseq data is still computational demanding and the simple access to raw data does not guarantee results reproducibility between laboratories. To address these two aspects, we developed SeqBox, a cheap, efficient and reproducible RNAseq/ChIPseq hardware/software solution based on NUC6I7KYK mini-PC (an Intel consumer game computer with a fast processor and a high performance SSD disk), and Docker container platform. In SeqBox the analysis of RNAseq and ChIPseq data is supported by a friendly GUI. This allows access to fast and reproducible analysis also to scientists with/without scripting experience.'),
(81221, 2018, 'bio', 'A fast fiducial marker tracking model for fully automatic alignment in electron tomography', 'Han, Renmin|Zhang, Fa|Gao, Xin', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia.', 'Saudi Arabia|China|Saudi Arabia', 7, 'Algorithms|Electron Microscope Tomography|Fiducial Markers|Image Processing, Computer-Assisted|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btx653', '29069299', 'Automatic alignment, especially fiducial marker-based alignment, has become increasingly important due to the high demand of subtomogram averaging and the rapid development of large-field electron microscopy. Among the alignment steps, fiducial marker tracking is a crucial one that determines the quality of the final alignment. Yet, it is still a challenging problem to track the fiducial markers accurately and effectively in a fully automatic manner.'),
(81222, 2018, 'bio', 'ROSC-Pred: web-service for rodent organ-specific carcinogenicity prediction', 'Lagunin, Alexey|Rudik, Anastasia|Druzhilovsky, Dmitry|Filimonov, Dmitry|Poroikov, Vladimir|Wren, Jonathan', 'Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.|Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.|Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.|Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.|Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.|Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.', '-|-|-|-|-|-', 9, 'Animals|Bayes Theorem|Carcinogens|Computational Biology|Databases, Factual|Female|Male|Mice|Models, Biological|Organ Specificity|Rats|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btx678', '29069300', 'Identification of rodent carcinogens is an important task in risk assessment of chemicals. SAR methods were proposed to reduce the number of animal experiments. Most of these methods ignore information about organ-specificity of tumorigenesis. Our study was aimed at the creation of classification models and a freely available online service for prediction of rodent carcinogens considering the species (rats, mice), sex and tissue-specificity from structural formula of compounds.'),
(81223, 2018, 'bio', 'heatmaply: an R package for creating interactive cluster heatmaps for online publishing', 'Galili, Tal|OCallaghan, Alan|Sidi, Jonathan|Sievert, Carson', 'Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 6997801, Israel.|Fios Genomics, 9 Edinburgh Bioquarter, EH16 4UX, Scotland, UK.|Department of Statistics, Hebrew University, Jerusalem 9190401, Israel.|Department of Statistics, Iowa State University, 2438 Osborn Dr Ames, IA 50011-1090.', 'Israel|United Kingdom|Israel|United Kingdom', 63, 'Publishing|Software', 'Journal Article', '10.1093/bioinformatics/btx657', '29069305', 'heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout.'),
(81224, 2018, 'bio', 'CLIFinder: identification of LINE-1 chimeric transcripts in RNA-seq data', 'Pinson, Marie-Elisa|Pogorelcnik, Romain|Court, Franck|Arnaud, Philippe|Vaurs-Barrière, Catherine', 'GReD, Université Clermont Auvergne, CNRS, INSERM, 63001 Clermont-Ferrand, France.|GReD, Université Clermont Auvergne, CNRS, INSERM, 63001 Clermont-Ferrand, France.|GReD, Université Clermont Auvergne, CNRS, INSERM, 63001 Clermont-Ferrand, France.|GReD, Université Clermont Auvergne, CNRS, INSERM, 63001 Clermont-Ferrand, France.|GReD, Université Clermont Auvergne, CNRS, INSERM, 63001 Clermont-Ferrand, France.', 'France|France|France|France|France', 1, 'Gene Expression Profiling|Genome, Human|Genomics|Humans|Long Interspersed Nucleotide Elements|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx671', '29069308', 'L1 Chimeric Transcripts (LCTs) are initiated by repeated LINE-1 element antisense promoters and include the L1 5UTR sequence in antisense orientation followed by the adjacent genomic region. LCTs have been characterized mainly using bioinformatics approaches to query dbEST. To take advantage of NGS data to unravel the transcriptome composition, we developed Chimeric LIne Finder (CLIFinder), a new bioinformatics tool. Using stranded paired-end RNA-seq data, we demonstrated that CLIFinder can identify genome-wide transcribed chimera sequences corresponding to potential LCTs. Moreover, CLIFinder can be adapted to study transcription from other repeat types.'),
(81225, 2018, 'bio', 'mORCA: sailing bioinformatics world with mobile devices', 'Dxc3xadaz-Del-Pino, Sergio|Falgueras, Juan|Perez-Wohlfeil, Esteban|Trelles, Oswaldo', 'Department of Computer Architecture.|Department of Computer Languages, University of Malaga, 29071 Malaga, Spain.|Department of Computer Architecture.|Department of Computer Architecture.', '-|Spain|-|-', 1, 'Cell Phone|Computational Biology|Metadata|Mobile Applications|Web Browser', 'Journal Article', '10.1093/bioinformatics/btx673', '29069310', 'Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers to mobile devices but nothing could be further from the truth. The transition demands new paradigms, designs and novel implementations.'),
(81226, 2018, 'bio', 'PESTO: Parameter EStimation TOolbox', 'Stapor, Paul|Weindl, Daniel|Ballnus, Benjamin|Hug, Sabine|Loos, Carolin|Fiedler, Anna|Krause, Sabrina|Hroß, Sabrina|Fröhlich, Fabian|Hasenauer, Jan|Wren, Jonathan', 'Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Software', 'Journal Article', '10.1093/bioinformatics/btx676', '29069312', 'PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.'),
(81227, 2018, 'bio', 'Evaluation of tools for long read RNA-seq splice-aware alignment', 'Križanovic, Krešimir|Echchiki, Amina|Roux, Julien|Šikic, Mile', 'Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.|Département dEcologie et dEvolution, Université de Lausanne, Quartier Sorge, 1015 Lausanne, Switzerland.|Département dEcologie et dEvolution, Université de Lausanne, Quartier Sorge, 1015 Lausanne, Switzerland.|Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.', 'Croatia|Switzerland|Switzerland|Croatia', 22, 'Animals|Drosophila melanogaster|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx668', '29069314', 'High-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.'),
(81228, 2018, 'bio', 'Constructing prediction models from expression profiles for large scale lncRNA-miRNA interaction profiling', 'Huang, Yu-An|Chan, Keith C C|You, Zhu-Hong', 'Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong', 31, 'Algorithms|Area Under Curve|Gene Expression Profiling|Gene Expression Regulation|Humans|MicroRNAs|RNA, Long Noncoding|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx672', '29069317', 'The interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyze known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, we analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions. From these uncovered patterns, we noticed that lncRNAs tend to interact collaboratively with miRNAs of similar expression profiles, and vice versa.'),
(81229, 2018, 'bio', 'Bartender: a fast and accurate clustering algorithm to count barcode reads', 'Zhao, Lu|Liu, Zhimin|Levy, Sasha F|Wu, Song', 'Department of Applied Mathematics and Statistics.|Laufer Center for Physical and Quantitative Biology.|Laufer Center for Physical and Quantitative Biology.|Department of Applied Mathematics and Statistics.', '-|-|-|-', 11, 'Algorithms|Animals|Artifacts|Bacteria|Cluster Analysis|Data Accuracy|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx655', '29069318', 'Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.'),
(81230, 2017, 'bio', 'DISC: DISulfide linkage Characterization from tandem mass spectra', 'Liu, Yi|Sun, Weiping|Shan, Baozhen|Zhang, Kaizhong', 'Department of Computer Science, The University of Western Ontario, London, ON N6A 5B7, Canada.|Department of Computer Science, The University of Western Ontario, London, ON N6A 5B7, Canada.|Bioinformatics Solutions Inc. (BSI), Waterloo, ON N2L 6J2, Canada.|Department of Computer Science, The University of Western Ontario, London, ON N6A 5B7, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Databases, Protein|Disulfides|Ions|Peptides|Proteins|Proteome|Proteomics|Quality Control|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btx667', '29069330', 'Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results.'),
(81231, 2018, 'bio', 'ArachnoServer 30: an online resource for automated discovery, analysis and annotation of spider toxins', 'Pineda, Sandy S|Chaumeil, Pierre-Alain|Kunert, Anne|Kaas, Quentin|Thang, Mike W C|Le, Lien|Nuhn, Michael|Herzig, Volker|Saez, Natalie J|Cristofori-Armstrong, Ben|Anangi, Raveendra|Senff, Sebastian|Gorse, Dominique|King, Glenn F', 'Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|EMBL Australia Bioinformatics Resource (EMBL ABR), The University of Queensland, St Lucia, QLD 4072, Australia.|EMBL Australia Bioinformatics Resource (EMBL ABR), The University of Queensland, St Lucia, QLD 4072, Australia.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.|Institute for Molecular Bioscience.', '-|-|-|-|-|Australia|Australia|-|-|-|-|-|-|-', 36, 'Animals|Automation, Laboratory|Disulfides|Insect Proteins|Peptides|Spider Venoms', 'Journal Article', '10.1093/bioinformatics/btx661', '29069336', 'ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81232, 2018, 'bio', 'DEEPre: sequence-based enzyme EC number prediction by deep learning', 'Li, Yu|Wang, Sheng|Umarov, Ramzan|Xie, Bingqing|Fan, Ming|Li, Lihua|Gao, Xin', 'Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer Science Department, Illinois Institute of Technology, Chicago, IL 60616, USA.|Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.|Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.|Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|United States|China|China|Saudi Arabia', 48, 'Computational Biology|Enzymes|Humans|Machine Learning|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btx680', '29069344', 'Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.'),
(81233, 2018, 'bio', 'VecScreen_plus_taxonomy: imposing a tax(onomy) increase on vector contamination screening', 'Schäffer, Alejandro A|Nawrocki, Eric P|Choi, Yoon|Kitts, Paul A|Karsch-Mizrachi, Ilene|McVeigh, Richard', 'Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Bacteria|Databases, Nucleic Acid|Eukaryota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx669', '29069347', 'Nucleic acid sequences in public databases should not contain vector contamination, but many sequences in GenBank do (or did) contain vectors. The National Center for Biotechnology Information uses the program VecScreen to screen submitted sequences for contamination. Additional tools are needed to distinguish true-positive (contamination) from false-positive (not contamination) VecScreen matches.'),
(81234, 2018, 'bio', 'Unsupervised multiple kernel learning for heterogeneous data integration', 'Mariette, Jérôme|Villa-Vialaneix, Nathalie', 'MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France.|MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France.', 'France|France', 15, 'Breast Neoplasms|Computational Biology|Female|Humans|Software|Unsupervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx682', '29077792', 'Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.'),
(81235, 2018, 'bio', 'Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information', 'Tang, Zaixiang|Shen, Yueping|Li, Yan|Zhang, Xinyan|Wen, Jia|Qian, Chenao|Zhuang, Wenzhuo|Shi, Xinghua|Yi, Nengjun', 'Department of Biostatistics, School of Public Health.|Department of Biostatistics, School of Public Health.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics.|Department of Cell Biology, School of Biology & Basic Medical Science, Soochow University, Suzhou 215123, China.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.', 'United States|United States|United States|United States|United States|United States|China|United States|United States', 9, 'Algorithms|Bayes Theorem|Computational Biology|Genes|Humans|Linear Models|Metabolic Networks and Pathways|Models, Biological|Prognosis|Risk Factors', 'Journal Article', '10.1093/bioinformatics/btx684', '29077795', 'Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information.'),
(81236, 2018, 'bio', 'netReg: network-regularized linear models for biological association studies', 'Dirmeier, Simon|Fuchs, Christiane|Mueller, Nikola S|Theis, Fabian J', 'Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, 85764 Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, 85764 Neuherberg, Germany.', 'Switzerland|Germany|Germany|Germany', 2, 'Computational Biology|Gene Expression Profiling|Models, Biological|Protein Interaction Maps|Regression Analysis|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx677', '29077797', 'Modelling biological associations or dependencies using linear regression is often complicated when the analyzed data-sets are high-dimensional and less observations than variables are available (n xe2x89xaa p). For genomic data-sets penalized regression methods have been applied settling this issue. Recently proposed regression models utilize prior knowledge on dependencies, e.g. in the form of graphs, arguing that this information will lead to more reliable estimates for regression coefficients. However, none of the proposed models for multivariate genomic response variables have been implemented as a computationally efficient, freely available library. In this paper we propose netReg, a package for graph-penalized regression models that use large networks and thousands of variables. netReg incorporates a priori generated biological graph information into linear models yielding sparse or smooth solutions for regression coefficients.'),
(81237, 2017, 'bio', 'ProtDec-LTR20: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank', 'Chen, Junjie|Guo, Mingyue|Li, Shumin|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.', 'China|China|China|China', 0, 'Algorithms|Computational Biology|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Supervised Machine Learning|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btx429', '29077805', 'As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool.'),
(81238, 2017, 'bio', 'Quark enables semi-reference-based compression of RNA-seq data', 'Sarkar, Hirak|Patro, Rob', 'Department of Computer Science, Stony Brook University Stony Brook, NY 11794-2424, USA.|Department of Computer Science, Stony Brook University Stony Brook, NY 11794-2424, USA.', 'United States|United States', 0, 'Algorithms|Animals|Data Compression|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx428', '29077806', 'The past decade has seen an exponential increase in biological sequencing capacity, and there has been a simultaneous effort to help organize and archive some of the vast quantities of sequencing data that are being generated. Although these developments are tremendous from the perspective of maximizing the scientific utility of available data, they come with heavy costs. The storage and transmission of such vast amounts of sequencing data is expensive.'),
(81239, 2017, 'bio', 'pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R', 'Hon, Jirí|Martínek, Tomáš|Zendulka, Jaroslav|Lexa, Matej', 'IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic.|IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic.|IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic.|Department of Information Technology, Faculty of Informatics, Masaryk University, 60200 Brno, Czech Republic.', '-|-|-|-', 0, 'Algorithms|G-Quadruplexes|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx413', '29077807', 'G-quadruplexes (G4s) are one of the non-B DNA structures easily observed in vitro and assumed to form in vivo. The latest experiments with G4-specific antibodies and G4-unwinding helicase mutants confirm this conjecture. These four-stranded structures have also been shown to influence a range of molecular processes in cells. As G4s are intensively studied, it is often desirable to screen DNA sequences and pinpoint the precise locations where they might form.'),
(81240, 2017, 'bio', 'TITINdb-a computational tool to assess titins role as a disease gene', 'Laddach, Anna|Gautel, Mathias|Fraternali, Franca', 'Randall Division of Cell and Molecular Biophysics, Kings College London BHF Centre of Research Excellence, London SE1xe2x80x891UL, UK.|Randall Division of Cell and Molecular Biophysics, Kings College London BHF Centre of Research Excellence, London SE1xe2x80x891UL, UK.|Randall Division of Cell and Molecular Biophysics, Kings College London BHF Centre of Research Excellence, London SE1xe2x80x891UL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 16, 'Connectin|Disease|Genetic Predisposition to Disease|Genetic Variation|Protein Isoforms|Software', 'Journal Article', '10.1093/bioinformatics/btx424', '29077808', 'Large numbers of rare and unique titin missense variants have been discovered in both healthy and disease cohorts, thus the correct classification of variants as pathogenic or non-pathogenic has become imperative. Due to titins large size (363 coding exons), current web applications are unable to map titin variants to domain structures. Here, we present a web application, TITINdb, which integrates titin structure, variant, sequence and isoform information, along with pre-computed predictions of the impact of non-synonymous single nucleotide variants, to facilitate the correct classification of titin variants.'),
(81241, 2017, 'bio', 'Linking metabolic network features to phenotypes using sparse group lasso', 'Samal, Satya Swarup|Radulescu, Ovidiu|Weber, Andreas|Fröhlich, Holger', 'Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Bonn D-53113, Germany.|DIMNP UMR CNRS 5235, University of Montpellier, Montpellier, France.|Institut für Informatik II, University of Bonn, Bonn 53113, Germany.|Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Bonn D-53113, Germany.', 'Germany|France|Germany|Germany', 4, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Glioblastoma|Humans|Male|Metabolic Networks and Pathways|Phenotype|Prostatic Neoplasms', 'Journal Article', '10.1093/bioinformatics/btx427', '29077809', 'Integration of metabolic networks with -omics data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features.'),
(81242, 2017, 'bio', 'ISEScan: automated identification of insertion sequence elements in prokaryotic genomes', 'Xie, Zhiqun|Tang, Haixu', 'School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.', 'India|India', 0, 'Archaea|Bacteria|DNA Transposable Elements|Genome, Archaeal|Genome, Bacterial|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx433', '29077810', 'The insertion sequence (IS) elements are the smallest but most abundant autonomous transposable elements in prokaryotic genomes, which play a key role in prokaryotic genome organization and evolution. With the fast growing genomic data, it is becoming increasingly critical for biology researchers to be able to accurately and automatically annotate ISs in prokaryotic genome sequences. The available automatic IS annotation systems are either providing only incomplete IS annotation or relying on the availability of existing genome annotations. Here, we present a new IS elements annotation pipeline to address these issues.'),
(81243, 2017, 'bio', 'Reactome enhanced pathway visualization', 'Sidiropoulos, Konstantinos|Viteri, Guilherme|Sevilla, Cristoffer|Jupe, Steve|Webber, Marissa|Orlic-Milacic, Marija|Jassal, Bijay|May, Bruce|Shamovsky, Veronica|Duenas, Corina|Rothfels, Karen|Matthews, Lisa|Song, Heeyeon|Stein, Lincoln|Haw, Robin|DEustachio, Peter|Ping, Peipei|Hermjakob, Henning|Fabregat, Antonio', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10xe2x80x891SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10xe2x80x891SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10xe2x80x891SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10xe2x80x891SD, UK.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|NYU Langone Medical Center, New York, NY 10016, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10xe2x80x891SD, UK.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|NYU Langone Medical Center, New York, NY 10016, USA.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.|NYU Langone Medical Center, New York, NY 10016, USA.|Department of Physiology, Medicine and Bioinformatics, NIH BD2K Center of Excellence, University of California, Los Angeles, CA 90095, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Canada|Canada|Canada|Canada|United Kingdom|United Kingdom|Canada|United Kingdom|Canada|Canada|Canada|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Biological Phenomena|Computer Graphics|Gene Ontology|Internet|Knowledge Bases|Libraries|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btx441', '29077811', 'Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users own research presentations and publications.'),
(81244, 2018, 'bio', 'Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths', 'Zhang, Yijia|Zheng, Wei|Lin, Hongfei|Wang, Jian|Yang, Zhihao|Dumontier, Michel', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023,China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023,China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023,China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023,China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023,China.|Institute of Data Science, Maastricht University, Maastricht, 6229 ER, The Netherlands.', 'China|China|China|China|China|Netherlands', 37, 'Data Mining|Drug Interactions|Humans|Neural Networks (Computer)|Pharmacovigilance|Publications', 'Journal Article', '10.1093/bioinformatics/btx659', '29077847', 'Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction.'),
(81245, 2018, 'bio', 'Dynamic assessment of microbial ecology (DAME): a web app for interactive analysis and visualization of microbial sequencing data', 'Piccolo, Brian D|Wankhade, Umesh D|Chintapalli, Sree V|Bhattacharyya, Sudeepa|Chunqiao, Luo|Shankar, Kartik', 'Arkansas Childrens Nutrition Center, Little Rock, AR 72202, USA.|Arkansas Childrens Nutrition Center, Little Rock, AR 72202, USA.|Arkansas Childrens Nutrition Center, Little Rock, AR 72202, USA.|Arkansas Childrens Nutrition Center, Little Rock, AR 72202, USA.|Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72202, USA.|Arkansas Childrens Nutrition Center, Little Rock, AR 72202, USA.', 'United States|United States|United States|United States|United States|United States', 8, 'Data Interpretation, Statistical|Ecosystem|Microbiological Techniques|Microbiology|Programming Languages|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx686', '29087435', 'Dynamic assessment of microbial ecology (DAME) is a Shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of xcexb1-diversity and xcexb2-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive.'),
(81246, 2018, 'bio', 'ndexr-an R package to interface with the network data exchange', 'Auer, Florian|Hammoud, Zaynab|Ishkin, Alexandr|Pratt, Dexter|Ideker, Trey|Kramer, Frank', 'Department of Medical Statistics, University Medical Center Göttingen, Göttingen 37099, Germany.|Department of Medical Statistics, University Medical Center Göttingen, Göttingen 37099, Germany.|Discovery Science, Clarivate Analytics, Boston, MA 02210, USA.|Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medical Statistics, University Medical Center Göttingen, Göttingen 37099, Germany.', 'Germany|Germany|United States|United States|United States|Germany', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Protein Interaction Maps|Publications|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx683', '29087446', 'Seamless exchange of biological network data enables bioinformatic algorithms to integrate networks as prior knowledge input as well as to document resulting network output. However, the interoperability between pathway databases and various methods and platforms for analysis is currently lacking. The Network Data Exchange (NDEx) is an open-source data commons that facilitates the user-centered sharing and publication of networks of many types and formats.'),
(81247, 2018, 'bio', 'ChIPWig: a random access-enabling lossless and lossy compression method for ChIP-seq data', 'Ravanmehr, Vida|Kim, Minji|Wang, Zhiying|Milenkovic, Olgica', 'Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|The Henry Samueli School of Engineering, Center for Pervasive Communications and Computing (CPCC), University of California, Irvine, CA 92697, USA.|Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States', 1, 'Chromatin Immunoprecipitation|Data Compression|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx685', '29087447', 'Chromatin immunoprecipitation sequencing (ChIP-seq) experiments are inexpensive and time-efficient, and result in massive datasets that introduce significant storage and maintenance challenges. To address the resulting Big Data problems, we propose a lossless and lossy compression framework specifically designed for ChIP-seq Wig data, termed ChIPWig. ChIPWig enables random access, summary statistics lookups and it is based on the asymptotic theory of optimal point density design for nonuniform quantizers.'),
(81248, 2018, 'bio', 'ViewBS: a powerful toolkit for visualization of high-throughput bisulfite sequencing data', 'Huang, Xiaosan|Zhang, Shaoling|Li, Kongqing|Thimmapuram, Jyothi|Xie, Shaojun|Wren, Jonathan', 'College of Horticulture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, No.6 Tongwei Road Nanjing, P. R. China, 210095.|College of Horticulture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, No.6 Tongwei Road Nanjing, P. R. China, 210095.|Department of Rural Development, Nanjing Agricultural University, No.6 Tongwei Road Nanjing, P. R. China, 210095.|Bioinformatics Core, Purdue University, 155 South Grant Street, West Lafayette, Indiana 47907, USA.|Bioinformatics Core, Purdue University, 155 South Grant Street, West Lafayette, Indiana 47907, USA.|Bioinformatics Core, Purdue University, 155 South Grant Street, West Lafayette, Indiana 47907, USA.', 'China|China|China|India|India|India', 0, 'Animals|DNA Methylation|High-Throughput Nucleotide Sequencing|Plants|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btx633', '29087450', 'High throughput bisulfite sequencing (BS-seq) is an important technology to generate single-base DNA methylomes in both plants and animals. In order to accelerate the data analysis of BS-seq data, toolkits for visualization are required.'),
(81249, 2018, 'bio', 'MutaNET: a tool for automated analysis of genomic mutations in gene regulatory networks', 'Hollander, Markus|Hamed, Mohamed|Helms, Volkhard|Neininger, Kerstin', 'Center for Bioinformatics, Campus Building E2.1, Saarland University, D-66123 Saarbrücken, Germany.|Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, D-18057 Rostock, Germany.|Center for Bioinformatics, Campus Building E2.1, Saarland University, D-66123 Saarbrücken, Germany.|Center for Bioinformatics, Campus Building E2.1, Saarland University, D-66123 Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Bacteria|Drug Resistance, Bacterial|Gene Regulatory Networks|Genomics|High-Throughput Nucleotide Sequencing|Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx687', '29087464', 'Mutations in genomic key elements can influence gene expression and function in various ways, and hence greatly contribute to the phenotype. We developed MutaNET to score the impact of individual mutations on gene regulation and function of a given genome. MutaNET performs statistical analyses of mutations in different genomic regions. The tool also incorporates the mutations in a provided gene regulatory network to estimate their global impact. The integration of a next-generation sequencing pipeline enables calling mutations prior to the analyses. As application example, we used MutaNET to analyze the impact of mutations in antibiotic resistance (AR) genes and their potential effect on AR of bacterial strains.'),
(81250, 2018, 'bio', 'DIEGO: detection of differential alternative splicing using Aitchisons geometry', 'Doose, Gero|Bernhart, Stephan H|Wagener, Rabea|Hoffmann, Steve', 'Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.|Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.|Institute of Human Genetics, University of Ulm and University of Ulm Medical Center, 89081 Ulm, Germany.|Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.', '-|-|Germany|-', 2, 'Alternative Splicing|Protein Isoforms|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx690', '29088309', 'Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads.'),
(81251, 2018, 'bio', 'TMEM132: an ancient architecture of cohesin and immunoglobulin domains define a new family of neural adhesion molecules', 'Sanchez-Pulido, Luis|Ponting, Chris P', 'Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK.|Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK.', 'United Kingdom|United Kingdom', 7, 'Amino Acid Sequence|Animals|Cell Adhesion Molecules|Cell Cycle Proteins|Chromosomal Proteins, Non-Histone|Humans|Immunoglobulin Domains|Membrane Proteins|Multigene Family|Protein Domains|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx689', '29088312', 'The molecular functions of TMEM132 genes remain poorly understood and under-investigated despite their mutations associated with non-syndromic hearing loss, panic disorder and cancer. Here we show the full domain architecture of human TMEM132 family proteins solved using in-depth sequence and structural analysis. We reveal them to be five previously unappreciated cell adhesion molecules whose domain architecture has an early holozoan origin prior to the emergence of choanoflagellates and metazoa. The extra-cellular portions of TMEM132 proteins contain five conserved domains including three tandem immunoglobulin domains, and a cohesin domain homologue, the first such domain found in animals. These findings strongly predict a cellular adhesion function for TMEM132 family, connecting the extracellular medium with the intracellular actin cytoskeleton.'),
(81252, 2018, 'bio', 'An exact test for comparing a fixed quantitative property between gene sets', 'Parks, Matthew M', 'Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA.', 'United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Genome, Human|Humans|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btx693', '29088314', 'A significant difference in the distribution of a feature between two gene sets can provide insight into function or regulation. This statistical setting differs from much of hypothesis testing theory because the genome is often considered to be effectively fixed, finite and entirely known in commonly studied organisms, such as human. The Mann-Whitney U test is commonly employed in this scenario despite the assumptions of the test not being met, leading to unreliable and generally underpowered results. Permutation tests are also commonly employed for this purpose, but are computationally burdensome and are not tractable for obtaining small P values or for multiple comparisons.'),
(81253, 2018, 'bio', 'Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver', 'Peng, Yunhui|Sun, Lexuan|Jia, Zhe|Li, Lin|Alexov, Emil', 'Department of Physics and Astronomy, Clemson University, Clemson SC 29634, USA.|Department of Physics and Astronomy, Clemson University, Clemson SC 29634, USA.|Department of Physics and Astronomy, Clemson University, Clemson SC 29634, USA.|Department of Physics and Astronomy, Clemson University, Clemson SC 29634, USA.|Department of Physics and Astronomy, Clemson University, Clemson SC 29634, USA.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|DNA|DNA-Binding Proteins|Molecular Dynamics Simulation|Mutation, Missense|Protein Binding|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx698', '29091991', 'Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments.'),
(81254, 2018, 'bio', 'Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra', 'Deepaisarn, S|Tar, P D|Thacker, N A|Seepujak, A|McMahon, A W', 'Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PG, UK.|Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PG, UK.|Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PG, UK.|Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PG, UK.|Division of Informatics, Imaging and Data Sciences, The University of Manchester, M13 9PG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Biological Products|Cattle|Female|Goats|Meat Products|Milk|Molecular Weight|Normal Distribution|Sheep|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btx630', '29091994', 'Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian.'),
(81255, 2018, 'bio', 'Visualization portal for genetic variation (VizGVar): a tool for interactive visualization of SNPs and somatic mutations in exons, genes and protein domains', 'Solano-Román, Antonio|Alfaro-Arias, Verónica|Cruz-Castillo, Carlos|Orozco-Solano, Allan', 'Department of Industrial Design Engineering, Costa Rica Institute of Technology (TEC), Cartago 30101, Costa Rica.|Department of Industrial Design Engineering, Costa Rica Institute of Technology (TEC), Cartago 30101, Costa Rica.|CENFOTEC University, San José 11501, Costa Rica.|School of Health Technologies, Faculty of Medicine, University of Costa Rica, San Pedro de Montes de Oca, San José 2060, Costa Rica.', 'Costa Rica|Costa Rica|Costa Rica|Costa Rica', 0, 'Exons|Genome|Genomics|Humans|Mutation|Polymorphism, Single Nucleotide|Protein Domains|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/btx694', '29091996', 'VizGVar was designed to meet the growing need of the research community for improved genomic and proteomic data viewers that benefit from better information visualization.'),
(81256, 2018, 'bio', 'Efficient comparative phylogenetics on large trees', 'Louca, Stilianos|Doebeli, Michael', 'Biodiversity Research Centre, University of British Columbia, Vancouver, BC, V6T1Z4, Canada.|Biodiversity Research Centre, University of British Columbia, Vancouver, BC, V6T1Z4, Canada.', 'Canada|Canada', 50, 'Biodiversity|Phenotype|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/btx701', '29091997', 'Biodiversity databases now comprise hundreds of thousands of sequences and trait records. For example, the Open Tree of Life includes over 1 491 000 metazoan and over 300 000 bacterial taxa. These data provide unique opportunities for analysis of phylogenetic trait distribution and reconstruction of ancestral biodiversity. However, existing tools for comparative phylogenetics scale poorly to such large trees, to the point of being almost unusable.'),
(81257, 2018, 'bio', 'pStab: prediction of stable mutants, unfolding curves, stability maps and protein electrostatic frustration', 'Gopi, Soundhararajan|Devanshu, Devanshu|Krishna, Praveen|Naganathan, Athi N', 'Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India.', 'India|India|India|India', 0, 'Computational Biology|Models, Molecular|Mutation|Protein Stability|Proteins|Software|Static Electricity|Temperature', 'Journal Article', '10.1093/bioinformatics/btx697', '29092002', 'We present a web-server for rapid prediction of changes in protein stabilities over a range of temperatures and experimental conditions upon single- or multiple-point substitutions of charged residues. Potential mutants are identified by a charge-shuffling procedure while the stability changes (i.e. an unfolding curve) are predicted employing an ensemble-based statistical-mechanical model. We expect this server to be a simple yet detailed tool for engineering stabilities, identifying electrostatically frustrated residues, generating local stability maps and in constructing fitness landscapes.'),
(81258, 2018, 'bio', 'GRASS: semi-automated NMR-based structure elucidation of saccharides', 'Kapaev, Roman R|Toukach, Philip V', 'Higher Chemical College, Russian Academy of Sciences, 125047 Moscow, Russia.|N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 119991 Moscow, Russia.', '-|-', 0, 'Animals|Bacteria|Carbohydrate Conformation|Computational Biology|Glycomics|Humans|Internet|Magnetic Resonance Spectroscopy|Software', 'Journal Article', '10.1093/bioinformatics/btx696', '29092007', 'Carbohydrates play crucial roles in various biochemical processes and are useful for developing drugs and vaccines. However, in case of carbohydrates, the primary structure elucidation is usually a sophisticated task. Therefore, they remain the least structurally characterized class of biomolecules, and it hampers the progress in glycochemistry and glycobiology. Creating a usable instrument designed to assist researchers in natural carbohydrate structure determination would advance glycochemistry in biomedical and pharmaceutical applications.'),
(81259, 2018, 'bio', 'Machine learning annotation of human branchpoints', 'Signal, Bethany|Gloss, Brian S|Dinger, Marcel E|Mercer, Tim R', 'Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.|Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.|Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.|Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Genetic Variation|Genome, Human|Humans|Introns|Machine Learning|Molecular Sequence Annotation|RNA Splicing|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx688', '29092009', 'The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we have developed a machine-learning algorithm-branchpointer-to identify branchpoint elements solely from gene annotations and genomic sequence.'),
(81260, 2018, 'bio', 'MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota', 'Olekhnovich, Evgenii I|Vasilyev, Artem T|Ulyantsev, Vladimir I|Kostryukova, Elena S|Tyakht, Alexander V', 'Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation.|ITMO University, Saint Petersburg, Russian Federation.|ITMO University, Saint Petersburg, Russian Federation.|Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation.|Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, Moscow, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russian Federation', 8, 'Algorithms|Bacteria|Drug Resistance, Bacterial|Gastrointestinal Microbiome|Humans|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btx681', '29092015', 'Antibiotic resistance is an important global public health problem. Human gut microbiota is an accumulator of resistance genes potentially providing them to pathogens. It is important to develop tools for identifying the mechanisms of how resistance is transmitted between gut microbial species and pathogens.'),
(81261, 2018, 'bio', 'Simulating the dynamics of targeted capture sequencing with CapSim', 'Cao, Minh Duc|Ganesamoorthy, Devika|Zhou, Chenxi|Coin, Lachlan J M', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, St Lucia, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, St Lucia, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, St Lucia, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, St Lucia, QLD 4072, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx691', '29092025', 'Targeted sequencing using capture probes has become increasingly popular in clinical applications due to its scalability and cost-effectiveness. The approach also allows for higher sequencing coverage of the targeted regions resulting in better analysis statistical power. However, because of the dynamics of the hybridization process, it is difficult to evaluate the efficiency of the probe design prior to the experiments which are time consuming and costly.'),
(81262, 2018, 'bio', 'BEAM web server: a tool for structural RNA motif discovery', 'Pietrosanto, Marco|Adinolfi, Marta|Casula, Riccardo|Ausiello, Gabriele|Ferrè, Fabrizio|Helmer-Citterich, Manuela', 'Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.|Department of Pharmacy and Biotechnology, University of Bologna Alma Mater, 40126 Bologna, Italy.|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 2, 'Base Sequence|Databases, Factual|Internet|Nucleotide Motifs|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx704', '29095974', 'RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies.'),
(81263, 2018, 'bio', 'pedigreejs: a web-based graphical pedigree editor', 'Carver, Tim|Cunningham, Alex P|Babb de Villiers, Chantal|Lee, Andrew|Hartley, Simon|Tischkowitz, Marc|Walter, Fiona M|Easton, Douglas F|Antoniou, Antonis C', 'Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.|The Primary Care Unit, Department of Public Health and Primary Care.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.|Department of Medical Genetics and National Institute for Health Research Cambridge Biomedical Research Centre.|The Primary Care Unit, Department of Public Health and Primary Care.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory.', '-|-|-|-|-|-|-|-|-', 0, 'Female|Humans|Internet|Male|Medical History Taking|Pedigree|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btx705', '29095980', 'The collection, management and visualization of clinical pedigree (family history) data is a core activity in clinical genetics centres. However, clinical pedigree datasets can be difficult to manage, as they are time consuming to capture, and can be difficult to build, manipulate and visualize graphically. Several standalone graphical pedigree editors and drawing applications exist but there are no freely available lightweight graphical pedigree editors that can be easily configured and incorporated into web applications.'),
(81264, 2018, 'bio', 'Mosdepth: quick coverage calculation for genomes and exomes', 'Pedersen, Brent S|Quinlan, Aaron R', 'Department of Human Genetics.|Department of Human Genetics.', '-|-', 45, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Whole Exome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx699', '29096012', 'Mosdepth is a new command-line tool for rapidly calculating genome-wide sequencing coverage. It measures depth from BAM or CRAM files at either each nucleotide position in a genome or for sets of genomic regions. Genomic regions may be specified as either a BED file to evaluate coverage across capture regions, or as a fixed-size window as required for copy-number calling. Mosdepth uses a simple algorithm that is computationally efficient and enables it to quickly produce coverage summaries. We demonstrate that mosdepth is faster than existing tools and provides flexibility in the types of coverage profiles produced.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81265, 2018, 'bio', 'Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations', 'Cario, Clinton L|Witte, John S', 'Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.', 'United States|United States', 4, 'Female|Humans|Male|Mutation|Neoplasms|Sequence Analysis, DNA|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx709', '29106441', 'As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks.'),
(81266, 2018, 'bio', 'Estimation of QTL heritability based on pooled sequencing data', 'Tang, Weiqi|Huang, Likun|Bu, Suhong|Zhang, Xuzhang|Wu, Weiren', 'Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, College of Life Sciences.|Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, College of Life Sciences.|Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, College of Life Sciences.|Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, College of Life Sciences.|Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, College of Life Sciences.', '-|-|-|-|-', 0, 'Algorithms|Chromosome Mapping|Genetics, Population|High-Throughput Nucleotide Sequencing|Oryza|Quantitative Trait Loci|Quantitative Trait, Heritable|Sequence Analysis, DNA|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx703', '29106443', 'Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem.'),
(81267, 2018, 'bio', 'FunImageJ: a Lisp framework for scientific image processing', 'Harrington, Kyle I S|Rueden, Curtis T|Eliceiri, Kevin W', 'Computational and Physical Systems Group, Virtual Technology and Design, University of Idaho, Moscow, ID, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison.', 'United States|United States|United States', 3, 'Animals|Embryo, Mammalian|Image Processing, Computer-Assisted|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx710', '29106446', 'FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis.'),
(81268, 2017, 'bio', 'AlloSigMA: allosteric signaling and mutation analysis server', 'Guarnera, Enrico|Tan, Zhen Wah|Zheng, Zejun|Berezovsky, Igor N', 'Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.|Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.|Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.|Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 0, 'Allosteric Regulation|Allosteric Site|Computational Biology|Ligands|Models, Molecular|Models, Statistical|Mutation|Protein Binding|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btx430', '29106449', 'Allostery is an omnipresent mechanism of the function modulation in proteins via either effector binding or mutations in the exosites. Despite the growing number of online servers and databases devoted to prediction/classification of allosteric sites and their characteristics, there is a lack of resources for an efficient and quick estimation of the causality and energetics of allosteric communication.'),
(81269, 2018, 'bio', 'pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information', 'Cheng, Xiang|Xiao, Xuan|Chou, Kuo-Chen', 'Computer Science, Jingdezhen Ceramic Institute, Jingdezhen 333000, China.|Computer Science, Jingdezhen Ceramic Institute, Jingdezhen 333000, China.|Computer Science, Jingdezhen Ceramic Institute, Jingdezhen 333000, China.', 'China|China|China', 0, 'Computational Biology|Gene Ontology|Humans|Protein Transport|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx711', '29106451', 'For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence information alone. Although considerable efforts have been made in this regard, the problem is far from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for both basic research and drug design.'),
(81270, 2018, 'bio', 'ViraPipe: scalable parallel pipeline for viral metagenome analysis from next generation sequencing reads', 'Maarala, Altti Ilari|Bzhalava, Zurab|Dillner, Joakim|Heljanko, Keijo|Bzhalava, Davit', 'Department of Computer Science, Aalto University, Espoo, Finland.|Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Computer Science, Aalto University, Espoo, Finland.|Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.', 'Finland|Sweden|Sweden|Finland|Sweden', 0, 'Algorithms|Computers|Genome, Viral|High-Throughput Nucleotide Sequencing|Humans|Metagenome|Metagenomics|Microbiota|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx702', '29106455', 'Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments. We propose ViraPipe, a scalable metagenome analysis pipeline that is able to analyze thousands of human microbiomes in parallel in tolerable time. The pipeline is tuned for analyzing viral metagenomes and the software is applicable for other metagenomic analyses as well. ViraPipe integrates parallel BWA-MEM read aligner, MegaHit De novo assembler, and BLAST and HMMER3 sequence search tools. We show the scalability of ViraPipe by running experiments on mining virus related genomes from NGS datasets in a distributed Spark computing cluster.'),
(81271, 2018, 'bio', 'DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication', 'Tanizawa, Yasuhiro|Fujisawa, Takatomo|Nakamura, Yasukazu', 'Center for Information Biology, National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima 411-8540, Japan.|Center for Information Biology, National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima 411-8540, Japan.|Center for Information Biology, National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima 411-8540, Japan.', 'Japan|Japan|Japan', 167, 'Bacteria|Databases, Nucleic Acid|Genome, Bacterial|Genomics|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx713', '29106469', 'We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10xe2x80x89min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.'),
(81272, 2018, 'bio', 'TabPath: interactive tables for metabolic pathway analysis', 'Moraes, Lauro Ângelo Gonçalves de|Felestrino, Érica Barbosa|Assis, Renata de Almeida Barbosa|Matos, Diogo|Lima, Joubert de Castro|Lima, Leandro de Araújo|Almeida, Nalvo Franco|Setubal, João Carlos|Garcia, Camila Carrião Machado|Moreira, Leandro Marcio', 'Laboratório Multiusuário de Bioinformática, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Laboratório Multiusuário de Bioinformática, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA.|Faculdade de Computação, Universidade Federal do Mato Grosso do Sul, Mato Grosso do Sul, MS 79070-900, Brazil.|Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo 05508-000, Brazil.|Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.|Laboratório Multiusuário de Bioinformática, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|United States|Brazil|Brazil|Brazil|Brazil', 1, 'Genome|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btx714', '29112698', 'Information about metabolic pathways in a comparative context is one of the most powerful tool to help the understanding of genome-based differences in phenotypes among organisms. Although several platforms exist that provide a wealth of information on metabolic pathways of diverse organisms, the comparison among organisms using metabolic pathways is still a difficult task.'),
(81273, 2018, 'bio', 'CommWalker: correctly evaluating modules in molecular networks in light of annotation bias', 'Luecken, M D|Page, M J T|Crosby, A J|Mason, S|Reinert, G|Deane, C M', 'Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Informatics, UCB Pharma, Slough SL1 3WE, UK.|Immunology Therapeutic Area, UCB Pharma, Slough SL1 3WE, UK.|Immunology Therapeutic Area, UCB Pharma, Slough SL1 3WE, UK.|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx706', '29112702', 'Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins.'),
(81274, 2018, 'bio', 'NanoStringNormCNV: pre-processing of NanoString CNV data', 'Sendorek, Dorota H|Lalonde, Emilie|Yao, Cindy Q|Sabelnykova, Veronica Y|Bristow, Robert G|Boutros, Paul C', 'Informatics and Biocomputing Program, Ontario Institute for Cancer Research.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research.|Department of Medical Biophysics, University of Toronto.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research.', '-|-|-|-|-|-', 1, 'Algorithms|DNA Copy Number Variations|Genomics|Humans|Male|Prostatic Neoplasms|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx707', '29112706', 'The NanoString System is a well-established technology for measuring RNA and DNA abundance. Although it can estimate copy number variation, relatively few tools support analysis of these data. To address this gap, we created NanoStringNormCNV, an R package for pre-processing and copy number variant calling from NanoString data. This package implements algorithms for pre-processing, quality-control, normalization and copy number variation detection. A series of reporting and data visualization methods support exploratory analyses. To demonstrate its utility, we apply it to a new dataset of 96 genes profiled on 41 prostate tumour and 24 matched normal samples.'),
(81275, 2017, 'bio', 'Identifying structural variants using linked-read sequencing data', 'Elyanow, Rebecca|Wu, Hsin-Ta|Raphael, Benjamin J', 'Center for Computational Molecular Biology, Brown University, Providence, 029012, USA.|Center for Computational Molecular Biology, Brown University, Providence, 029012, USA.|Department of Computer Science, Princeton University, Princeton, 08540, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx712', '29112732', 'Structural variation, including large deletions, duplications, inversions, translocations, and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (~5-10) DNA molecules ~50Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants.'),
(81276, 2018, 'bio', 'PERF: an exhaustive algorithm for ultra-fast and efficient identification of microsatellites from large DNA sequences', 'Avvaru, Akshay Kumar|Sowpati, Divya Tej|Mishra, Rakesh Kumar', 'CSIR - Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500007, India.|CSIR - Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500007, India.|CSIR - Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500007, India.', 'India|India|India', 10, 'Algorithms|Genome, Human|Genomics|Humans|Microsatellite Repeats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx721', '29121165', 'Microsatellites or Simple Sequence Repeats (SSRs) are short tandem repeats of DNA motifs present in all genomes. They have long been used for a variety of purposes in the areas of population genetics, genotyping, marker-assisted selection and forensics. Numerous studies have highlighted their functional roles in genome organization and gene regulation. Though several tools are currently available to identify SSRs from genomic sequences, they have significant limitations.'),
(81277, 2018, 'bio', 'MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool', 'Lochovsky, Lucas|Zhang, Jing|Gerstein, Mark', 'Program in Computational Biology and Bioinformatics.|Program in Computational Biology and Bioinformatics.|Program in Computational Biology and Bioinformatics.', '-|-|-', 3, 'DNA Mutational Analysis|Genomics|Humans|Mutation|Mutation Rate|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx700', '29121169', 'Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of xe2x88xbc250.'),
(81278, 2018, 'bio', 'PREFMD: a web server for protein structure refinement via molecular dynamics simulations', 'Heo, Lim|Feig, Michael', 'Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.|Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 0, 'Caspases|Computers|Internet|Molecular Dynamics Simulation|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx726', '29126101', 'Refinement of protein structure models is a long-standing problem in structural bioinformatics. Molecular dynamics-based methods have emerged as an avenue to achieve consistent refinement. The PREFMD web server implements an optimized protocol based on the method successfully tested in CASP11. Validation with recent CASP refinement targets shows consistent and more significant improvement in global structure accuracy over other state-of-the-art servers.'),
(81279, 2018, 'bio', 'AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing', 'Shin, Sunguk|Lee, Hanna|Son, Hyeonju|Paik, Soonmyung|Kim, Sangwoo', 'Severance Biomedical Science Institute.|Severance Biomedical Science Institute.|Severance Biomedical Science Institute.|Severance Biomedical Science Institute.|Severance Biomedical Science Institute.', '-|-|-|-|-', 2, 'Diagnostic Errors|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btx719', '29126106', 'Ion Torrent sequencing is one of the most frequently used platforms in healthcare research and industry. Despite many advantages, platform-specific artifacts complicate efficient separation of true variants from errors, especially in variants with lower allele frequencies (<15%). Here, we developed a multi-step filtering toolbox AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to xe2x88xbc98% reduction of false variants when combined to conventional public pipelines and xe2x88xbc48% to the in-house commercial solution, with a minimal loss of sensitivity.'),
(81280, 2018, 'bio', 'Cost-effective and accurate method of measuring fetal fraction using SNP imputation', 'Kim, Minjeong|Kim, Jai-Hoon|Kim, Kangseok|Kim, Sunshin', 'Department of Knowledge Information Engineering, Ajou University, 16499 Suwon, Korea.|Department of Knowledge Information Engineering, Ajou University, 16499 Suwon, Korea.|Department of Knowledge Information Engineering, Ajou University, 16499 Suwon, Korea.|GenomeCare, 16499 Suwon, Korea.', '-|-|-|-', 0, 'Algorithms|Alleles|Cost-Benefit Analysis|DNA|Data Accuracy|Female|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide|Pregnancy|Prenatal Diagnosis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx728', '29126132', 'With the discovery of cell-free fetal DNA in maternal blood, the demand for non-invasive prenatal testing (NIPT) has been increasing. To obtain reliable NIPT results, it is important to accurately estimate the fetal fraction. In this study, we propose an accurate and cost-effective method for measuring fetal fractions using single-nucleotide polymorphisms (SNPs).'),
(81281, 2018, 'bio', 'RepLong: de novo repeat identification using long read sequencing data', 'Guo, Rui|Li, Yan-Ran|He, Shan|Ou-Yang, Le|Sun, Yiwen|Zhu, Zexuan', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.|School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.|College of Information Science.|School of Medicine, Shenzhen University, Shenzhen 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.', 'China|China|United Kingdom|United Kingdom|China|China', 0, 'Algorithms|Animals|Drosophila melanogaster|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Phylogeny|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx717', '29126180', 'The identification of repetitive elements is important in genome assembly and phylogenetic analyses. The existing de novo repeat identification methods exploiting the use of short reads are impotent in identifying long repeats. Since long reads are more likely to cover repeat regions completely, using long reads is more favorable for recognizing long repeats.'),
(81282, 2018, 'bio', 'Complex analyses of inverted repeats in mitochondrial genomes revealed their importance and variability', 'Cechovxc3xa1, Jana|Lxc3xbdsek, Jirxc3xad|Bartas, Martin|Brxc3xa1zda, Vxc3xa1clav', 'Department of Biophysical Chemistry and Molecular Oncology, Institute of Biophysics, The Czech Academy of Sciences, 612 65 Brno, Czech Republic.|Department of Informatics, Mendel University in Brno, 613 00 Brno, Czech Republic.|Department of Biophysical Chemistry and Molecular Oncology, Institute of Biophysics, The Czech Academy of Sciences, 612 65 Brno, Czech Republic.|Department of Biophysical Chemistry and Molecular Oncology, Institute of Biophysics, The Czech Academy of Sciences, 612 65 Brno, Czech Republic.', '-|-|-|-', 0, 'Eukaryota|Evolution, Molecular|Genome, Mitochondrial|Genomics|Inverted Repeat Sequences|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx729', '29126205', 'The NCBI database contains mitochondrial DNA (mtDNA) genomes from numerous species. We investigated the presence and locations of inverted repeat sequences (IRs) in these mtDNA sequences, which are known to be important for regulating nuclear genomes.'),
(81283, 2018, 'bio', 'LIBRA-WA: a web application for ligand binding site detection and protein function recognition', 'Toti, Daniele|Viet Hung, Le|Tortosa, Valentina|Brandi, Valentina|Polticelli, Fabio', 'Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Science and Technology, Nguyen Tat Thanh University, Ho chi Minh City, Vietnam.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.', 'Italy|-|Italy|Italy|Italy', 8, 'Algorithms|Binding Sites|Catalytic Domain|Computational Biology|Humans|Kinesin|Ligands|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx715', '29126218', 'Recently, LIBRA, a tool for active/ligand binding site prediction, was described. LIBRAs effectiveness was comparable to similar state-of-the-art tools, however, its scoring scheme, output presentation, dependence on local resources and overall convenience were amenable to improvements. To solve these issues, LIBRA-WA, a web application based on an improved LIBRA engine, has been developed, featuring a novel scoring scheme consistently improving LIBRAs performance, and a refined algorithm that can identify binding sites hosted at the interface between different subunits. LIBRA-WA also sports additional functionalities like ligand clustering and a completely redesigned interface for an easier analysis of the output. Extensive tests on 373 apoprotein structures indicate that LIBRA-WA is able to identify the biologically relevant ligand/ligand binding site in 357 cases (xe2x88xbc96%), with the correct prediction ranking first in 349 cases (xe2x88xbc98% of the latter, xe2x88xbc94% of the total). The earlier stand-alone tool has also been updated and dubbed LIBRA+, by integrating LIBRA-WAs improved engine for cross-compatibility purposes.'),
(81284, 2018, 'bio', 'Bringing numerous methods for expression and promoter analysis to a public cloud computing service', 'Polanski, Krzysztof|Gao, Bo|Mason, Sam A|Brown, Paul|Ott, Sascha|Denby, Katherine J|Wild, David L', 'Department of Statistics.|Department of Statistics.|Systems Biology Centre.|Department of Mathematics.|Systems Biology Centre.|Department of Biology, University of York, York YO10 5DD, UK.|Department of Statistics.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cloud Computing|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Promoter Regions, Genetic|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx692', '29126246', 'Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We have taken 14 popular methods, the majority related to expression data or promoter analysis, developed these up to a good implementation standard and housed the tools in isolated Docker containers which we integrated into the CyVerse Discovery Environment, making these easily usable for a wide community as part of the CyVerse UK project.'),
(81285, 2018, 'bio', 'Deep learning for tumor classification in imaging mass spectrometry', 'Behrmann, Jens|Etmann, Christian|Boskamp, Tobias|Casadonte, Rita|Kriegsmann, Jörg|Maaß, Peter', 'Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.|Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.|Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.|Proteopath GmbH, 54296 Trier, Germany.|Proteopath GmbH, 54296 Trier, Germany.|Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Computational Biology|Humans|Mass Spectrometry|Neoplasm Proteins|Neoplasms|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx724', '29126286', 'Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification.'),
(81286, 2018, 'bio', 'Membranome 20: database for proteome-wide profiling of bitopic proteins and their dimers', 'Lomize, Andrei L|Hage, Jacob M|Pogozheva, Irina D', 'Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109-1065, USA.|Electrical Engineering and Computer Science Department, College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109-1065, USA.', 'United States|United States|United States', 4, 'Databases, Genetic|Humans|Membrane Proteins|Protein Domains|Protein Multimerization|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btx720', '29126305', 'Structural studies of TM domains of single-spanning (bitopic) membrane proteins are impeded by their instability, flexibility and heterogeneity. The new computational method TMDOCK allows reliable modeling of homodimers of transmembrane (TM) xcexb1-helices on a proteomic scale.'),
(81287, 2018, 'bio', 'Predicting loop conformational ensembles', 'Marks, Claire|Shi, Jiye|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Chemistry, UCB Pharma, Slough SL1 3WE, UK.|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.', 'United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Computational Biology|Models, Molecular|Protein Conformation', 'Comparative Study', '10.1093/bioinformatics/btx718', '29136084', 'Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a targets conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures.'),
(81288, 2018, 'bio', 'microRPM: a microRNA prediction model based only on plant small RNA sequencing data', 'Tseng, Kuan-Chieh|Chiang-Hsieh, Yi-Fan|Pai, Hsuan|Chow, Chi-Nga|Lee, Shu-Chuan|Zheng, Han-Qin|Kuo, Po-Li|Li, Guan-Zhen|Hung, Yu-Cheng|Lin, Na-Sheng|Chang, Wen-Chi', 'College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.|Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan.|College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.', '-|-|-|-|-|-|-|-|-|-|-', 0, 'Computational Biology|Genome, Plant|High-Throughput Nucleotide Sequencing|MicroRNAs|Plants|RNA, Plant|Sequence Analysis, RNA|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx725', '29136092', 'MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary.'),
(81289, 2018, 'bio', 'Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction', 'de Oliveira, Saulo H P|Law, Eleanor C|Shi, Jiye|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Informatics, UCB Pharma, Slough SL1 3WE, UK.|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Animals|Caspase 12|Computational Biology|Humans|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx722', '29136098', 'Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally.'),
(81290, 2018, 'bio', 'Identification of topological features in renal tumor microenvironment associated with patient survival', 'Cheng, Jun|Mo, Xiaokui|Wang, Xusheng|Parwani, Anil|Feng, Qianjin|Huang, Kun', 'Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.|Center for Biostatistics, The Ohio State University Wexner Medical Center.|Department of Electrical and Computer Engineering.|Department of Pathology.|Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.|Department of Electrical and Computer Engineering.', 'China|-|-|-|China|-', 0, 'Adult|Aged|Aged, 80 and over|Carcinoma, Papillary|Female|Humans|Image Processing, Computer-Assisted|Kidney Neoplasms|Machine Learning|Male|Middle Aged|Prognosis|Tumor Microenvironment', 'Journal Article', '10.1093/bioinformatics/btx723', '29136101', 'As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologists visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem.'),
(81291, 2018, 'bio', 'Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks', 'Avsec, Žiga|Barekatain, Mohammadamin|Cheng, Jun|Gagneur, Julien', 'Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.', 'Germany|Germany|Germany|Germany', 7, 'DNA|Genomics|Hep G2 Cells|Humans|K562 Cells|Machine Learning|Models, Genetic|Neural Networks (Computer)|Protein Binding|Proteins|RNA|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx727', '29155928', 'Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed.'),
(81292, 2018, 'bio', 'QVZ: lossy compression of quality values', 'Malysa, Greg|Hernaez, Mikel|Ochoa, Idoia|Rao, Milind|Ganesan, Karthik|Weissman, Tsachy', 'Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.|Department of Informatics, Technical University of Munich, 85748 Garching, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx654', '29177464', '-'),
(81293, 2018, 'bio', 'Extreme learning machines for reverse engineering of gene regulatory networks from expression time series', 'Rubiolo, M|Milone, D H|Stegmayer, G', 'Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, 3000 Santa Fe, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, 3000 Santa Fe, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, 3000 Santa Fe, Argentina.', 'Argentina|Argentina|Argentina', 7, 'Computational Biology|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Models, Genetic|Saccharomyces cerevisiae|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx730', '29182723', 'The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene expression data.'),
(81294, 2018, 'bio', 'CALQ: compression of quality values of aligned sequencing data', 'Voges, Jan|Ostermann, Jxc3xb6rn|Hernaez, Mikel', 'Fakultxc3xa4t fxc3xbcr Elektrotechnik und Informatik, Institut fxc3xbcr Informationsverarbeitung (TNT), Leibniz Universitxc3xa4t Hannover, 30167 Hannover, Germany.|Fakultxc3xa4t fxc3xbcr Elektrotechnik und Informatik, Institut fxc3xbcr Informationsverarbeitung (TNT), Leibniz Universitxc3xa4t Hannover, 30167 Hannover, Germany.|Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801, USA.', 'Germany|Germany|United States', 8, 'Algorithms|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx737', '29186284', 'Recent advancements in high-throughput sequencing technology have led to a rapid growth of genomic data. Several lossless compression schemes have been proposed for the coding of such data present in the form of raw FASTQ files and aligned SAM/BAM files. However, due to their high entropy, losslessly compressed quality values account for about 80% of the size of compressed files. For the quality values, we present a novel lossy compression scheme named CALQ. By controlling the coarseness of quality value quantization with a statistical genotyping model, we minimize the impact of the introduced distortion on downstream analyses.'),
(81295, 2018, 'bio', 'Myriads: P-value-based multiple testing correction', 'Carvajal-Rodríguez, Antonio', 'Department of Biochemistry, Genetics and Immunology, Address Facultad de Biologia, University of Vigo, Vigo, Spain.', 'Spain', 7, 'Computational Biology|Software', 'Journal Article', '10.1093/bioinformatics/btx746', '29186285', 'There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P-values as currently demanded by microarray and other omic technologies.'),
(81296, 2017, 'bio', 'Reproducible and flexible simulation experiments with ML-Rules and SESSL', 'Warnke, T|Helms, T|Uhrmacher, A M', 'University of Rostock, Institute of Computer Science, Albert-Einstein-Straße 22, 18059 Rostock, Germany.|University of Rostock, Institute of Computer Science, Albert-Einstein-Straße 22, 18059 Rostock, Germany.|University of Rostock, Institute of Computer Science, Albert-Einstein-Straße 22, 18059 Rostock, Germany.', 'Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx741', '29186288', 'The modeling language ML-Rules allows specifying and simulating complex systems biology models at multiple levels of organization. The development of such simulation models involves a wide variety of simulation experiments and the replicability of generated simulation results requires suitable means for documenting simulation experiments. Embedded domain-specific languages, such as SESSL, cater both requirements. With SESSL, the user can integrate diverse simulation experimentation methods and third-party software components into an executable, readable simulation experiment specification. A newly developed SESSL binding for ML-Rules exploits these features of SESSL, opening up new possibilities for executing and documenting simulation experiments with ML-Rules models.'),
(81297, 2018, 'bio', 'scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data', 'Liu, Tong|Wang, Zheng', 'Department of Computer Science, University of Miami, Coral Gables, FL 33124, USA.|Department of Computer Science, University of Miami, Coral Gables, FL 33124, USA.', 'United States|United States', 7, 'Animals|Bias|Humans|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx747', '29186290', 'We build a software package scHiCNorm that uses zero-inflated and hurdle models to remove biases from single-cell Hi-C data. Our evaluations prove that our models can effectively eliminate systematic biases for single-cell Hi-C data, which better reveal cell-to-cell variances in terms of chromosomal structures.'),
(81298, 2018, 'bio', 'SATORI: a system for ontology-guided visual exploration of biomedical data repositories', 'Lekschas, Fritz|Gehlenborg, Nils', 'Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA 02138, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States', 3, 'Animals|Biological Ontologies|Computational Biology|Humans|Internet|Metadata|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/btx739', '29186292', 'The ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating datasets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find datasets of interest.'),
(81299, 2018, 'bio', 'Classifying next-generation sequencing data using a zero-inflated Poisson model', 'Zhou, Yan|Wan, Xiang|Zhang, Baoxue|Tong, Tiejun', 'College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen 518060, China.|Department of Computer Science, and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|School of Statistics, Capital University of Economics and Business, Beijing 100070, China.|Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.', 'China|Hong Kong|China|Hong Kong', 3, 'Breast Neoplasms|Discriminant Analysis|Female|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx768', '29186294', 'With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18-30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros.'),
(81300, 2018, 'bio', 'SimulaTE: simulating complex landscapes of transposable elements of populations', 'Kofler, Robert', 'Institute für Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria.', 'Austria', 2, 'DNA Transposable Elements|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx772', '29186298', 'Estimating the abundance of transposable elements (TEs) in populations (or tissues) promises to answer many open research questions. However, progress is hampered by the lack of concordance between different approaches for TE identification and thus potentially unreliable results.'),
(81301, 2018, 'bio', 'Mapping-free variant calling using haplotype reconstruction from k-mer frequencies', 'Audano, Peter A|Ravishankar, Shashidhar|Vannberg, Fredrik O', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia', 5, 'Algorithms|Genome, Bacterial|Genomics|Haplotypes|Multilocus Sequence Typing|Polymorphism, Genetic|Software|Streptococcus pneumoniae', 'Journal Article', '10.1093/bioinformatics/btx753', '29186321', 'The standard protocol for detecting variation in DNA is to map millions of short sequence reads to a known reference and find loci that differ. While this approach works well, it cannot be applied where the sample contains dense variants or is too distant from known references. De novo assembly or hybrid methods can recover genomic variation, but the cost of computation is often much higher. We developed a novel k-mer algorithm and software implementation, Kestrel, capable of characterizing densely packed SNPs and large indels without mapping, assembly or de Bruijn graphs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81302, 2018, 'bio', 'SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles', 'Koehorst, Jasper J|van Dam, Jesse C J|Saccenti, Edoardo|Martins Dos Santos, Vitor A P|Suarez-Diez, Maria|Schaap, Peter J', 'Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 11, 'Genomics|Molecular Sequence Annotation|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/btx767', '29186322', 'To unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyze existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis.'),
(81303, 2018, 'bio', 'An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition', 'Luo, Ling|Yang, Zhihao|Yang, Pei|Zhang, Yin|Wang, Lei|Lin, Hongfei|Wang, Jian', 'College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|Beijing Institute of Health Administration and Medical Information, Beijing 100850, China.|Beijing Institute of Health Administration and Medical Information, Beijing 100850, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.', 'China|China|China|China|China|China|China', 63, 'Biomedical Research|Chemistry|Data Mining|Neural Networks (Computer)|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btx761', '29186323', 'In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem.'),
(81304, 2018, 'bio', 'Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing', 'Liao, Peizhou|Satten, Glen A|Hu, Yi-Juan', 'Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.|Division of Reproductive Health, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.', 'United States|United States|United States', 0, 'Algorithms|Genetics, Population|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Principal Component Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx708', '29186324', 'Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project.'),
(81305, 2018, 'bio', 'glactools: a command-line toolset for the management of genotype likelihoods and allele counts', 'Renaud, Gabriel', 'Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany.', 'Germany', 2, 'Alleles|Genetics, Population|Genotype|Humans|Metagenomics|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btx749', '29186325', 'Research projects involving population genomics routinely need to store genotyping information, population allele counts, combine files from different samples, query the data and export it to various formats. This is often done using bespoke in-house scripts, which cannot be easily adapted to new projects and seldom constitute reproducible workflows.'),
(81306, 2018, 'bio', 'Efficiently counting all orbits of graphlets of any order in a graph using autogenerated equations', 'Melckenbeeck, Ine|Audenaert, Pieter|Colle, Didier|Pickavet, Mario', 'IDLab, Ghent University - Imec, B-9052 Ghent, Belgium.|IDLab, Ghent University - Imec, B-9052 Ghent, Belgium.|IDLab, Ghent University - Imec, B-9052 Ghent, Belgium.|IDLab, Ghent University - Imec, B-9052 Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 4, 'Algorithms|Computational Biology|Computer Graphics', 'Journal Article', '10.1093/bioinformatics/btx758', '29186327', 'Graphlets are a useful tool to determine a graphs small-scale structure. Finding them is exponentially hard with respect to the number of nodes in each graphlet. Therefore, equations can be used to reduce the size of graphlets that need to be enumerated to calculate the number of each graphlet touching each node. Hoxc4x8devar and Demxc5xa1ar first introduced such equations, which were derived manually, and an algorithm that uses them, but only graphlets with four or five nodes can be counted this way.'),
(81307, 2018, 'bio', 'Meta-analytic principal component analysis in integrative omics application', 'Kim, SungHwan|Kang, Dongwan|Huo, Zhiguang|Park, Yongseok|Tseng, George C', 'Department of Statistics, Keimyung University, Daegu 42601, South Korea.|Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA.', 'United States|United States|United States|United States|United States', 8, 'Animals|DNA Methylation|Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Humans|Male|Meta-Analysis as Topic|Mice|Neoplasms|Principal Component Analysis|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx765', '29186328', 'With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization.'),
(81308, 2018, 'bio', 'ASElux: an ultra-fast and accurate allelic reads counter', 'Miao, Zong|Alvarez, Marcus|Pajukanta, Pxc3xa4ivi|Ko, Arthur', 'Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA.|Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA.|Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA.|Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA.', 'United States|United States|United States|United States', 0, 'Alleles|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx762', '29186329', 'Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment.'),
(81309, 2018, 'bio', 'DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches', 'Olayan, Rawan S|Ashoor, Haitham|Bajic, Vladimir B', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.', 'Saudi Arabia|United States|Saudi Arabia', 0, 'Area Under Curve|Computational Biology|Drug Interactions|Humans|Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx731', '29186331', 'Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.'),
(81310, 2018, 'bio', 'The systematic analysis of ultraconserved genomic regions in the budding yeast', 'Yang, Zhi-Kai|Gao, Feng', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx751', '29186332', '-'),
(81311, 2018, 'bio', 'FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web', 'Probst, Daniel|Reymond, Jean-Louis', 'Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Berne, 3012 Berne, Switzerland.|Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Berne, 3012 Berne, Switzerland.', 'Switzerland|Switzerland', 15, 'Algorithms|Data Visualization|Databases, Factual|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btx760', '29186333', 'During the past decade, big data have become a major tool in scientific endeavors. Although statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib and plot.ly, do not support interactive visualizations of datasets exceeding 100 000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations. However, recent hardware developments, especially advancements in graphical processing units, allow for the rendering of millions of data points on a wide range of consumer hardware such as laptops, tablets and mobile phones. Similar to the challenges and opportunities brought to virtually every scientific field by big data, both the visualization of and interaction with copious amounts of data are both demanding and hold great promise.'),
(81312, 2018, 'bio', 'Phenotypic stability and plasticity in GMP-derived cells as determined by their underlying regulatory network', 'Ramírez, Carlos|Mendoza, Luis', 'Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México.|Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Mx., CP04510, México.', '-|-', 0, 'Animals|Cell Differentiation|Computational Biology|Gene Regulatory Networks|Hematopoiesis|Humans|Models, Biological|Monocyte-Macrophage Precursor Cells', 'Journal Article', '10.1093/bioinformatics/btx736', '29186334', 'Blood cell formation has been recognized as a suitable system to study celular differentiation mainly because of its experimental accessibility, and because it shows characteristics such as hierarchical and gradual bifurcated patterns of commitment, which are present in several developmental processes. Although hematopoiesis has been extensively studied and there is a wealth of molecular and cellular data about it, it is not clear how the underlying molecular regulatory networks define or restrict cellular differentiation processes. Here, we infer the molecular regulatory network that controls the differentiation of a blood cell subpopulation derived from the granulocyte-monocyte precursor (GMP), comprising monocytes, neutrophils, eosinophils, basophils and mast cells.'),
(81313, 2018, 'bio', 'The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery', 'Ivanov, Andrei A|Revennaugh, Brian|Rusnak, Lauren|Gonzalez-Pecchi, Valentina|Mo, Xiulei|Johns, Margaret A|Du, Yuhong|Cooper, Lee A D|Moreno, Carlos S|Khuri, Fadlo R|Fu, Haian', 'Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.|Winship Cancer Institute of Emory University.|Winship Cancer Institute of Emory University.|Winship Cancer Institute of Emory University.|Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine.', '-|-|-|-|-|-|-|-|-|-|-', 14, 'Cloud Computing|Humans|Internet|Neoplasm Proteins|Neoplasms|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btx743', '29186335', 'As cancer genomics initiatives move toward comprehensive identification of genetic alterations in cancer, attention is now turning to understanding how interactions among these genes lead to the acquisition of tumor hallmarks. Emerging pharmacological and clinical data suggest a highly promising role of cancer-specific protein-protein interactions (PPIs) as druggable cancer targets. However, large-scale experimental identification of cancer-related PPIs remains challenging, and currently available resources to explore oncogenic PPI networks are limited.'),
(81314, 2018, 'bio', '3dRPC: a web server for 3D RNA-protein structure prediction', 'Huang, Yangyu|Li, Haotian|Xiao, Yi', 'Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.', 'China|China|China', 0, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Internet|Molecular Docking Simulation|Nucleic Acid Conformation|Nucleotides|Protein Conformation|RNA|RNA-Binding Proteins|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/btx742', '29186336', 'RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction.'),
(81315, 2018, 'bio', 'CytoCtrlAnalyser: a Cytoscape app for biomolecular network controllability analysis', 'Wu, Lin|Li, Min|Wang, Jianxin|Wu, Fang-Xiang', 'Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9.|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China.|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9.', 'Canada|China|China|Canada', 10, 'Algorithms|Computational Biology|Humans|Metabolic Networks and Pathways|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx764', '29186337', 'Studying the controllability of biomolecular networks can result in profound knowledge about molecular biological systems. However, there is no comprehensive and easy-to-use platform for analyzing controllability of biomolecular networks although various algorithms for analyzing complex network controllability have been proposed recently. In this application note, we develop the CytoCtrlAnalyser which is a Cytoscape app to provide a comprehensive platform for analyzing controllability of biomolecular networks. Nine algorithms have been integrated in CytoCtrlAnalyser. With network topologies and customized control settings imported into CytoCtrlAnalyser, users can identify the steering nodes which should be actuated by input control signals for achieving different control objectives as well as investigate the importance of nodes from different perspectives in the controllability of networks. CytoCtrlAnalyser offers a tool for many promising applications, such as identification of potential drug targets or biologically important nodes in biomolecular networks.'),
(81316, 2018, 'bio', 'Cpf1-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cpf1', 'Park, Jeongbin|Bae, Sangsu', 'Department of Chemistry, Hanyang University, Seoul 04763, South Korea.|Department of Chemistry, Hanyang University, Seoul 04763, South Korea.', '-|-', 7, 'CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Endonucleases|Gene Editing|Gene Knockout Techniques|Genome|Genomic Library|Internet|RNA, Guide', 'Journal Article', '10.1093/bioinformatics/btx695', '29186338', 'Following the type II CRISPR-Cas9 system, type V CRISPR-Cpf1 endonucleases have been found to be applicable for genome editing in various organisms in vivo. However, there are as yet no web-based tools capable of optimally selecting guide RNAs (gRNAs) among all possible genome-wide target sites. Here, we present Cpf1-Database, a genome-wide gRNA library design tool for LbCpf1 and AsCpf1, which have DNA recognition sequences of 5-TTTN-3 at the 5 ends of target sites. Cpf1-Database provides a sophisticated but simple way to design gRNAs for AsCpf1 nucleases on the genome scale. One can easily access the data using a straightforward web interface, and using the powerful collections feature one can easily design gRNAs for thousands of genes in short time.'),
(81317, 2018, 'bio', 'bioalcidae, samjs and vcffilterjs: object-oriented formatters and filters for bioinformatics files', 'Lindenbaum, Pierre|Redon, Richard', 'Cardiovascular Genetics, LInstitut du Thorax, INSERM, CNRS, UNIV Nantes, Nantes 44007, France.|Cardiovascular Genetics, LInstitut du Thorax, INSERM, CNRS, UNIV Nantes, Nantes 44007, France.', 'France|France', 3, 'Computational Biology|High-Throughput Nucleotide Sequencing|Information Storage and Retrieval|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx734', '29186339', 'Reformatting and filtering bioinformatics files are common tasks for bioinformaticians. Standard Linux tools and specific programs are usually used to perform such tasks but there is still a gap between using these tools and the programming interface of some existing libraries.'),
(81318, 2018, 'bio', 'Semi-supervised network inference using simulated gene expression dynamics', 'Nguyen, Phan|Braun, Rosemary', 'Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA.|Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA.', 'United States|United States', 1, 'Computational Biology|Computer Simulation|Gene Expression Regulation|Gene Regulatory Networks|Models, Biological|Sample Size|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx748', '29186340', 'Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regulatory dynamics, leading to networks with missing and anomalous links. Integration of prior network information (e.g. from pathway databases) has the potential to improve reconstructions.'),
(81319, 2018, 'bio', 'MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks', 'Keel, Brittney N|Deng, Bo|Moriyama, Etsuko N', 'USDA †, ARS, U.S. Meat Animal Research Center, Clay Center, NE 68933, USA.|Department of Mathematics, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.|School of Biological Sciences and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Bacteria|Cluster Analysis|Eukaryota|Protein Domains|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx755', '29186344', 'Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure.'),
(81320, 2018, 'bio', 'GDSCTools for mining pharmacogenomic interactions in cancer', 'Cokelaer, Thomas|Chen, Elisabeth|Iorio, Francesco|Menden, Michael P|Lightfoot, Howard|Saez-Rodriguez, Julio|Garnett, Mathew J', 'Institut Pasteur-Bioinformatics and Biostatistics Hub-C3BI, USR 3756 IP CNRS, Paris, France.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.|Oncology Innovative Medicines and Early Development, AstraZeneca, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'France|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Antineoplastic Agents|Cell Line, Tumor|Computational Biology|Drug Discovery|Genomics|Humans|Neoplasms|Pharmacogenetics|Software', 'Journal Article', '10.1093/bioinformatics/btx744', '29186349', 'Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.'),
(81321, 2018, 'bio', 'Reactome diagram viewer: data structures and strategies to boost performance', 'Fabregat, Antonio|Sidiropoulos, Konstantinos|Viteri, Guilherme|Marin-Garcia, Pablo|Ping, Peipei|Stein, Lincoln|DEustachio, Peter|Hermjakob, Henning', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.|Fundacixc3xb3n Investigacixc3xb3n INCLIVA, Universitat de Valxc3xa8ncia, Valencia, Spain.|NIH BD2K Center of Excellence and Department of Physiology, Medicine and Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Ontario Institute for Cancer Research, Toronto ON M5G 0A3, Canada.|NYU Langone Medical Center, New York NY 10016, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|Spain|United Kingdom|Canada|United Kingdom|United Kingdom', 11, 'Algorithms|Computational Biology|Databases, Factual|Humans|Internet|Knowledge Bases|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btx752', '29186351', 'Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community.'),
(81322, 2018, 'bio', 'Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach', 'Ali, Mehreen|Khan, Suleiman A|Wennerberg, Krister|Aittokallio, Tero', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290 Helsinki, Finland.', 'Finland|Finland|Finland|Finland', 0, 'Antineoplastic Agents|Biomarkers|Cell Line, Tumor|Gene Expression Regulation, Neoplastic|Humans|Mass Spectrometry|Neoplasms|Protein Array Analysis|Proteomics', 'Journal Article', '10.1093/bioinformatics/btx766', '29186355', 'Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds.'),
(81323, 2018, 'bio', 'shinyCircos: an R/Shiny application for interactive creation of Circos plot', 'Yu, Yiming|Ouyang, Yidan|Yao, Wen', 'National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.|National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.|National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.', 'China|China|China', 33, 'Computer Graphics|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx763', '29186362', 'Creation of Circos plot is one of the most efficient approaches to visualize genomic data. However, the installation and use of existing tools to make Circos plot are challenging for users lacking of coding experiences. To address this issue, we developed an R/Shiny application shinyCircos, a graphical user interface for interactive creation of Circos plot. shinyCircos can be easily installed either on computers for personal use or on local or public servers to provide online use to the community. Furthermore, various types of Circos plots could be easily generated and decorated with simple mouse-click.'),
(81324, 2018, 'bio', 'PipelineDog: a simple and flexible graphic pipeline construction and maintenance tool', 'Zhou, Anbo|Zhang, Yeting|Sun, Yazhou|Xing, Jinchuan', 'Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.|Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.|New Jersey Center for Science, Technology & Mathematics, Kean University, Union, NJ 07083, USA.|Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.', 'Jersey|Jersey|Jersey|Jersey', 0, 'Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx759', '29186378', 'Analysis pipelines are an essential part of bioinformatics research, and ad hoc pipelines are frequently created by researchers for prototyping and proof-of-concept purposes. However, most existing pipeline management system or workflow engines are too complex for rapid prototyping or learning the pipeline concept. A lightweight, user-friendly and flexible solution is thus desirable. In this study, we developed a new pipeline construction and maintenance tool, PipelineDog. This is a web-based integrated development environment with a modern web graphical user interface. It offers cross-platform compatibility, project management capabilities, code formatting and error checking functions and an online repository. It uses an easy-to-read/write script system that encourages code reuse. With the online repository, it also encourages sharing of pipelines, which enhances analysis reproducibility and accountability. For most users, PipelineDog requires no software installation. Overall, this web application provides a way to rapidly create and easily manage pipelines.'),
(81325, 2018, 'bio', 'PlanNET: homology-based predicted interactome for multiple planarian transcriptomes', 'Castillo-Lara, S|Abril, J F', 'Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Catalonia, Spain.|Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Catalonia, Spain.', 'Spain|Spain', 5, 'Animals|Gene Expression Profiling|Humans|Internet|Planarians|Protein Interaction Maps|Regeneration|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx738', '29186384', 'Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein-protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities.'),
(81326, 2017, 'bio', 'Identifying simultaneous rearrangements in cancer genomes', 'Oesper, Layla|Dantas, Simone|Raphael, Benjamin J', 'Department of Computer Science, Carleton College, Northfield, MN, 55057, USA.|Institute of Mathematics and Statistics, Fluminense Federal University, Niterói, Rio de Janeiro, Brazil.|Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.', 'United States|Brazil|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx745', '29186385', 'The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis, where a large number of mutations instead occur simultaneously. A number of potential signatures of chromothripsis have been proposed. In this work we provide a rigorous formulation and analysis of the ability to walk the derivative chromosome signature originally proposed by Korbel and Campbell (2013). In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction, which allows us to overcome some of the limitations of the original signature.'),
(81327, 2018, 'bio', 'SPECTRE: a suite of phylogenetic tools for reticulate evolution', 'Bastkowski, Sarah|Mapleson, Daniel|Spillner, Andreas|Wu, Taoyang|Balvociute, Monika|Moulton, Vincent', 'Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.|Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.|Merseburg University of Applied Sciences, 06217 Merseburg, Germany.|School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.|Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.', 'United Kingdom|United Kingdom|Germany|United Kingdom|Germany|United Kingdom', 1, 'Algorithms|Gene Library|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btx740', '29186450', 'Split-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here, we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing environments.'),
(81328, 2018, 'bio', 'Genome Context Viewer: visual exploration of multiple annotated genomes using microsynteny', 'Cleary, Alan|Farmer, Andrew', 'Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA.|National Center for Genome Resources, Santa Fe, NM 87505, USA.', 'United States|United States', 0, 'Data Mining|Data Visualization|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx757', '29194466', 'The Genome Context Viewer is a visual data-mining tool that allows users to search across multiple providers of genome data for regions with similarly annotated content that may be aligned and visualized at the level of their shared functional elements. By handling ordered sequences of gene family memberships as a unit of search and comparison, the user interface enables quick and intuitive assessment of the degree of gene content divergence and the presence of various types of structural events within syntenic contexts. Insights into functionally significant differences seen at this level of abstraction can then serve to direct the user to more detailed explorations of the underlying data in other interconnected, provider-specific tools.'),
(81329, 2018, 'bio', 'bioBakery: a metaomic analysis environment', 'McIver, Lauren J|Abu-Ali, Galeb|Franzosa, Eric A|Schwager, Randall|Morgan, Xochitl C|Waldron, Levi|Segata, Nicola|Huttenhower, Curtis', 'Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.|Graduate School of Public Health and Health Policy, City University of New York, New York, NY 10027, USA.|Centre for Integrative Biology, University of Trento, Trento 38123, Italy.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States|Italy|United States', 27, 'Metagenomics|Microbiota|Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx754', '29194469', 'bioBakery is a metaomic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility.'),
(81330, 2018, 'bio', 'iDINGO-integrative differential network analysis in genomics with Shiny application', 'Class, Caleb A|Ha, Min Jin|Baladandayuthapani, Veerabhadran|Do, Kim-Anh', 'Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|Disease Progression|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btx750', '29194470', 'Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple omics data independently does not account for the hierarchical structure of the data.'),
(81331, 2018, 'bio', 'ThetaMater: Bayesian estimation of population size parameter xcexb8 from genomic data', 'Adams, Richard H|Schield, Drew R|Card, Daren C|Corbin, Andrew|Castoe, Todd A', 'Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.', 'United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Genome|Genomics|Population Density|Software', 'Journal Article', '10.1093/bioinformatics/btx733', '29194472', 'We describe ThetaMater, an open source R package comprising a suite of functions for efficient and scalable Bayesian estimation of the population size parameter xcexb8 from genomic data.'),
(81332, 2018, 'bio', 'Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies', 'Derkach, Andriy|Zhang, Haoyu|Chatterjee, Nilanjan', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.|Department of Biostatistics, Bloomberg School of Public Health, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.|Department of Biostatistics, Bloomberg School of Public Health, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.', 'United States|United States|United States', 6, 'Gene Frequency|Genetic Loci|Genetic Variation|Genetics, Population|Genome-Wide Association Study|Humans|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btx770', '29194474', 'Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus.'),
(81333, 2018, 'bio', 'RecoverY: k-mer-based read classification for Y-chromosome-specific sequencing and assembly', 'Rangavittal, Samarth|Harris, Robert S|Cechova, Monika|Tomaszkiewicz, Marta|Chikhi, Rayan|Makova, Kateryna D|Medvedev, Paul', 'Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.|Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.|Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.|Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.|CNRS, CRIStAL, 59655 Villeneuve dAscq, France.|Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.|Department of Computer Science and Engineering.', 'United States|United States|United States|United States|France|United States|United States', 0, 'Algorithms|Animals|Chromosomes, Mammalian|Genomics|Gorilla gorilla|High-Throughput Nucleotide Sequencing|Humans|Male|Mammals|Sequence Analysis, DNA|Software|Y Chromosome', 'Journal Article', '10.1093/bioinformatics/btx771', '29194476', 'The haploid mammalian Y chromosome is usually under-represented in genome assemblies due to high repeat content and low depth due to its haploid nature. One strategy to ameliorate the low coverage of Y sequences is to experimentally enrich Y-specific material before assembly. As the enrichment process is imperfect, algorithms are needed to identify putative Y-specific reads prior to downstream assembly. A strategy that uses k-mer abundances to identify such reads was used to assemble the gorilla Y. However, the strategy required the manual setting of key parameters, a time-consuming process leading to sub-optimal assemblies.'),
(81334, 2018, 'bio', 'MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action', 'Nelson, Justin|Simpkins, Scott W|Safizadeh, Hamid|Li, Sheena C|Piotrowski, Jeff S|Hirano, Hiroyuki|Yashiroda, Yoko|Osada, Hiroyuki|Yoshida, Minoru|Boone, Charles|Myers, Chad L', 'Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.|Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.|Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|Yumanity Therapeutics, Cambridge, MA 02139, USA.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.|Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.', 'United States|United States|United States|Japan|United States|Japan|Japan|Japan|Japan|Japan|United States', 6, 'Computational Biology|Databases, Factual|Drug Discovery|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Gene-Environment Interaction|Internet|Models, Genetic|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btx732', '29206899', 'Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81335, 2018, 'bio', 'GenSSI 20: multi-experiment structural identifiability analysis of SBML models', 'Ligon, Thomas S|Fröhlich, Fabian|Chis, Oana T|Banga, Julio R|Balsa-Canto, Eva|Hasenauer, Jan', 'Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 München, Germany.|Technological Institute for Industrial Mathematics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.|(Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain.|(Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 München, Germany.', 'Germany|Germany|Spain|Spain|Spain|Germany', 0, 'Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx735', '29206901', 'Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed.'),
(81336, 2018, 'bio', 'Identifying functionally informative evolutionary sequence profiles', 'Gil, Nelson|Fiser, Andras', 'Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.', 'United States|United States', 0, 'Algorithms|Biological Evolution|Escherichia coli|Humans|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx779', '29211823', 'Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually.'),
(81337, 2018, 'bio', 'Bicycle: a bioinformatics pipeline to analyze bisulfite sequencing data', 'Graña, Osvaldo|López-Fernández, Hugo|Fdez-Riverola, Florentino|González Pisano, David|Glez-Peña, Daniel', 'Bioinformatics Unit, Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Departamento de Informática. ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, Universidade de Vigo, 32004 Ourense, Spain.|Departamento de Informática. ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, Universidade de Vigo, 32004 Ourense, Spain.|Bioinformatics Unit, Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Departamento de Informática. ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, Universidade de Vigo, 32004 Ourense, Spain.', 'Spain|Spain|Spain|Spain|Spain', 8, 'Computational Biology|DNA Methylation|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btx778', '29211825', 'High-throughput sequencing of bisulfite-converted DNA is a technique used to measure DNA methylation levels. Although a considerable number of computational pipelines have been developed to analyze such data, none of them tackles all the peculiarities of the analysis together, revealing limitations that can force the user to manually perform additional steps needed for a complete processing of the data. This article presents bicycle, an integrated, flexible analysis pipeline for bisulfite sequencing data. Bicycle analyzes whole genome bisulfite sequencing data, targeted bisulfite sequencing data and hydroxymethylation data. To show how bicycle overtakes other available pipelines, we compared them on a defined number of features that are summarized in a table. We also tested bicycle with both simulated and real datasets, to show its level of performance, and compared it to different state-of-the-art methylation analysis pipelines.'),
(81338, 2018, 'bio', 'ASAR: visual analysis of metagenomes in R', 'Orakov, Askarbek N|Sakenova, Nazgul K|Sorokin, Anatoly|Goryanin, Igor I', 'Biological Systems Unit, Okinawa Institute of Science and Technology, Onna-son 904-0412, Japan.|Biological Systems Unit, Okinawa Institute of Science and Technology, Onna-son 904-0412, Japan.|Mechanism of Cell Genome Functioning Laboratory, Institute of Cell Biophysics RAS, Pushchino 142290, Russia.|Biological Systems Unit, Okinawa Institute of Science and Technology, Onna-son 904-0412, Japan.', 'Japan|Japan|-|Japan', 2, 'Metagenomics|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btx775', '29211828', 'Functional and taxonomic analyses are critical steps in understanding interspecific interactions within microbial communities. Currently, such analyses are run separately, which complicates interpretation of results. Here we present the ASAR interactive tool for simultaneous analysis of metagenomic data in three dimensions: taxonomy, function, metagenome.'),
(81339, 2018, 'bio', 'Model-based design of bistable cell factories for metabolic engineering', 'Srinivasan, Shyam|Cluett, William R|Mahadevan, Radhakrishnan', 'Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada.|Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada.|Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada.', 'Canada|Canada|Canada', 0, 'Kinetics|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btx769', '29220508', 'Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks.'),
(81340, 2018, 'bio', 'Integrating 5hmC and gene expression data to infer regulatory mechanisms', 'Mitrea, Cristina|Wijesinghe, Priyanga|Dyson, Greg|Kruger, Adéle|Ruden, Douglas M|Draghici, Sorin|Bollig-Fischer, Aliccia', 'Department of Oncology.|Department of Oncology.|Department of Oncology.|Department of Obstetrics and Gynecology.|Department of Obstetrics and Gynecology.|Department of Computer Science.|Department of Oncology.', '-|-|-|-|-|-|-', 2, '5-Methylcytosine|Cell Line, Tumor|Computational Biology|Epigenesis, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Triple Negative Breast Neoplasms', 'Journal Article', '10.1093/bioinformatics/btx777', '29220513', 'Epigenetic mechanisms are known to play a major role in breast cancer. However, the role of 5-hydroxymethylcytosine (5hmC) remains understudied. We hypothesize that 5hmC mediates redox regulation of gene expression in an aggressive subtype known as triple negative breast cancer (TNBC). To address this, our objective was to highlight genes that may be the target of this process by identifying redox-regulated, antioxidant-sensitive, gene-localized 5hmC changes associated with mRNA changes in TNBC cells.'),
(81341, 2018, 'bio', 'Gene Graphics: a genomic neighborhood data visualization web application', 'Harrison, Katherine J|Crécy-Lagard, Valérie de|Zallot, Rémi', 'Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA.|Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA.|Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States', 0, 'Computer Graphics|Data Visualization|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btx793', '29228171', 'The examination of gene neighborhood is an integral part of comparative genomics but no tools to produce publication quality graphics of gene clusters are available. Gene Graphics is a straightforward web application for creating such visuals. Supported inputs include National Center for Biotechnology Information gene and protein identifiers with automatic fetching of neighboring information, GenBank files and data extracted from the SEED database. Gene representations can be customized for many parameters including gene and genome names, colors and sizes. Gene attributes can be copied and pasted for rapid and user-friendly customization of homologous genes between species. In addition to Portable Network Graphics and Scalable Vector Graphics, produced representations can be exported as Tagged Image File Format or Encapsulated PostScript, formats that are standard for publication. Hands-on tutorials with real life examples inspired from publications are available for training.'),
(81342, 2018, 'bio', 'SPRING: a kinetic interface for visualizing high dimensional single-cell expression data', 'Weinreb, Caleb|Wolock, Samuel|Klein, Allon M', 'Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States', 0, 'Cluster Analysis|Computational Biology|Gene Expression Profiling|Hematopoietic Stem Cells|Humans|Respiratory Mucosa|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx792', '29228172', 'Single-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies.'),
(81343, 2018, 'bio', 'SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments', 'Hayes, Wayne B|Mamano, Nil', 'Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.|Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.', 'United States|United States', 2, 'Animals|Computational Biology|Data Accuracy|Eukaryota|Evolution, Molecular|Gene Ontology|Humans|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btx716', '29228175', 'Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolated: each pairing is dependent on every other via the alignment itself. Existing measures fail to take into account the frequency of GO terms across networks, instead imposing arbitrary rules on when to allow GO terms.'),
(81344, 2018, 'bio', 'PEITH(xcex98): perfecting experiments with information theory in Python with GPU support', 'Dony, Leander|Mackerodt, Jonas|Ward, Scott|Filippi, Sarah|Stumpf, Michael P H|Liepe, Juliane', 'Department of Life Sciences.|Department of Life Sciences.|Department of Life Sciences.|Faculty of Medicine, School of Public Health.|Department of Life Sciences.|Department of Life Sciences.', '-|-|-|-|-|-', 0, 'Bayes Theorem|Information Theory|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx776', '29228182', 'Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.'),
(81345, 2018, 'bio', 'DNCON2: improved protein contact prediction using two-level deep convolutional neural networks', 'Adhikari, Badri|Hou, Jie|Cheng, Jianlin', 'Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA.|Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA.|Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA.', 'United States|United States|United States', 46, 'Caspases|Computational Biology|Machine Learning|Neural Networks (Computer)|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx781', '29228185', 'Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction.'),
(81346, 2018, 'bio', 'Aether: leveraging linear programming for optimal cloud computing in genomics', 'Luber, Jacob M|Tierney, Braden T|Cofer, Evan M|Patel, Chirag J|Kostic, Aleksandar D', 'Section on Pathophysiology and Molecular Pharmacology.|Section on Pathophysiology and Molecular Pharmacology.|Section on Pathophysiology and Molecular Pharmacology.|Department of Biomedical Informatics.|Section on Pathophysiology and Molecular Pharmacology.', '-|-|-|-|-', 0, 'Cloud Computing|Genomics|Programming, Linear|Software', 'Journal Article', '10.1093/bioinformatics/btx787', '29228186', 'Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities.'),
(81347, 2018, 'bio', 'FROGS: Find, Rapidly, OTUs with Galaxy Solution', 'Escudié, Frédéric|Auer, Lucas|Bernard, Maria|Mariadassou, Mahendra|Cauquil, Laurent|Vidal, Katia|Maman, Sarah|Hernandez-Raquet, Guillermina|Combes, Sylvie|Pascal, Géraldine', 'Bioinformatics platform Toulouse Midi-Pyrenees, MIAT, INRA Auzeville CS 52627 31326 Castanet Tolosan cedex, France.|INRA, UMR 1136, Université de Lorraine, INRA-Nancy, 54280, Champenoux, France.|GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.|MaIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France.|GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France.|GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France.|GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France.|Laboratoire dingénierie des Systèmes Biologiques et des Procédés-LISBP, Université de Toulouse, INSA, INRA, CNRS, Toulouse, France.|GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France.|GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France.', 'France|France|France|France|France|France|France|France|France|France', 0, 'Bacteria|Cluster Analysis|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btx791', '29228191', 'Metagenomics leads to major advances in microbial ecology and biologists need user friendly tools to analyze their data on their own.'),
(81348, 2018, 'bio', 'DeepSF: deep convolutional neural network for mapping protein sequences to folds', 'Hou, Jie|Adhikari, Badri|Cheng, Jianlin', 'Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 34, 'Animals|Computational Biology|Humans|Models, Molecular|Neural Networks (Computer)|Protein Folding|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx780', '29228193', 'Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice.'),
(81349, 2018, 'bio', 'LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models', 'Soto, Axel J|Zerva, Chrysoula|Batista-Navarro, Riza|Ananiadou, Sophia', 'National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester M1 7DN, UK.|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester M1 7DN, UK.|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester M1 7DN, UK.|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester M1 7DN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Graphics|Data Mining|Publications|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx774', '29228271', 'Pathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support.'),
(81350, 2018, 'bio', 'Matrix factorization-based data fusion for the prediction of lncRNA-disease associations', 'Fu, Guangyuan|Wang, Jun|Domeniconi, Carlotta|Yu, Guoxian', 'College of Computer and Information Science, Southwest University, Chongqing 400715, China.|College of Computer and Information Science, Southwest University, Chongqing 400715, China.|Department of Computer Science, George Mason University, Farifax, VA 22030, USA.|College of Computer and Information Science, Southwest University, Chongqing 400715, China.', 'China|China|United States|China', 38, 'Algorithms|Breast Neoplasms|Computational Biology|Computer Simulation|Female|Genetic Predisposition to Disease|Humans|RNA, Long Noncoding|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btx794', '29228285', 'Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be.'),
(81351, 2018, 'bio', 'Accurate mapping of tRNA reads', 'Hoffmann, Anne|Fallmann, Jörg|Vilardo, Elisa|Mörl, Mario|Stadler, Peter F|Amman, Fabian', 'Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany.|Center for Anatomy and Cell Biology, Medical University of Vienna, Austria.|Institute for Biochemistry, Leipzig University, D-04103 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany.|Department of Theoretical Chemistry of the University of Vienna, A-1090 Vienna, Austria.', 'Germany|Germany|Austria|Germany|Germany|Austria', 0, 'Chromosome Mapping|Genome, Human|Genomics|Humans|RNA, Transfer|Repetitive Sequences, Nucleic Acid|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx756', '29228294', 'Many repetitive DNA elements are transcribed at appreciable expression levels. Mapping the corresponding RNA sequencing reads back to a reference genome is notoriously difficult and error-prone task, however. This is in particular true if chemical modifications introduce systematic mismatches, while at the same time the genomic loci are only approximately identical, as in the case of tRNAs.'),
(81352, 2018, 'bio', 'TarHunter, a tool for predicting conserved microRNA targets and target mimics in plants', 'Ma, Xuan|Liu, Chunyan|Gu, Lianfeng|Mo, Beixin|Cao, Xiaofeng|Chen, Xuemei', 'Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.|State Key Laboratory of Plant Genomics, National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.|Haixia Institute of Science and Technology (HIST), Fujian Agriculture and Forestry University, Fuzhou 350002, China.|Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.|State Key Laboratory of Plant Genomics, National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.|Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.', 'China|China|China|China|China|China', 9, 'Computational Biology|Gene Expression Regulation, Plant|MicroRNAs|Plants|RNA, Plant|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx797', '29236948', 'In plants, the targets of deeply conserved microRNAs (miRNAs) were comprehensively studied. Evidence is emerging that targets of less conserved miRNAs, endogenous target mimics (eTM) and non-canonical targets play functional roles. Existing plant miRNA prediction tools lack a cross-species conservation filter and eTM prediction function. We developed a tool named TarHunter that features a strict cross-species conservation filter and capability of predicting eTMs. TarHunter has higher recall or precision rate as compared with other tools, and the conservation filter effectively increases prediction precision. TarHunter prediction combined with degradome analysis uncovered previously neglected miRNA targets including non-canonical target sites from various plant species, which are available at the TarHunter website (http://tarhunter.genetics.ac.cn/).'),
(81353, 2018, 'bio', 'NGLview-interactive molecular graphics for Jupyter notebooks', 'Nguyen, Hai|Case, David A|Rose, Alexander S', 'Department of Chemistry and Chemical Biology, Rutgers University, New Brunswick, NJ 08854, USA.|Department of Chemistry and Chemical Biology, Rutgers University, New Brunswick, NJ 08854, USA.|RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, San Diego, CA 92093, USA.', 'United States|United States|United States', 19, 'Computational Biology|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btx789', '29236954', 'NGLview is a Jupyter/IPython widget to interactively view molecular structures as well as trajectories from molecular dynamics simulations. Fast and scalable molecular graphics are provided through the NGL Viewer. The widget supports showing data from the file-system, online data bases and from objects of many popular analysis libraries including mdanalysis, mdtraj, pytraj, rdkit and more.'),
(81354, 2018, 'bio', 'Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates', 'Norton, Scott S|Vaquero-Garcia, Jorge|Lahens, Nicholas F|Grant, Gregory R|Barash, Yoseph', 'Department of Genetics, Perelman School of Medicine.|Department of Genetics, Perelman School of Medicine.|Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Genetics, Perelman School of Medicine.|Department of Genetics, Perelman School of Medicine.', 'United States|United States|United States|United States|United States', 14, 'Algorithms|Alternative Splicing|Gene Expression Profiling|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx790', '29236961', 'A key component in many RNA-Seq-based studies is contrasting multiple replicates from different experimental conditions. In this setup, replicates play a key role as they allow to capture underlying biological variability inherent to the compared conditions, as well as experimental variability. However, what constitutes a bad replicate is not necessarily well defined. Consequently, researchers might discard valuable data or downstream analysis may be hampered by failed experiments.'),
(81355, 2018, 'bio', 'DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification', 'Chiesa, Mattia|Colombo, Gualtiero I|Piacentini, Luca', 'Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, 20138 Milan, Italy.|Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, 20138 Milan, Italy.|Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, 20138 Milan, Italy.', 'Italy|Italy|Italy', 7, 'Data Mining|Gene Expression Profiling|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx795', '29236969', 'RNA-Seq is becoming the technique of choice for high-throughput transcriptome profiling, which, besides class comparison for differential expression, promises to be an effective and powerful tool for biomarker discovery. However, a systematic analysis of high-dimensional genomic data is a demanding task for such a purpose. DaMiRseq offers an organized, flexible and convenient framework to remove noise and bias, select the most informative features and perform accurate classification.'),
(81356, 2018, 'bio', 'New algorithms to represent complex pseudoknotted RNA structures in dot-bracket notation', 'Antczak, Maciej|Popenda, Mariusz|Zok, Tomasz|Zurkowski, Michal|Adamiak, Ryszard W|Szachniuk, Marta', 'Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan 61-704, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Algorithms|Computational Biology|Databases, Factual|Humans|Models, Molecular|Nucleic Acid Conformation|RNA|RNA Folding|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx783', '29236971', 'Understanding the formation, architecture and roles of pseudoknots in RNA structures are one of the most difficult challenges in RNA computational biology and structural bioinformatics. Methods predicting pseudoknots typically perform this with poor accuracy, often despite experimental data incorporation. Existing bioinformatic approaches differ in terms of pseudoknots recognition and revealing their nature. A few ways of pseudoknot classification exist, most common ones refer to a genus or order. Following the latter one, we propose new algorithms that identify pseudoknots in RNA structure provided in BPSEQ format, determine their order and encode in dot-bracket-letter notation. The proposed encoding aims to illustrate the hierarchy of RNA folding.'),
(81357, 2018, 'bio', 'IndeCut evaluates performance of network motif discovery algorithms', 'Ansariola, Mitra|Megraw, Molly|Koslicki, David', 'Center for Genome Research and Biocomputing.|Center for Genome Research and Biocomputing.|Center for Genome Research and Biocomputing.', '-|-|-', 0, 'Algorithms|Computational Biology|Escherichia coli|Gene Regulatory Networks|Humans|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx798', '29236975', 'Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs.'),
(81358, 2018, 'bio', 'gene2drug: a computational tool for pathway-based rational drug repositioning', 'Napolitano, Francesco|Carrella, Diego|Mandriani, Barbara|Pisonero-Vaquero, Sandra|Sirci, Francesco|Medina, Diego L|Brunetti-Pierri, Nicola|di Bernardo, Diego', 'Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 14, 'Animals|Cell Line|Computational Biology|Computer Simulation|Drug Discovery|Drug Repositioning|Humans|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx800', '29236977', 'Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects.'),
(81359, 2018, 'bio', 'Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data', 'Yang, Yang|Niehaus, Katherine E|Walker, Timothy M|Iqbal, Zamin|Walker, A Sarah|Wilson, Daniel J|Peto, Tim E A|Crook, Derrick W|Smith, E Grace|Zhu, Tingting|Clifton, David A', 'Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.|Public Health England, Colindale, London, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 19, 'Antitubercular Agents|Ciprofloxacin|Ethambutol|Humans|Isoniazid|Machine Learning|Microbial Sensitivity Tests|Moxifloxacin|Mycobacterium tuberculosis|Ofloxacin|Pyrazinamide|Rifampin|Sequence Analysis, DNA|Streptomycin|Tuberculosis, Multidrug-Resistant', 'Journal Article', '10.1093/bioinformatics/btx801', '29240876', 'Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification.'),
(81360, 2018, 'bio', 'GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records', 'Tahsin, Tasnia|Weissenbacher, Davy|OConnor, Karen|Magge, Arjun|Scotch, Matthew|Gonzalez-Hernandez, Graciela', 'Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.|Institute of Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.|Institute of Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States', 4, 'Databases, Nucleic Acid|Metadata|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx799', '29240889', 'GeoBoost is a command-line software package developed to address sparse or incomplete metadata in GenBank sequence records that relate to the location of the infected host (LOIH) of viruses. Given a set of GenBank accession numbers corresponding to virus GenBank records, GeoBoost extracts, integrates and normalizes geographic information reflecting the LOIH of the viruses using integrated information from GenBank metadata and related full-text publications. In addition, to facilitate probabilistic geospatial modeling, GeoBoost assigns probability scores for each possible LOIH.'),
(81361, 2018, 'bio', 'CscoreTool: fast Hi-C compartment analysis at high resolution', 'Zheng, Xiaobin|Zheng, Yixian', 'Department of Embryology, Carnegie Institution for Science, Baltimore, MD 21218, USA.|Department of Embryology, Carnegie Institution for Science, Baltimore, MD 21218, USA.', 'United States|United States', 14, 'Chromatin|Genomics|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btx802', '29244056', 'The genome-wide chromosome conformation capture (Hi-C) has revealed that the eukaryotic genome can be partitioned into A and B compartments that have distinctive chromatin and transcription features. Current Principle Component Analyses (PCA)-based method for the A/B compartment prediction based on Hi-C data requires substantial CPU time and memory. We report the development of a method, CscoreTool, which enables fast and memory-efficient determination of A/B compartments at high resolution even in datasets with low sequencing depth.'),
(81362, 2018, 'bio', 'fMLC: fast multi-level clustering and visualization of large molecular datasets', 'Vu, D|Georgievska, S|Szoke, S|Kuzniar, A|Robert, V', 'Bioinformatics group, Westerdijk Fungal Biodiversity Institute, 3584CT Utrecht, The Netherlands.|Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands.|Bioinformatics group, Westerdijk Fungal Biodiversity Institute, 3584CT Utrecht, The Netherlands.|Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands.|Bioinformatics group, Westerdijk Fungal Biodiversity Institute, 3584CT Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Cluster Analysis|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btx810', '29253070', 'Despite successful applications of data clustering and visualization techniques in molecular sequence identification, current technologies still do not scale to large biological datasets.'),
(81363, 2018, 'bio', 'K2 and K2*: efficient alignment-free sequence similarity measurement based on Kendall statistics', 'Lin, Jie|Adjeroh, Donald A|Jiang, Bing-Hua|Jiang, Yue', 'Department of Software engineering, College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China.|Department of Computer Science & Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.|Department of Pathology, Carver College of Medicine, The University of Iowa, Iowa City, IA 52242, USA.|Department of Software engineering, College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China.', 'China|United States|United States|China', 0, 'Algorithms|Animals|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx809', '29253072', 'Alignment-free sequence comparison methods can compute the pairwise similarity between a huge number of sequences much faster than sequence-alignment based methods.'),
(81364, 2018, 'bio', 'Analyzing large scale genomic data on the cloud with Sparkhit', 'Huang, Liren|Krxc3xbcger, Jan|Sczyrba, Alexander', 'Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.', 'Germany|Germany|Germany', 4, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx808', '29253074', 'The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform.'),
(81365, 2018, 'bio', 'A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases', 'Chen, Xing|Huang, Yu-An|You, Zhu-Hong|Yan, Gui-Ying|Wang, Xue-Song', 'Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.|Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx773', '29253075', '-'),
(81366, 2018, 'bio', 'DeepSynergy: predicting anti-cancer drug synergy with Deep Learning', 'Preuer, Kristina|Lewis, Richard P I|Hochreiter, Sepp|Bender, Andreas|Bulusu, Krishna C|Klambauer, Günter', 'Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria.|Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge CB2 1EW, UK.|Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria.|Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge CB2 1EW, UK.|Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge CB2 1EW, UK.|Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria.', 'Austria|United Kingdom|Austria|United Kingdom|United Kingdom|Austria', 47, 'Antineoplastic Combined Chemotherapy Protocols|Cell Line, Tumor|Computational Biology|Deep Learning|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx806', '29253077', 'While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.'),
(81367, 2018, 'bio', 'Curatr: a web application for creating, curating and sharing a mass spectral library', 'Palmer, Andrew|Phapale, Prasad|Fay, Dominik|Alexandrov, Theodore', 'European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 3, 'Chromatography, Liquid|Metabolomics|Metadata|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btx786', '29253079', 'We have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de.'),
(81368, 2018, 'bio', 'JASPAR RESTful API: accessing JASPAR data from any programming language', 'Khan, Aziz|Mathelier, Anthony', 'Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349 Oslo, Norway.|Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349 Oslo, Norway.', 'Norway|Norway', 0, 'Databases, Factual|Programming Languages|Protein Binding|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx804', '29253085', 'JASPAR is a widely used open-access database of curated, non-redundant transcription factor binding profiles. Currently, data from JASPAR can be retrieved as flat files or by using programming language-specific interfaces. Here, we present a programming language-independent application programming interface (API) to access JASPAR data using the Representational State Transfer (REST) architecture. The REST API enables programmatic access to JASPAR by most programming languages and returns data in eight widely used formats. Several endpoints are available to access the data and an endpoint is available to infer the TF binding profile(s) likely bound by a given DNA binding domain protein sequence. Additionally, it provides an interactive browsable interface for bioinformatics tool developers.'),
(81369, 2018, 'bio', 'Delta: a new web-based 3D genome visualization and analysis platform', 'Tang, Bixia|Li, Feifei|Li, Jing|Zhao, Wenming|Zhang, Zhihua', 'CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 101300, China.|CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 101300, China.|CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 101300, China.|BIG Data Center (BIGD), Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 101300, China.|CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 101300, China.', 'China|China|China|China|China', 6, 'Chromatin|Data Visualization|Genome, Human|Genomics|Humans|Imaging, Three-Dimensional|Internet|Software|beta-Globins', 'Journal Article', '10.1093/bioinformatics/btx805', '29253110', 'Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Delta features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs. Finally, by visually comparing the 3D model of the xcexb2-globin gene locus and its annotation, we speculated a plausible transitory interaction pattern in the locus. Experimental evidence was found to support this speculation by literature survey. This served as an example of intuitive hypothesis testing with the help of Delta.'),
(81370, 2018, 'bio', 'ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions', 'King, Brendan|Farrah, Terry|Richards, Matthew A|Mundy, Michael|Simeonidis, Evangelos|Price, Nathan D', 'Institute for Systems Biology, Seattle, WA 98102, USA.|Institute for Systems Biology, Seattle, WA 98102, USA.|Institute for Systems Biology, Seattle, WA 98102, USA.|Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA.|Institute for Systems Biology, Seattle, WA 98102, USA.|Institute for Systems Biology, Seattle, WA 98102, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Genome|Likelihood Functions|Software', 'Journal Article', '10.1093/bioinformatics/btx796', '29267848', 'Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organisms genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy.'),
(81371, 2018, 'bio', 'Rcupcake: an R package for querying and analyzing biomedical data through the BD2K PIC-SURE RESTful API', 'Gutiérrez-Sacristán, Alba|Guedj, Romain|Korodi, Gabor|Stedman, Jason|Furlong, Laura I|Patel, Chirag J|Kohane, Isaac S|Avillach, Paul', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona 08003, Spain.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|Spain|United States|United States|United States', 1, 'Computational Biology|Databases, Factual|Genome, Human|Humans|Phenotype|Precision Medicine|Software', 'Journal Article', '10.1093/bioinformatics/btx788', '29267850', 'In the era of big data and precision medicine, the number of databases containing clinical, environmental, self-reported and biochemical variables is increasing exponentially. Enabling the experts to focus on their research questions rather than on computational data management, access and analysis is one of the most significant challenges nowadays.');
