INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80806, 2017, 'bio', 'GDISC: a web portal for integrative analysis of gene-drug interaction for survival in cancer', 'Spainhour, John Christian Givhan|Lim, Juho|Qiu, Peng', 'Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.|Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.|Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia', 4, 'Antineoplastic Agents|Computational Biology|Gene-Environment Interaction|Genes, Neoplasm|Humans|Neoplasms|Software|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btw830', '28453687', 'Survival analysis has been applied to The Cancer Genome Atlas (TCGA) data. Although drug exposure records are available in TCGA, existing survival analyses typically did not consider drug exposure, partly due to naming inconsistencies in the data. We have spent extensive effort to standardize the drug exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of cancer patients. Using this strategy, we integrated gene copy number data, drug exposure data and patient survival data to infer gene-drug interactions that impact survival. The collection of all analyzed gene-drug interactions in 32 cancer types are organized and presented in a searchable web-portal called gene-drug Interaction for survival in cancer (GDISC). GDISC allows biologists and clinicians to interactively explore the gene-drug interactions identified in the context of TCGA, and discover interactions associated to their favorite cancer, drug and/or gene of interest. In addition, GDISC provides the standardized drug exposure data, which is a valuable resource for developing new methods for drug-specific analysis.'),
(80807, 2017, 'bio', 'Edlib: a C/Cxe2x80x89++ library for fast, exact sequence alignment using edit distance', 'xc5xa0oxc5xa1ic, Martin|xc5xa0ikic, Mile', 'Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb, HR 10000, Croatia.|Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb, HR 10000, Croatia.', 'Croatia|Croatia', 0, 'Algorithms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw753', '28453688', 'We present Edlib, an open-source C/Cxe2x80x89++xe2x80x89library for exact pairwise sequence alignment using edit distance. We compare Edlib to other libraries and show that it is the fastest while not lacking in functionality and can also easily handle very large sequences. Being easy to use, flexible, fast and low on memory usage, we expect it to be easily adopted as a building block for future bioinformatics tools.'),
(80808, 2017, 'bio', 'RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach', 'Fukunaga, Tsukasa|Hamada, Michiaki', 'Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.|Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.', 'Japan|Japan', 25, 'Computational Biology|Humans|Molecular Sequence Annotation|RNA, Long Noncoding|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx287', '28459942', 'LncRNAs play important roles in various biological processes. Although more than 58xe2x80x89000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA-RNA interactions are difficult, computational prediction of lncRNA-RNA interactions is an indispensable technique. However, the high computational costs of existing RNA-RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.'),
(80809, 2017, 'bio', 'The spike-and-slab lasso Cox model for survival prediction and associated genes detection', 'Tang, Zaixiang|Shen, Yueping|Zhang, Xinyan|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.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Humans|Proportional Hazards Models', 'Journal Article', '10.1093/bioinformatics/btx300', '28472220', 'Large-scale molecular profiling data have offered extraordinary opportunities to improve survival prediction of cancers and other diseases and to detect disease associated genes. However, there are considerable challenges in analyzing large-scale molecular data.'),
(80810, 2017, 'bio', 'OMSim: a simulator for optical map data', 'Miclotte, Giles|Plaisance, Stxc3xa9phane|Rombauts, Stephane|Van de Peer, Yves|Audenaert, Pieter|Fostier, Jan', 'Department of Information Technology, IDLab, Ghent University-IMEC, Ghent 9052, Belgium.|Nucleomics Core, VIB, Leuven 3000, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent 9052, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent 9052, Belgium.|Department of Information Technology, IDLab, Ghent University-IMEC, Ghent 9052, Belgium.|Department of Information Technology, IDLab, Ghent University-IMEC, Ghent 9052, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Genome, Human|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx293', '28472230', 'The Bionano Genomics platform allows for the optical detection of short sequence patterns in very long DNA molecules (up to 2.5xe2x80x89Mbp). Molecules with overlapping patterns can be assembled to generate a consensus optical map of the entire genome. In turn, these optical maps can be used to validate or improve de novo genome assembly projects or to detect large-scale structural variation in genomes. Simulated optical map data can assist in the development and benchmarking of tools that operate on those data, such as alignment and assembly software. Additionally, it can help to optimize the experimental setup for a genome of interest. Such a simulator is currently not available.'),
(80811, 2017, 'bio', 'Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests', 'Le, Trang T|Simmons, W Kyle|Misaki, Masaya|Bodurka, Jerzy|White, Bill C|Savitz, Jonathan|McKinney, Brett A', 'Department of Mathematics, University of Tulsa, Tulsa, OK 74104, USA.|Laureate Institute for Brain Research, Tulsa, OK 74136, USA.|Laureate Institute for Brain Research, Tulsa, OK 74136, USA.|Laureate Institute for Brain Research, Tulsa, OK 74136, USA.|Tandy School of Computer Science, University of Tulsa, OK 74104, USA.|Laureate Institute for Brain Research, Tulsa, OK 74136, USA.|Department of Mathematics, University of Tulsa, Tulsa, OK 74104, USA.', 'United States|United States|United States|United States|United States|United States|United States', 10, 'Classification|Computational Biology|Depressive Disorder, Major|Humans|Machine Learning|Models, Biological|Privacy|Software', 'Journal Article', '10.1093/bioinformatics/btx298', '28472232', 'Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations pxe2x89xabn , these differential privacy methods are susceptible to overfitting.'),
(80812, 2017, 'bio', 'KMC 3: counting and manipulating k-mer statistics', 'Kokot, Marek|Dlugosz, Maciej|Deorowicz, Sebastian', 'Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice 44-100, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice 44-100, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice 44-100, Poland.', 'Poland|Poland|Poland', 0, 'Algorithms|Animals|Chickens|Computational Biology|Databases, Factual|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx304', '28472236', 'Counting all k -mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k -mer databases. Usefulness of the tools is shown on a few real problems.'),
(80813, 2017, 'bio', 'Accounting for tumor purity improves cancer subtype classification from DNA methylation data', 'Zhang, Weiwei|Feng, Hao|Wu, Hao|Zheng, Xiaoqi', 'Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.|Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.', 'China|United States|United States|China', 12, 'DNA Methylation|Genomics|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Precision Medicine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx303', '28472248', 'Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The tumor purity, or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.'),
(80814, 2017, 'bio', 'A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy', 'Xiong, Dapeng|Zeng, Jianyang|Gong, Haipeng', 'MOE Key Laboratory of Bioinformatics, School of Life Sciences.|Beijing Innovation Center of Structural Biology.|MOE Key Laboratory of Bioinformatics, School of Life Sciences.', '-|-|-', 20, 'Computational Biology|Databases, Protein|Machine Learning|Models, Molecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx296', '28472263', 'Residue-residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein structure prediction in practice. Despite progresses in the past decade on protein targets with abundant homologous sequences, accurate contact prediction for proteins with limited sequence information is still far from satisfaction. Methodologies for these hard targets still need further improvement.'),
(80815, 2017, 'bio', 'Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method', 'Liu, Keqin|Beck, Dominik|Thoms, Julie A I|Liu, Liang|Zhao, Weiling|Pimanda, John E|Zhou, Xiaobo', 'Department of Radiology, Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Lowy Cancer Research Centre, Prince of Wales Clinical School, University of New South Wales, Sydney, 2052, Australia.|Lowy Cancer Research Centre, Prince of Wales Clinical School, University of New South Wales, Sydney, 2052, Australia.|Department of Radiology, Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Department of Radiology, Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Lowy Cancer Research Centre, Prince of Wales Clinical School, University of New South Wales, Sydney, 2052, Australia.|Department of Radiology, Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.', 'United States|Australia|Australia|United States|United States|Australia|United States', 0, 'Aged|Gene Expression Profiling|Gene Expression Regulation|Humans|MicroRNAs|Middle Aged|Molecular Sequence Annotation|Myelodysplastic Syndromes|Neoplasms|Oligonucleotide Array Sequence Analysis|RNA, Long Noncoding|RNA, Messenger|Sequence Analysis, RNA|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx280', '28472271', 'Long non-coding RNAs (lncRNAs) have been implicated in the regulation of diverse biological functions. The number of newly identified lncRNAs has increased dramatically in recent years but their expression and function have not yet been described from most diseases. To elucidate lncRNA function in human disease, we have developed a novel network based method (NLCFA) integrating correlations between lncRNA, protein coding genes and noncoding miRNAs. We have also integrated target gene associations and protein-protein interactions and designed our model to provide information on the combined influence of mRNAs, lncRNAs and miRNAs on cellular signal transduction networks.'),
(80816, 2017, 'bio', 'modlAMP: Python for antimicrobial peptides', 'Müller, Alex T|Gabernet, Gisela|Hiss, Jan A|Schneider, Gisbert', 'Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), CH-8093 Zurich, Switzerland.|Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), CH-8093 Zurich, Switzerland.|Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), CH-8093 Zurich, Switzerland.|Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), CH-8093 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 25, 'Anti-Infective Agents|Computational Biology|Machine Learning|Peptides|Software', 'Journal Article', '10.1093/bioinformatics/btx285', '28472272', 'We have implemented the lecular esign aboratorys nti icrobial eptides package ( ), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra.'),
(80817, 2017, 'bio', 'EBT: a statistic test identifying moderate size of significant features with balanced power and precision for genome-wide rate comparisons', 'Hui, Xinjie|Hu, Yueming|Sun, Ming-An|Shu, Xingsheng|Han, Rongfei|Ge, Qinggang|Wang, Yejun', 'Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China.|Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China.|Epigenomics and Computational Biology Lab, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA.|Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China.|Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China.|Department of Critical Care Unit, Peking University Third Hospital, Beijing 100191, China.|Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China.', 'China|China|United States|China|China|China|China', 6, 'Adenocarcinoma|Adenocarcinoma of Lung|Carcinoma, Squamous Cell|Databases, Genetic|Genome, Human|Genomics|Humans|Lung Neoplasms|Machine Learning|Mutation|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx294', '28472273', 'In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decreases the statistic power.'),
(80818, 2017, 'bio', 'Reliability of algorithmic somatic copy number alteration detection from targeted capture data', 'Rieber, Nora|Bohnert, Regina|Ziehm, Ulrike|Jansen, Gunther', 'Molecular Health GmbH, Kurfürsten-Anlage 21, 69115 Heidelberg, Germany.|Molecular Health GmbH, Kurfürsten-Anlage 21, 69115 Heidelberg, Germany.|Molecular Health GmbH, Kurfürsten-Anlage 21, 69115 Heidelberg, Germany.|Molecular Health GmbH, Kurfürsten-Anlage 21, 69115 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 5, 'Algorithms|DNA Copy Number Variations|DNA, Neoplasm|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Reproducibility of Results|Whole Exome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx284', '28472276', 'Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.'),
(80819, 2017, 'bio', 'STOPGAP: a database for systematic target opportunity assessment by genetic association predictions', 'Shen, Judong|Song, Kijoung|Slater, Andrew J|Ferrero, Enrico|Nelson, Matthew R', 'Biostatistics and Research Decision Sciences, Merck Research Laboratories, Rahway, NJ, USA 07065.|Target Sciences, GSK, King of Prussia, Philadelphia, PA 27513, USA.|OmicSoft Corporation, Cary, NC 27513, USA.|Target Sciences, GSK, Stevenage, SG1 2NY UK.|Target Sciences, GSK, King of Prussia, Philadelphia, PA 27513, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Factual|Genetic Association Studies|Genetic Variation|Humans|Linkage Disequilibrium|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx274', '28472345', 'We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications.'),
(80820, 2017, 'bio', 'PopFly: the Drosophila population genomics browser', 'Hervas, Sergi|Sanz, Esteve|Casillas, Sònia|Pool, John E|Barbadilla, Antonio', 'Institut de Biotecnologia i de Biomedicina and Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.|Servei de Genòmica i Bioinformàtica, Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.|Institut de Biotecnologia i de Biomedicina and Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.|Laboratory of Genetics, University of Wisconsin, Madison, WI 53706, USA.|Institut de Biotecnologia i de Biomedicina and Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193, Spain.', 'Spain|Spain|Spain|United States|Spain', 12, 'Animals|Biological Evolution|Drosophila melanogaster|Genetic Variation|Genome, Insect|Genomics|Linkage Disequilibrium|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx301', '28472360', 'The recent compilation of over 1100 worldwide wild-derived Drosophila melanogaster genome sequences reassembled using a standardized pipeline provides a unique resource for population genomic studies (Drosophila Genome Nexus, DGN). A visual display of the estimated metrics describing genome-wide variation and selection patterns would allow gaining a global view and understanding of the evolutionary forces shaping genome variation.'),
(80821, 2017, 'bio', 'SinCHet: a MATLAB toolbox for single cell heterogeneity analysis in cancer', 'Li, Jiannong|Smalley, Inna|Schell, Michael J|Smalley, Keiran S M|Chen, Y Ann', 'Department of Biostatistics and Bioinformatics.|Departments of Tumor Biology and Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.|Department of Biostatistics and Bioinformatics.|Departments of Tumor Biology and Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.|Department of Biostatistics and Bioinformatics.', 'United States|United States|United States|United States|United States', 4, 'Epigenomics|Gene Expression Profiling|Humans|Neoplasms|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx297', '28472395', 'Single-cell technologies allow characterization of transcriptomes and epigenomes for individual cells under different conditions and provide unprecedented resolution for researchers to investigate cellular heterogeneity in cancer. The SinCHet ( gle ell erogeneity) toolbox is developed in MATLAB and has a graphical user interface (GUI) for visualization and user interaction. It analyzes both continuous (e.g. mRNA expression) and binary omics data (e.g. discretized methylation data). The toolbox does not only quantify cellular heterogeneity using S hannon P rofile (SP) at different clonal resolutions but also detects heterogeneity differences using a D statistic between two populations. It is defined as the area under the P rofile of S hannon D ifference (PSD). This flexible tool provides a default clonal resolution using the change point of PSD detected by multivariate adaptive regression splines model, it also allows user-defined clonal resolutions for further investigation. This tool provides insights into emerging or disappearing clones between conditions, and enables the prioritization of biomarkers for follow-up experiments based on heterogeneity or marker differences between and/or within cell populations.'),
(80822, 2017, 'bio', 'Exploring spatially adjacent TFBS-clustered regions with Hi-C data', 'Chen, Hebing|Jiang, Shuai|Zhang, Zhuo|Li, Hao|Lu, Yiming|Bo, Xiaochen', 'Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Binding Sites|Cell Line|DNA|Embryonic Stem Cells|Genome, Human|Genomics|Humans|Models, Genetic|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx282', '28472433', 'Transcription factor binding sites (TFBSs) are clustered in the human genome, forming the TFBS-clustered regions that regulate gene transcription, which requires dynamic chromatin configurations between promoters and distal regulatory elements. Here, we propose a regulatory model called spatially adjacent TFBS-clustered regions (SATs), in which TFBS-clustered regions are connected by spatial proximity as identified by high-resolution Hi-C data.'),
(80823, 2017, 'bio', 'twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis', 'Chiu, Anthony|Ayub, Mahmood|Dive, Caroline|Brady, Ged|Miller, Crispin J', 'Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20?4BX, UK.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20?4BX, UK.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20?4BX, UK.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20?4BX, UK.|RNA Biology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20?4BX, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'DNA, Neoplasm|Humans|Neoplasms|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btx308', '28475662', 'Droplet Digital PCR (ddPCR) is a sensitive platform used to quantify specific nucleic acid molecules amplified by polymerase chain reactions. Its sensitivity makes it particularly useful for the detection of rare mutant molecules, such as those present in a sample of circulating free tumour DNA obtained from cancer patients. ddPCR works by partitioning a sample into individual droplets for which the majority contain only zero or one target molecule. Each droplet then becomes a reaction chamber for PCR, which through the use of fluorochrome labelled probes allows the target molecules to be detected by measuring the fluorescence intensity of each droplet. The technology supports two channels, allowing, for example, mutant and wild type molecules to be detected simultaneously in the same sample. As yet, no open source software is available for the automatic gating of two channel ddPCR experiments in the case where the droplets can be grouped into four clusters. Here, we present an open source R package twoddpcr, which uses Poisson statistics to estimate the number of molecules in such two channel ddPCR data. Using the Shiny framework, an accompanying graphical user interface (GUI) is also included for the package, allowing users to adjust parameters and see the results in real-time.'),
(80824, 2017, 'bio', 'Assembling draft genomes using contiBAIT', 'ONeill, Kieran|Hills, Mark|Gottlieb, Mike|Borkowski, Matthew|Karsan, Aly|Lansdorp, Peter M', 'Michael Smith Genome Sciences Centre, BC Cancer Agency.|Terry Fox Laboratory, BC Cancer Agency.|Michael Smith Genome Sciences Centre, BC Cancer Agency.|Michael Smith Genome Sciences Centre, BC Cancer Agency.|Michael Smith Genome Sciences Centre, BC Cancer Agency.|Michael Smith Genome Sciences Centre, BC Cancer Agency.', '-|-|-|-|-|-', 3, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx281', '28475666', 'Massively parallel sequencing is now widely used, but data interpretation is only as good as the reference assembly to which it is aligned. While the number of reference assemblies has rapidly expanded, most of these remain at intermediate stages of completion, either as scaffold builds, or as chromosome builds (consisting of correctly ordered, but not necessarily correctly oriented scaffolds separated by gaps). Completion of de novo assemblies remains difficult, as regions that are repetitive or hard to sequence prevent the accumulation of larger scaffolds, and create errors such as misorientations and mislocalizations. Thus, complementary methods for determining the orientation and positioning of fragments are important for finishing assemblies. Strand-seq is a method for determining template strand inheritance in single cells, information that can be used to determine relative genomic distance and orientation between scaffolds, and find errors within them. We present contiBAIT, an R/Bioconductor package which uses Strand-seq data to repair and improve existing assemblies.'),
(80825, 2017, 'bio', 'MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud', 'Expósito, Roberto R|Veiga, Jorge|González-Domínguez, Jorge|Touriño, Juan', 'Grupo de Arquitectura de Computadores, Universidade da Coruña, Campus de A Coruña, A Coruña 15071, Spain.|Grupo de Arquitectura de Computadores, Universidade da Coruña, Campus de A Coruña, A Coruña 15071, Spain.|Grupo de Arquitectura de Computadores, Universidade da Coruña, Campus de A Coruña, A Coruña 15071, Spain.|Grupo de Arquitectura de Computadores, Universidade da Coruña, Campus de A Coruña, A Coruña 15071, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx307', '28475668', 'This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool.'),
(80826, 2017, 'bio', 'FlashPCA2: principal component analysis of Biobank-scale genotype datasets', 'Abraham, Gad|Qiu, Yixuan|Inouye, Michael', 'Centre for Systems Genomics, School of BioSciences.|Department of Statistics, Purdue University, West Lafayette, IN 47907-2066, USA.|Centre for Systems Genomics, School of BioSciences.', 'United States|United States|United States', 0, 'Genetics, Population|Genomics|Genotyping Techniques|Humans|Principal Component Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx299', '28475694', 'Principal component analysis (PCA) is a crucial step in quality control of genomic data and a common approach for understanding population genetic structure. With the advent of large genotyping studies involving hundreds of thousands of individuals, standard approaches are no longer feasible. However, when the full decomposition is not required, substantial computational savings can be made.'),
(80827, 2017, 'bio', 'An efficient graph kernel method for non-coding RNA functional prediction', 'Navarin, Nicolxc3xb2|Costa, Fabrizio', 'Department of Mathematics, University of Padova, Padova 35121, Italy.|Department of Computer Science, University of Freiburg, D-79110 Freiburg, Germany.', 'Italy|Germany', 0, 'Computational Biology|Gene Expression Regulation|Humans|Molecular Sequence Annotation|RNA, Untranslated|Sequence Analysis, RNA|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx295', '28475710', 'The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the functional characterization of specific instances remains a challenging task in genome scale settings. For this reason, automatic annotation approaches are of interest. Existing computational methods are either efficient but non-accurate or they offer increased precision, but present scalability problems.'),
(80828, 2017, 'bio', 'RankProd 20: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets', 'Del Carratore, Francesco|Jankevics, Andris|Eisinga, Rob|Heskes, Tom|Hong, Fangxin|Breitling, Rainer', 'Faculty of Science and Engineering, Manchester Institute of Biotechnology, University of Manchester, Manchester M1?7DN, UK.|Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69?3GJ, UK.|Department of Social Science Research Methods, Radboud University Nijmegen, Nijmegen 6525 GD, Netherlands.|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen 6525 EC, Netherlands.|Harvard School of Public Health, Dana-Farber Cancer Institute, Boston, MA 02115, USA.|Faculty of Science and Engineering, Manchester Institute of Biotechnology, University of Manchester, Manchester M1?7DN, UK.', 'United Kingdom|United Kingdom|Netherlands|Netherlands|United Kingdom|United Kingdom', 0, 'Gene Expression|Gene Expression Profiling|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx292', '28481966', 'The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable.'),
(80829, 2017, 'bio', 'Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta', 'Kleffner, Robert|Flatten, Jeff|Leaver-Fay, Andrew|Baker, David|Siegel, Justin B|Khatib, Firas|Cooper, Seth', 'College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.|Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Biochemistry.|Genome Center.|Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA.|College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States|United States', 21, 'Computational Biology|Models, Molecular|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software|Video Games', 'Journal Article', '10.1093/bioinformatics/btx283', '28481970', 'Foldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter.'),
(80830, 2017, 'bio', 'Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma', 'Mareschal, Sylvain|Ruminy, Philippe|Alcantara, Marion|Villenet, Céline|Figeac, Martin|Dubois, Sydney|Bertrand, Philippe|Bouzelfen, Abdelilah|Viailly, Pierre-Julien|Penther, Dominique|Tilly, Hervé|Bastard, Christian|Jardin, Fabrice', 'INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|Plate-Forme de Génomique Fonctionnelle et Structurale, Université de Lille II, 59000 Lille, France.|Plate-Forme de Génomique Fonctionnelle et Structurale, Université de Lille II, 59000 Lille, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.|INSERM U1245 Team Genomics and Biomarkers in Lymphoma and Solid Tumors, Centre Henri Becquerel, 76000 Rouen, France.', 'France|France|France|France|France|France|France|France|France|France|France|France|France', 0, 'Algorithms|Comparative Genomic Hybridization|Genomics|Humans|Lymphoma, Large B-Cell, Diffuse|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btx309', '28481978', 'Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.'),
(80831, 2017, 'bio', 'McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences', 'Tickotsky, Nili|Sagiv, Tal|Prilusky, Jaime|Shifrut, Eric|Friedman, Nir', 'Department of Immunology.|Department of Immunology.|Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Immunology.|Department of Immunology.', '-|-|Israel|-|-', 52, 'Animals|Antigens|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Mice|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx286', '28481982', 'While growing numbers of T cell receptor (TCR) repertoires are being mapped by high-throughput sequencing, existing methods do not allow for computationally connecting a given TCR sequence to its target antigen, or relating it to a specific pathology. As an alternative, a manually-curated database can relate TCR sequences with their cognate antigens and associated pathologies based on published experimental data.'),
(80832, 2017, 'bio', 'rMFilter: acceleration of long read-based structure variation calling by chimeric read filtering', 'Liu, Bo|Jiang, Tao|Yiu, S M|Li, Junyi|Wang, Yadong', 'Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|China|China|China', 0, 'Genome, Human|Genomic Structural Variation|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx279', '28482046', 'Long read sequencing technologies provide new opportunities to investigate genome structural variations (SVs) more accurately. However, the state-of-the-art SV calling pipelines are computational intensive and the applications of long reads are restricted.'),
(80833, 2017, 'bio', 'BioMake: a GNU make-compatible utility for declarative workflow management', 'Holmes, Ian H|Mungall, Christopher J', 'Department of Bioengineering, University of California, Berkeley, CA 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.', 'United States|United States', 1, 'Computational Biology|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx306', '28486579', 'The Unix make program is widely used in bioinformatics pipelines, but suffers from problems that limit its application to large analysis datasets. These include reliance on file modification times to determine whether a target is stale, lack of support for parallel execution on clusters, and restricted flexibility to extend the underlying logic program.'),
(80834, 2017, 'bio', 'PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics', 'Jemimah, Sherlyn|Yugandhar, K|Michael Gromiha, M', 'Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.', 'India|India|India', 0, 'Databases, Factual|Kinetics|Mutant Proteins|Mutation, Missense|Protein Interaction Domains and Motifs|Protein Multimerization|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx312', '28498885', 'We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data.'),
(80835, 2017, 'bio', 'MAGenTA: a Galaxy implemented tool for complete Tn-Seq analysis and data visualization', 'McCoy, Katherine Maia|Antonio, Margaret L|van Opijnen, Tim', 'Biology Department, Boston College, Chestnut Hill, MA, USA.|Biology Department, Boston College, Chestnut Hill, MA, USA.|Biology Department, Boston College, Chestnut Hill, MA, USA.', 'United States|United States|United States', 11, 'Bacteria|DNA Transposable Elements|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx320', '28498899', 'Transposon insertion sequencing (Tn-Seq) is a microbial systems-level tool, that can determine on a genome-wide scale and in high-throughput, whether a gene, or a specific genomic region, is important for fitness under a specific experimental condition.'),
(80836, 2017, 'bio', 'E2FM: an encrypted and compressed full-text index for collections of genomic sequences', 'Montecuollo, Ferdinando|Schmid, Giovannni|Tagliaferri, Roberto', 'Centro Reti, Sistemi e Servizi Informatici/CRESSI, Università degli Studi della Campania Luigi Vanvitelli, Napoli 80133, Italy.|Istituto di Calcolo e Reti ad Alte Prestazioni/ICAR, Consiglio Nazionale delle Ricerche, Napoli 80131, Italy.|Dipartimento di Scienze Aziendali - Management & Innovation Systems/DISA-MIS, Università di Salerno, Fisciano 84084, Italy.', 'Italy|Italy|Italy', 1, 'Algorithms|Chromosomes, Human, Pair 11|Databases, Nucleic Acid|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx313', '28498928', 'Next Generation Sequencing (NGS) platforms and, more generally, high-throughput technologies are giving rise to an exponential growth in the size of nucleotide sequence databases. Moreover, many emerging applications of nucleotide datasets-as those related to personalized medicine-require the compliance with regulations about the storage and processing of sensitive data.'),
(80837, 2017, 'bio', 'IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies', 'Dai, Mingwei|Ming, Jingsi|Cai, Mingxuan|Liu, Jin|Yang, Can|Wan, Xiang|Xu, Zongben', 'School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|Department of Mathematics, Hong Kong Baptist University, Hong Kong.|Department of Mathematics, Hong Kong Baptist University, Hong Kong.|Centre of Quantitative Medicine, Duke-NUS Medical School, Singapore.|Department of Mathematics, Hong Kong Baptist University, Hong Kong.|Department of Computer Science, Hong Kong Baptist University, Hong Kong.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.', 'China|Hong Kong|Hong Kong|Singapore|Hong Kong|Hong Kong|China', 5, 'Algorithms|Genome-Wide Association Study|Humans|Models, Statistical|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btx314', '28498950', 'Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as polygenicity. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question.'),
(80838, 2017, 'bio', 'OMTools: a software package for visualizing and processing optical mapping data', 'Leung, Alden King-Yung|Jin, Nana|Yip, Kevin Y|Chan, Ting-Fung', 'School of Life Sciences.|School of Life Sciences.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.|School of Life Sciences.', '-|-|China|-', 8, 'Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx317', '28505226', 'Optical mapping is a molecular technique capturing specific patterns of fluorescent labels along DNA molecules. It has been widely applied in assisted-scaffolding in sequence assemblies, microbial strain typing and detection of structural variations. Various computational methods have been developed to analyze optical mapping data. However, existing tools for processing and visualizing optical map data still have many shortcomings. Here, we present OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses.'),
(80839, 2017, 'bio', 'Single-cell regulome data analysis by SCRAT', 'Ji, Zhicheng|Zhou, Weiqiang|Ji, Hongkai', 'Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.', 'United States|United States|United States', 13, 'Animals|Computational Biology|DNA|Embryonic Stem Cells|Gene Expression Regulation|Humans|Mice|Promoter Regions, Genetic|Single-Cell Analysis|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx315', '28505247', 'Emerging single-cell technologies (e.g. single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Single-cell regulome data are highly sparse and discrete. Analyzing such data is challenging. User-friendly software tools are still lacking. We present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g. gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80840, 2017, 'bio', 'False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing', 'Xiao, Jian|Cao, Hongyuan|Chen, Jun', 'Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, U.S.A.|Department of Statistics, University of Missouri, Columbia, MO, USA.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, U.S.A.', 'United States|United States|United States', 20, 'Bacteria|Genomics|High-Throughput Nucleotide Sequencing|Humans|Microbiota|Phylogeny|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx311', '28505251', 'Next generation sequencing technologies have enabled the study of the human microbiome through direct sequencing of microbial DNA, resulting in an enormous amount of microbiome sequencing data. One unique characteristic of microbiome data is the phylogenetic tree that relates all the bacterial species. Closely related bacterial species have a tendency to exhibit a similar relationship with the environment or disease. Thus, incorporating the phylogenetic tree information can potentially improve the detection power for microbiome-wide association studies, where hundreds or thousands of tests are conducted simultaneously to identify bacterial species associated with a phenotype of interest. Despite much progress in multiple testing procedures such as false discovery rate (FDR) control, methods that take into account the phylogenetic tree are largely limited.'),
(80841, 2017, 'bio', 'HTSvis: a web app for exploratory data analysis and visualization of arrayed high-throughput screens', 'Scheeder, Christian|Heigwer, Florian|Boutros, Michael', 'Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany.|Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany.|Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 3, 'Computational Biology|High-Throughput Screening Assays|RNA Interference|Software', 'Journal Article', '10.1093/bioinformatics/btx319', '28505270', 'Arrayed high-throughput screens (HTS) cover a broad range of applications using RNAi or small molecules as perturbations and specialized software packages for statistical analysis have become available. However, exploratory data analysis and integration of screening results has remained challenging due to the size of the data sets and the lack of user-friendly tools for interpretation and visualization of screening results. Here we present HTSvis, a web application to interactively visualize raw data, perform quality control and assess screening results from single to multi-channel measurements such as image-based screens. Per well aggregated raw and analyzed data of various assay types and scales can be loaded in a generic tabular format.'),
(80842, 2017, 'bio', 'PIXiE: an algorithm for automated ion mobility arrival time extraction and collision cross section calculation using global data association', 'Ma, Jian|Casey, Cameron P|Zheng, Xueyun|Ibrahim, Yehia M|Wilkins, Christopher S|Renslow, Ryan S|Thomas, Dennis G|Payne, Samuel H|Monroe, Matthew E|Smith, Richard D|Teeguarden, Justin G|Baker, Erin S|Metz, Thomas O', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, 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', 5, 'Algorithms|Computational Biology|Mass Spectrometry|Software', 'Journal Article', '10.1093/bioinformatics/btx305', '28505286', 'Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses.'),
(80843, 2017, 'bio', 'DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes', 'Pavlovic, Milos|Ray, Pradipta|Pavlovic, Kristina|Kotamarti, Aaron|Chen, Min|Zhang, Michael Q', 'Department of Biological Sciences, Center for Systems Biology.|Department of Biological Sciences, Center for Systems Biology.|Department of Biological Sciences, Center for Systems Biology.|Department of Biological Sciences, Center for Systems Biology.|Department of Clinical Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Biological Sciences, Center for Systems Biology.', 'United States|United States|United States|United States|United States|United States', 0, '5-Methylcytosine|Algorithms|Animals|CpG Islands|DNA|DNA Methylation|Epigenesis, Genetic|Humans|Machine Learning|Mammals|Software', 'Journal Article', '10.1093/bioinformatics/btx316', '28505334', '5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps.'),
(80844, 2017, 'bio', 'Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data', 'Shi, Qianqian|Zhang, Chuanchao|Peng, Minrui|Yu, Xiangtian|Zeng, Tao|Liu, Juan|Chen, Luonan', 'Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.|State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.', 'China|China|China|China|China|China|China', 16, 'Algorithms|Cluster Analysis|Computational Biology|Humans|Neoplasms|Reproducibility of Results|Signal-To-Noise Ratio|Software', 'Journal Article', '10.1093/bioinformatics/btx176', '28520848', 'Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi-view manner. One key for such an integration is to extract intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called pattern fusion analysis (PFA), which performs automated information alignment and bias correction, to fuse local sample-patterns (e.g. from each data type) into a global sample-pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample-patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data.'),
(80845, 2017, 'bio', 'The neXtProt peptide uniqueness checker: a tool for the proteomics community', 'Schaeffer, Mathieu|Gateau, Alain|Teixeira, Daniel|Michel, Pierre-André|Zahn-Zabal, Monique|Lane, Lydie', 'Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland.|CALIPHO Group, SIB Swiss Institute of Bioinformatics, CMU, 1211 Geneva 4, Switzerland.|CALIPHO Group, SIB Swiss Institute of Bioinformatics, CMU, 1211 Geneva 4, Switzerland.|CALIPHO Group, SIB Swiss Institute of Bioinformatics, CMU, 1211 Geneva 4, Switzerland.|CALIPHO Group, SIB Swiss Institute of Bioinformatics, CMU, 1211 Geneva 4, Switzerland.|Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 30, 'Databases, Protein|Humans|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx318', '28520855', 'The neXtProt peptide uniqueness checker allows scientists to define which peptides can be used to validate the existence of human proteins, i.e. map uniquely versus multiply to human protein sequences taking into account isobaric substitutions, alternative splicing and single amino acid variants.'),
(80846, 2017, 'bio', 'Accelerating high-dimensional clustering with lossless data reduction', 'Qaqish, Bahjat F|OBrien, Jonathon J|Hibbard, Jonathan C|Clowers, Katie J', 'Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|DNA Methylation|Fungal Proteins|Gene Expression Regulation, Fungal|Humans|Proteomics|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx328', '28520900', 'For cluster analysis, high-dimensional data are associated with instability, decreased classification accuracy and high-computational burden. The latter challenge can be eliminated as a serious concern. For applications where dimension reduction techniques are not implemented, we propose a temporary transformation which accelerates computations with no loss of information. The algorithm can be applied for any statistical procedure depending only on Euclidean distances and can be implemented sequentially to enable analyses of data that would otherwise exceed memory limitations.'),
(80847, 2017, 'bio', 'PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization', 'Kreft, Lukasz|Botzki, Alexander|Coppens, Frederik|Vandepoele, Klaas|Van Bel, Michiel', 'VIB Bioinformatics Core, VIB, 9052 Ghent, Belgium.|VIB Bioinformatics Core, VIB, 9052 Ghent, Belgium.|Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.|Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.|Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 32, 'Genomics|Internet|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx324', '28525531', 'Comparative and evolutionary studies utilize phylogenetic trees to analyze and visualize biological data. Recently, several web-based tools for the display, manipulation and annotation of phylogenetic trees, such as iTOL and Evolview, have released updates to be compatible with the latest web technologies. While those web tools operate an open server access model with a multitude of registered users, a feature-rich open source solution using current web technologies is not available.'),
(80848, 2017, 'bio', 'chimeraviz: a tool for visualizing chimeric RNA', 'Lågstad, Stian|Zhao, Sen|Hoff, Andreas M|Johannessen, Bjarne|Lingjærde, Ole Christian|Skotheim, Rolf I', 'Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.|Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.|Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.|Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.|Department of Informatics, Faculty of Mathematics and Natural Sciences.|Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.', 'Norway|Norway|Norway|Norway|-|Norway', 9, 'Cell Line, Tumor|High-Throughput Nucleotide Sequencing|Humans|RNA Splicing|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx329', '28525538', 'Advances in high-throughput RNA sequencing have enabled more efficient detection of fusion transcripts, but the technology and associated software used for fusion detection from sequencing data often yield a high false discovery rate. Good prioritization of the results is important, and this can be helped by a visualization framework that automatically integrates RNA data with known genomic features. Here we present chimeraviz , a Bioconductor package that automates the creation of chimeric RNA visualizations. The package supports input from nine different fusion-finder tools: deFuse, EricScript, InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION.'),
(80849, 2017, 'bio', 'On patterns and re-use in bioinformatics databases', 'Bell, Michael J|Lord, Phillip', 'School of Computing Science, Newcastle University, Newcastle-upon-Tyne, UK.|School of Computing Science, Newcastle University, Newcastle-upon-Tyne, UK.', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Linguistics|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btx310', '28525546', 'As the quantity of data being depositing into biological databases continues to increase, it becomes ever more vital to develop methods that enable us to understand this data and ensure that the knowledge is correct. It is widely-held that data percolates between different databases, which causes particular concerns for data correctness, if this percolation occurs, incorrect data in one database may eventually affect many others while, conversely, corrections in one database may fail to percolate to others. In this paper, we test this widely-held belief by directly looking for sentence reuse both within and between databases. Further, we investigate patterns of how sentences are reused over time. Finally, we consider the limitations of this form of analysis and the implications that this may have for bioinformatics database design.'),
(80850, 2017, 'bio', 'Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study', 'Städler, Nicolas|Dondelinger, Frank|Hill, Steven M|Akbani, Rehan|Lu, Yiling|Mills, Gordon B|Mukherjee, Sach', 'Global Pricing and Market Access, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland.|Lancaster Medical School, Lancaster University, Lancaster, LA1 4YQ, UK.|MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK.|Department of Bioinformatics and Computational Biology.|Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany.', 'Switzerland|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Germany', 0, 'Cluster Analysis|Computational Biology|Female|Humans|Neoplasm Proteins|Neoplasms|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btx322', '28535188', 'Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging. Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset.'),
(80851, 2017, 'bio', 'Predicting accurate contacts in thousands of Pfam domain families using PconsC3', 'Michel, Mirco|Skwark, Marcin J|Menéndez Hurtado, David|Ekeberg, Magnus|Elofsson, Arne', 'Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.|Department of Chemistry, Vanderbilt University, Nashville, TN, USA.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.|School of Computer Science and Communication, Royal Institute of Technology, Stockholm 10691, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.', 'Sweden|United States|Sweden|Sweden|Sweden', 0, 'Computational Biology|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx332', '28535189', 'A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it is possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for such contact prediction methods.'),
(80852, 2017, 'bio', 'MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive', 'Bernstein, Matthew N|Doan, AnHai|Dewey, Colin N', 'Department of Computer Sciences.|Department of Computer Sciences.|Department of Computer Sciences.', '-|-|-', 0, 'Biological Ontologies|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Metadata|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btx334', '28535296', 'The NCBIs Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate, however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with each sample. The rules governing submissions to the SRA do not dictate a standardized set of terms that should be used to describe the biological samples from which the sequencing data are derived. As a result, the metadata include many synonyms, spelling variants and references to outside sources of information. Furthermore, manual annotation of the data remains intractable due to the large number of samples in the archive. For these reasons, it has been difficult to perform large-scale analyses that study the relationships between biomolecular processes and phenotype across diverse diseases, tissues and cell types present in the SRA.'),
(80853, 2017, 'bio', 'DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding', 'Ma, Wenxiu|Yang, Lin|Rohs, Remo|Noble, William Stafford', 'Department of Statistics, University of California Riverside, Riverside, CA 92521, USA.|Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.|Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States', 8, 'Binding Sites|DNA|DNA-Binding Proteins|Genomics|Nucleic Acid Conformation|Nucleotide Motifs|Protein Array Analysis|Protein Binding|Sequence Analysis, DNA|Software|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx336', '28541376', 'Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites.'),
(80854, 2017, 'bio', 'ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data', 'Gardeux, Vincent|David, Fabrice P A|Shajkofci, Adrian|Schwalie, Petra C|Deplancke, Bart', 'Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Animals|Computer Graphics|Gene Expression Profiling|Internet|Mice|Sequence Analysis, RNA|Single-Cell Analysis|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx337', '28541377', 'Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets.'),
(80855, 2017, 'bio', 'Flexbar 30 - SIMD and multicore parallelization', 'Roehr, Johannes T|Dieterich, Christoph|Reinert, Knut', 'Institute of Bioinformatics, Department of Mathematics and Computer Science, FU Berlin, 14195 Berlin, Germany.|Klaus Tschira Institute for Integrative Computational Cardiology, Department of Internal Medicine III, University of Heidelberg, 69120 Heidelberg, Germany.|Institute of Bioinformatics, Department of Mathematics and Computer Science, FU Berlin, 14195 Berlin, Germany.', 'Germany|Germany|Germany', 0, 'Animals|Caenorhabditis elegans|Genome, Helminth|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx330', '28541403', 'High-throughput sequencing machines can process many samples in a single run. For Illumina systems, sequencing reads are barcoded with an additional DNA tag that is contained in the respective sequencing adapters. The recognition of barcode and adapter sequences is hence commonly needed for the analysis of next-generation sequencing data. Flexbar performs demultiplexing based on barcodes and adapter trimming for such data. The massive amounts of data generated on modern sequencing machines demand that this preprocessing is done as efficiently as possible.'),
(80856, 2017, 'bio', 'M2Align: parallel multiple sequence alignment with a multi-objective metaheuristic', 'Zambrano-Vega, Cristian|Nebro, Antonio J|García-Nieto, José|Aldana-Montes, José F', 'Facultad de Ciencias de la Ingeniería, Universidad Técnica Estatal de Quevedo, Quevedo, Ecuador.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.', 'Ecuador|Spain|Spain|Spain', 5, 'Algorithms|Sequence Alignment|Software', 'Evaluation Studies', '10.1093/bioinformatics/btx338', '28541404', 'Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers.'),
(80857, 2017, 'bio', 'CRISPRcloud: a secure cloud-based pipeline for CRISPR pooled screen deconvolution', 'Jeong, Hyun-Hwan|Kim, Seon Young|Rousseaux, Maxime W C|Zoghbi, Huda Y|Liu, Zhandong', 'Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.|Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, USA.', 'United States|United States|United States|United States|United States', 0, 'Clustered Regularly Interspaced Short Palindromic Repeats|Computational Biology|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx335', '28541456', 'We present a user-friendly, cloud-based, data analysis pipeline for the deconvolution of pooled screening data. This tool, CRISPRcloud, serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. Moreover, CRISPRcloud serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. Taken together, the framework described in this study is expected to accelerate development of web-based bioinformatics tool for handling all studies which include next generation sequencing data.'),
(80858, 2017, 'bio', 'A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization', 'Li, Xiong', 'School of Software, East China Jiaotong University, Nanchang 330013, China.', 'China', 17, 'Algorithms|Bayes Theorem|Computational Biology|Entropy|Epistasis, Genetic|Genetic Heterogeneity|Models, Genetic|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btx339', '28541468', 'The existing epistasis analysis approaches have been criticized mainly for their: (i) ignoring heterogeneity during epistasis analysis, (ii) high computational costs, and (iii) volatility of performances and results. Therefore, they will not perform well in general, leading to lack of reproducibility and low power in complex disease association studies. In this work, a fast scheme is proposed to accelerate exhaustive searching based on multi-objective optimization named ESMO for concurrently analyzing heterogeneity and epistasis phenomena. In ESMO, mutual entropy and Bayesian network approaches are combined for evaluating epistatic SNP combinations. In order to be compatible with heterogeneity of complex diseases, we designed an adaptive framework based on non-dominant sort and top k selection algorithm with improved time complexity O(k*M*N) . Moreover, ESMO is accelerated by strategies such as trading space for time, calculation sharing and parallel computing. Finally, ESMO is nonparametric and model-free.'),
(80859, 2017, 'bio', 'GLANET: genomic loci annotation and enrichment tool', 'Otlu, Burçak|Firtina, Can|Keles, Sündüz|Tastan, Oznur', 'Department of Computer Engineering, Middle East Technical University, 06800, Ankara, Turkey.|Department of Computer Engineering, Bilkent University, 06800, Ankara, Turkey.|Department of Statistics, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Computer Engineering, Bilkent University, 06800, Ankara, Turkey.', 'Turkey|Turkey|United States|Turkey', 5, 'DNA|Epigenomics|Genetic Loci|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx326', '28541490', 'Genomic studies identify genomic loci representing genetic variations, transcription factor (TF) occupancy, or histone modification through next generation sequencing (NGS) technologies. Interpreting these loci requires evaluating them with known genomic and epigenomic annotations.'),
(80860, 2017, 'bio', 'CWDPRNP: a tool for cervid prion sequence analysis in program R', 'Miller, William L|Walter, W David', 'Pennsylvania Cooperative Fish and Wildlife Research Unit, Department of Ecosystem Science and Management, Intercollege Graduate Degree Program in Ecology, The Pennsylvania State University, University Park, PA 16802, USA.|U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, PA 16802, USA.', 'United States|United States', 0, 'Animals|Deer|Gene Frequency|Genotype|Polymorphism, Single Nucleotide|Prions|Sequence Analysis, DNA|Software|Wasting Disease, Chronic', 'Journal Article', '10.1093/bioinformatics/btx333', '28549074', 'Chronic wasting disease is a fatal, neurological disease caused by an infectious prion protein, which affects economically and ecologically important members of the family Cervidae. Single nucleotide polymorphisms within the prion protein gene have been linked to differential susceptibility to the disease in many species. Wildlife managers are seeking to determine the frequencies of disease-associated alleles and genotypes and delineate spatial genetic patterns. The CWDPRNP package, implemented in program R, provides a unified framework for analyzing prion protein gene variability and spatial structure.'),
(80861, 2017, 'bio', 'HEROD: a human ethnic and regional specific omics database', 'Zeng, Xian|Tao, Lin|Zhang, Peng|Qin, Chu|Chen, Shangying|He, Weidong|Tan, Ying|Xia Liu, Hong|Yang, Sheng Yong|Chen, Zhe|Jiang, Yu Yang|Chen, Yu Zong', 'The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.|The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543.|The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.|The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.|State Key Laboratory of Biotherapy, Molecular Medicine Research Center, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China.|Zhejiang Key Laboratory of Gastro-Intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou 310006, China.|The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.|The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, P. R. China.', 'China|China|Singapore|Singapore|Singapore|Singapore|China|China|China|China|China|China', 0, 'Computational Biology|Databases, Genetic|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Humans|Internet|Neoplasms|Population Groups|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx340', '28549078', 'Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data.'),
(80862, 2017, 'bio', 'Computational modeling of immune system of the fish for a more effective vaccination in aquaculture', 'Madonia, Alice|Melchiorri, Cristiano|Bonamano, Simone|Marcelli, Marco|Bulfon, Chiara|Castiglione, Filippo|Galeotti, Marco|Volpatti, Donatella|Mosca, Francesco|Tiscar, Pietro-Giorgio|Romano, Nicla', 'Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy.|Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy.|Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy.|Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy.|Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy.|Faculty of Veterinary Medicine, University of Teramo, Italy.|Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy.|Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy.|Institute of Applied Computing M.Picone, CNR, 00185, Rome, Italy.|Institute of Applied Computing M.Picone, CNR, 00185, Rome, Italy.|Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Animals|Aquaculture|Bass|Computer Simulation|Fish Diseases|Immune System|Vaccination', 'Journal Article', '10.1093/bioinformatics/btx341', '28549079', 'A computational model equipped with the main immunological features of the sea bass (Dicentrarchus labrax L.) immune system was used to predict more effective vaccination in fish. The performance of the model was evaluated by using the results of two in vivo vaccinations trials against L. anguillarum and P. damselae.'),
(80863, 2017, 'bio', 'Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules', 'Yao, Xiaohui|Yan, Jingwen|Liu, Kefei|Kim, Sungeun|Nho, Kwangsik|Risacher, Shannon L|Greene, Casey S|Moore, Jason H|Saykin, Andrew J|Shen, Li|Alzheimer’s Disease Neuroimaging Initiative', 'Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.', 'India|India|India|India|India|India|United States|United States|India|India|India', 11, 'Aged|Aged, 80 and over|Alzheimer Disease|Amygdala|Computational Biology|Female|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Machine Learning|Male|Middle Aged|Phenotype|Polymorphism, Genetic|Positron Emission Tomography Computed Tomography|Software', 'Journal Article', '10.1093/bioinformatics/btx344', '28575147', 'Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity.'),
(80864, 2017, 'bio', 'GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping', 'Alser, Mohammed|Hassan, Hasan|Xin, Hongyi|Ergin, Oguz|Mutlu, Onur|Alkan, Can', 'Department of Computer Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey.|TOBB University of Economics & Technology, Sogutozu, Ankara, Turkey.|Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|TOBB University of Economics & Technology, Sogutozu, Ankara, Turkey.|Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland.|Department of Computer Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey.', 'Turkey|Turkey|United States|Turkey|Switzerland|Turkey', 22, 'Algorithms|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx342', '28575161', 'High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and candidate locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mappers execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms.'),
(80865, 2017, 'bio', 'MD-TASK: a software suite for analyzing molecular dynamics trajectories', 'Brown, David K|Penkler, David L|Sheik Amamuddy, Olivier|Ross, Caroline|Atilgan, Ali Rana|Atilgan, Canan|Tastan Bishop, Özlem', 'Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.|Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956, Istanbul, Turkey.|Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956, Istanbul, Turkey.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.', 'South Africa|South Africa|South Africa|South Africa|Turkey|Turkey|South Africa', 33, 'Computational Biology|Molecular Dynamics Simulation|Molecular Structure|Software', 'Journal Article', '10.1093/bioinformatics/btx349', '28575169', 'Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.'),
(80866, 2017, 'bio', 'karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data', 'Gel, Bernat|Serra, Eduard', 'Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer - Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus Can Ruti, Badalona, Spain.|Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer - Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus Can Ruti, Badalona, Spain.', 'Spain|Spain', 0, 'Computer Graphics|Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btx346', '28575171', 'Data visualization is a crucial tool for data exploration, analysis and interpretation. For the visualization of genomic data there lacks a tool to create customizable non-circular plots of whole genomes from any species.'),
(80867, 2017, 'bio', 'DeepSite: protein-binding site predictor using 3D-convolutional neural networks', 'Jiménez, J|Doerr, S|Martínez-Rosell, G|Rose, A S|De Fabritiis, G', 'Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.|Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.|Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.|San Diego Supercomputer Center, UC San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093-0505. USA.|Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.', 'Spain|Spain|Spain|United States|Spain', 0, 'Algorithms|Binding Sites|Drug Design|Machine Learning|Neural Networks (Computer)|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx350', '28575181', 'An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein.'),
(80868, 2017, 'bio', 'Structurexplor: a platform for the exploration of structural features of RNA secondary structures', 'Glouzon, Jean-Pierre Séhi|Perreault, Jean-Pierre|Wang, Shengrui', 'Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1 Canada.|RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada.|Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1 Canada.', 'Canada|Canada|Canada', 0, 'Protein Structure, Secondary|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx323', '28575203', 'Discovering function-related structural features, such as the cloverleaf shape of transfer RNA secondary structures, is essential to understand RNA function. With this aim, we have developed a platform, named Structurexplor, to facilitate the exploration of structural features in populations of RNA secondary structures. It has been designed and developed to help biologists interactively search for, evaluate and select interesting structural features that can potentially explain RNA functions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80869, 2017, 'bio', 'Morans I quantifies spatio-temporal pattern formation in neural imaging data', 'Schmal, Christoph|Myung, Jihwan|Herzel, Hanspeter|Bordyugov, Grigory', 'Institute for Theoretical Biology, Charité Universitätsmedizin and Humboldt Universität, Berlin D-10115, Germany.|Institute for Theoretical Biology, Charité Universitätsmedizin and Humboldt Universität, Berlin D-10115, Germany.|Institute for Theoretical Biology, Charité Universitätsmedizin and Humboldt Universität, Berlin D-10115, Germany.|Institute for Theoretical Biology, Charité Universitätsmedizin and Humboldt Universität, Berlin D-10115, Germany.', 'Germany|Germany|Germany|Germany', 7, 'Animals|Brain|Image Processing, Computer-Assisted|Male|Mice|Spatial Analysis|Suprachiasmatic Nucleus', 'Journal Article', '10.1093/bioinformatics/btx351', '28575207', 'Neural activities of the brain occur through the formation of spatio-temporal patterns. In recent years, macroscopic neural imaging techniques have produced a large body of data on these patterned activities, yet a numerical measure of spatio-temporal coherence has often been reduced to the global order parameter, which does not uncover the degree of spatial correlation. Here, we propose to use the spatial autocorrelation measure Morans I, which can be applied to capture dynamic signatures of spatial organization. We demonstrate the application of this technique to collective cellular circadian clock activities measured in the small network of the suprachiasmatic nucleus (SCN) in the hypothalamus.'),
(80870, 2017, 'bio', 'Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data', 'Vavoulis, Dimitrios V|Taylor, Jenny C|Schuh, Anna', 'The Nuffield Division of Clinical Laboratory Sciences.|The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN UK.|National Health Service Translational Molecular Diagnostics Centre, Oxford University Hospitals, John Radcliffe Hospital, Oxford, OX3 9DU UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Gene Expression|Genetic Variation|High-Throughput Nucleotide Sequencing|Models, Statistical|Quantitative Trait Loci|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx355', '28575251', 'The identification of genetic variants influencing gene expression (known as expression quantitative trait loci or eQTLs) is important in unravelling the genetic basis of complex traits. Detecting multiple eQTLs simultaneously in a population based on paired DNA-seq and RNA-seq assays employs two competing types of models: models which rely on appropriate transformations of RNA-seq data (and are powered by a mature mathematical theory), or count-based models, which represent digital gene expression explicitly, thus rendering such transformations unnecessary. The latter constitutes an immensely popular methodology, which is however plagued by mathematical intractability.'),
(80871, 2017, 'bio', 'Protein-Sol: a web tool for predicting protein solubility from sequence', 'Hebditch, Max|Carballo-Amador, M Alejandro|Charonis, Spyros|Curtis, Robin|Warwicker, Jim', 'School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.|School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.|School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.|School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.|School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Escherichia coli Proteins|Internet|Sequence Analysis, Protein|Software|Solubility|Thioredoxins', 'Journal Article', '10.1093/bioinformatics/btx345', '28575391', 'Protein solubility is an important property in industrial and therapeutic applications. Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties.'),
(80872, 2017, 'bio', 'Model-based branching point detection in single-cell data by K-branches clustering', 'Chlis, Nikolaos K|Wolf, F Alexander|Theis, Fabian J', 'Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.', 'Germany|Germany|Germany', 4, 'Algorithms|Animals|Cell Differentiation|Cluster Analysis|Gene Expression Profiling|Humans|Mice|Models, Biological|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx325', '28582478', 'The identification of heterogeneities in cell populations by utilizing single-cell technologies such as single-cell RNA-Seq, enables inference of cellular development and lineage trees. Several methods have been proposed for such inference from high-dimensional single-cell data. They typically assign each cell to a branch in a differentiation trajectory. However, they commonly assume specific geometries such as tree-like developmental hierarchies and lack statistically sound methods to decide on the number of branching events.'),
(80873, 2017, 'bio', 'PASTASpark: multiple sequence alignment meets Big Data', 'Abuxc3xadn, Josxc3xa9 M|Pena, Tomxc3xa1s F|Pichel, Juan C', 'CiTIUS, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.|CiTIUS, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.|CiTIUS, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.', 'Spain|Spain|Spain', 4, 'Algorithms|Computational Biology|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx354', '28582480', 'One basic step in many bioinformatics analyses is the multiple sequence alignment. One of the state-of-the-art tools to perform multiple sequence alignment is PASTA (Practical Alignments using SATxc3xa9 and TrAnsitivity). PASTA supports multithreading but it is limited to process datasets on shared memory systems. In this work we introduce PASTASpark, a tool that uses the Big Data engine Apache Spark to boost the performance of the alignment phase of PASTA, which is the most expensive task in terms of time consumption.'),
(80874, 2017, 'bio', 'FunGAP: Fungal Genome Annotation Pipeline using evidence-based gene model evaluation', 'Min, Byoungnam|Grigoriev, Igor V|Choi, In-Geol', 'Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Korea.|US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.|Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Korea.', 'United States|United States|United States', 0, 'Computational Biology|Fungal Proteins|Genome, Fungal|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btx353', '28582481', 'Successful genome analysis depends on the quality of gene prediction. Although fungal genome sequencing and assembly have become trivial, its annotation procedure has not been standardized yet.'),
(80875, 2017, 'bio', 'COMPASS: the COMPletely Arbitrary Sequence Simulator', 'Low, Andrew|Rodrigue, Nicolas|Wong, Alex', 'Department of Biology, Carleton University, Ottawa, ON, Canada K1S 5B6.|Department of Biology, Carleton University, Ottawa, ON, Canada K1S 5B6.|Department of Biology, Carleton University, Ottawa, ON, Canada K1S 5B6.', 'Canada|Canada|Canada', 1, 'Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx347', '28582485', 'Simulated sequence alignments are frequently used to test bioinformatics tools, but current sequence simulators are limited to defined state spaces. Here, we present the COMPletely Arbitrary Sequence Simulator (COMPASS), which is able to simulate the evolution of absolutely any discrete state space along a tree, for any form of time-reversible model.'),
(80876, 2017, 'bio', 'JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data', 'Ji, Jiadong|He, Di|Feng, Yang|He, Yong|Xue, Fuzhong|Xie, Lei', 'Department of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China.|Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.|Department of Statistics, Columbia University, New York, NY 10027, USA.|Department of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China.|Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.|Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.', 'China|United States|United States|China|China|United States', 11, 'Breast Neoplasms|Carcinoma|Computer Simulation|Female|Gene Regulatory Networks|Humans', 'Journal Article', '10.1093/bioinformatics/btx360', '28582486', 'A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.'),
(80877, 2017, 'bio', 'HUGIn: Hi-C Unifying Genomic Interrogator', 'Martin, Joshua S|Xu, Zheng|Reiner, Alex P|Mohlke, Karen L|Sullivan, Patrick|Ren, Bing|Hu, Ming|Li, Yun', 'Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Statistics.|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, UCSD Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA 92093, USA.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA.|Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Cell Line|Chromatin|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btx359', '28582503', 'High throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChIA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability.'),
(80878, 2017, 'bio', 'Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank', 'Park, Sang-Jun|Lee, Jumin|Patel, Dhilon S|Ma, Hongjing|Lee, Hui Sun|Jo, Sunhwan|Im, Wonpil', 'Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.|Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.|Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.|Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.|Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.|Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA.|Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 19, 'Carbohydrates|Databases, Protein|Glycoproteins|Polysaccharides|Sugars', 'Journal Article', '10.1093/bioinformatics/btx358', '28582506', 'Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader.'),
(80879, 2017, 'bio', 'BreakPoint Surveyor: a pipeline for structural variant visualization', 'Wyczalkowski, Matthew A|Wylie, Kristine M|Cao, Song|McLellan, Michael D|Flynn, Jennifer|Huang, Mo|Ye, Kai|Fan, Xian|Chen, Ken|Wendl, Michael C|Ding, Li', 'Oncology Division, Department of Medicine.|McDonnell Genome Institute.|Oncology Division, Department of Medicine.|McDonnell Genome Institute.|Department of Genetics.|Oncology Division, Department of Medicine.|McDonnell Genome Institute.|Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA.|Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA.|Oncology Division, Department of Medicine.|Oncology Division, Department of Medicine.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Exons|Genome, Viral|Genomic Structural Variation|Genomics|Sequence Analysis, RNA|Software|Virus Integration|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx362', '28582538', 'BreakPoint Surveyor (BPS) is a computational pipeline for the discovery, characterization, and visualization of complex genomic rearrangements, such as viral genome integration, in paired-end sequence data. BPS facilitates interpretation of structural variants by merging structural variant breakpoint predictions, gene exon structure, read depth, and RNA-sequencing expression into a single comprehensive figure.'),
(80880, 2017, 'bio', 'ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design', 'Wood, Christopher W|Heal, Jack W|Thomson, Andrew R|Bartlett, Gail J|Ibarra, Amaurys xc3x81|Brady, R Leo|Sessions, Richard B|Woolfson, Derek N', 'School of Chemistry, University of Bristol, Bristol BS8xe2x80x891TS, UK.|School of Chemistry, University of Bristol, Bristol BS8?1TS, UK.|School of Chemistry, University of Bristol, Bristol BS8xe2x80x891TS, UK.|School of Chemistry, University of Bristol, Bristol BS8?1TS, UK.|School of Biochemistry, University of Bristol, Bristol BS8?1TD, UK.|School of Biochemistry, University of Bristol, Bristol BS8?1TD, UK.|School of Biochemistry, University of Bristol, Bristol BS8xe2x80x891TD, UK.|School of Chemistry, University of Bristol, Bristol BS8xe2x80x891TS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Computer Simulation|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx352', '28582565', 'The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users.'),
(80881, 2017, 'bio', 'Integrative analysis of multiple genomic variables using a hierarchical Bayesian model', 'Schäfer, Martin|Klein, Hans-Ulrich|Schwender, Holger', 'Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany.|Program in Translational Neuropsychiatric Genomics, Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Womens Hospital, Boston, MA 02115, USA.|Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany.', 'Germany|United States|Germany', 4, 'Algorithms|Animals|Bayes Theorem|Chromatin Immunoprecipitation|Epigenesis, Genetic|Gene Expression Profiling|Gene Expression Regulation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|Models, Statistical|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx356', '28582573', 'Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels.'),
(80882, 2017, 'bio', 'Phantom: investigating heterogeneous gene sets in time-course data', 'Gu, Jinghua|Wang, Xuan|Chan, Jinyan|Baldwin, Nicole E|Turner, Jacob A', 'Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA.|Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA.|Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA.|Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA.|Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Gene Expression Regulation|Humans|Influenza, Human|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btx348', '28595310', 'Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.'),
(80883, 2017, 'bio', 'DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates', 'Peng, Hao|Yang, Yifan|Zhe, Shandian|Wang, Jian|Gribskov, Michael|Qi, Yuan', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Eli Lilly and Company, Indianapolis, IN 46285, USA.|Department of Computer Science.|Department of Computer Science.', 'United States|United States|United States|India|United States|United States', 0, 'Bayes Theorem|Carcinoma, Hepatocellular|Computer Simulation|Gene Expression Profiling|Humans|Liver Neoplasms|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx357', '28595376', 'High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.'),
(80884, 2017, 'bio', 'Comparison of pre-processing methods for Infinium HumanMethylation450 BeadChip array', 'Shiah, Yu-Jia|Fraser, Michael|Bristow, Robert G|Boutros, Paul C', 'Informatics, Ontario Institute for Cancer Research.|Princess Margaret Cancer Centre, University Health Network.|Princess Margaret Cancer Centre, University Health Network.|Informatics, Ontario Institute for Cancer Research.', '-|-|-|-', 3, 'Adenocarcinoma|CpG Islands|DNA Methylation|Genome, Human|Genomics|Humans|Male|Oligonucleotide Array Sequence Analysis|Polymorphism, Genetic|Prostatic Neoplasms|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btx372', '28605401', 'Microarrays are widely used to quantify DNA methylation because they are economical, require only small quantities of input DNA and focus on well-characterized regions of the genome. However, pre-processing of methylation microarray data is challenging because of confounding factors that include background fluorescence, dye bias and the impact of germline polymorphisms. Therefore, we present valuable insights and a framework for those seeking the most optimal pre-processing method through a data-driven approach.'),
(80885, 2017, 'bio', 'Sparse redundancy analysis of high-dimensional genetic and genomic data', 'Csala, Attila|Voorbraak, Frans P J M|Zwinderman, Aeilko H|Hof, Michel H', 'Department of Clinical Epidemiology, Biostatistics and Bioinformatics.|Department of Medical Informatics, Academic Medical Center, Amsterdam 1105 AZ, The Netherlands.|Department of Clinical Epidemiology, Biostatistics and Bioinformatics.|Department of Clinical Epidemiology, Biostatistics and Bioinformatics.', '-|Netherlands|-|-', 4, 'DNA Methylation|Genome, Human|Genomics|Humans|Marfan Syndrome|Reproducibility of Results|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx374', '28605402', 'Recent technological developments have enabled the possibility of genetic and genomic integrated data analysis approaches, where multiple omics datasets from various biological levels are combined and used to describe (disease) phenotypic variations. The main goal is to explain and ultimately predict phenotypic variations by understanding their genetic basis and the interaction of the associated genetic factors. Therefore, understanding the underlying genetic mechanisms of phenotypic variations is an ever increasing research interest in biomedical sciences. In many situations, we have a set of variables that can be considered to be the outcome variables and a set that can be considered to be explanatory variables. Redundancy analysis (RDA) is an analytic method to deal with this type of directionality. Unfortunately, current implementations of RDA cannot deal optimally with the high dimensionality of omics data (pxe2x89xabn). The existing theoretical framework, based on Ridge penalization, is suboptimal, since it includes all variables in the analysis. As a solution, we propose to use Elastic Net penalization in an iterative RDA framework to obtain a sparse solution.'),
(80886, 2017, 'bio', 'CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens', 'Bais, Preeti|Namburi, Sandeep|Gatti, Daniel M|Zhang, Xinyu|Chuang, Jeffrey H', 'The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.', 'United States|United States|United States|United States|United States', 23, 'Antigens, Neoplasm|Genomics|High-Throughput Nucleotide Sequencing|Histocompatibility Testing|Humans|Mutation|Peptides|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx375', '28605406', 'We present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data.'),
(80887, 2017, 'bio', 'FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool', 'Brown, Joseph|Pirrung, Meg|McCue, Lee Ann', 'Pacific Northwest National Laboratory, Richland, WA, 99352, USA.|Pacific Northwest National Laboratory, Richland, WA, 99352, USA.|Pacific Northwest National Laboratory, Richland, WA, 99352, USA.', 'United States|United States|United States', 96, '-', 'Journal Article', '10.1093/bioinformatics/btx373', '28605449', 'FQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data.'),
(80888, 2017, 'bio', 'A spectral algorithm for fast de novo layout of uncorrected long nanopore reads', 'Recanati, Antoine|Brxc3xbcls, Thomas|dAspremont, Alexandre', 'CNRS & D.I., UMR 8548, xc3x89cole Normale Supxc3xa9rieure, Paris, France.|Commissariat xc3xa0 lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Fondamentale, Genoscope.|CNRS & D.I., UMR 8548, xc3x89cole Normale Supxc3xa9rieure, Paris, France.', 'France|-|France', 2, 'Algorithms|Bacteria|Genome|High-Throughput Nucleotide Sequencing|Nanopores|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx370', '28605450', 'New long read sequencers promise to transform sequencing and genome assembly by producing reads tens of kilobases long. However, their high error rate significantly complicates assembly and requires expensive correction steps to layout the reads using standard assembly engines.'),
(80889, 2017, 'bio', 'MoDMaps3D: an interactive webtool for the quantification and 3D visualization of interrelationships in a dataset of DNA sequences', 'Karamichalis, Rallis|Kari, Lila', 'Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.|Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.', 'Canada|Canada', 4, 'Computer Graphics|DNA|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx367', '28605460', 'MoDMaps3D (Molecular Distance Maps 3D) is an alignment-free, fast, computationally lightweight webtool for computing and visualizing the interrelationships within any dataset of DNA sequences, based on pairwise comparisons between their oligomer compositions. MoDMaps3D is a general-purpose interactive webtool that is free of any requirements on sequence composition, position of the sequences in their respective genomes, presence or absence of similarity or homology, sequence length, or even sequence origin (biological or computer-generated).'),
(80890, 2017, 'bio', 'Message from the ISCB: 2017 ISCB Accomplishment by a Senior Scientist Award Given to Pavel Pevzner', 'Fogg, Christiana N|Kovats, Diane E|Berger, Bonnie', 'Freelance Science Writer, Kensington, Maryland, United States of America.|Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Mathematics, MIT, Cambridge, MA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx289', '28605489', '-'),
(80891, 2017, 'bio', 'Message from the ISCB: 2017 Outstanding Contributions to ISCB Award Given to Fran Lewitter', 'Fogg, Christiana N|Kovats, Diane E|Berger, Bonnie', 'Freelance Science Writer, Kensington, Maryland, United States of America.|Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.|Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx291', '28605497', '-'),
(80892, 2017, 'bio', 'EpiCompare: an online tool to define and explore genomic regions with tissue or cell type-specific epigenomic features', 'He, Yu|Wang, Ting', 'Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.', 'United States|United States', 7, 'Algorithms|Enhancer Elements, Genetic|Epigenomics|Genome, Human|Humans|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btx371', '28605501', 'The Human Reference Epigenome Map, generated by the Roadmap Epigenomics Consortium, contains thousands of genome-wide epigenomic datasets that describe epigenomes of a variety of different human tissue and cell types. This map has allowed investigators to obtain a much deeper and more comprehensive view of our regulatory genome, e.g. defining regulatory elements including all promoters and enhancers for a given tissue or cell type. An outstanding task is to combine and compare different epigenomes in order to identify regions with epigenomic features specific to certain types of tissues or cells, e.g. lineage-specific regulatory elements. Currently available tools do not directly address this question. This need motivated us to develop a tool that allows investigators to easily identify regions with epigenetic features unique to specific epigenomes that they choose, making detection of common regulatory elements and/or cell type-specific regulatory elements an interactive and dynamic experience.'),
(80893, 2017, 'bio', 'Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data', 'Mandric, Igor|Temate-Tiagueu, Yvette|Shcheglova, Tatiana|Al Seesi, Sahar|Zelikovsky, Alex|Mandoiu, Ion I', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Immunology Department, University of Connecticut Health Center, Farmington, CT 06030, USA.|Immunology Department, University of Connecticut Health Center, Farmington, CT 06030, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA.', 'Georgia|Georgia|United States|United States|Georgia|United States', 17, 'Computational Biology|Confidence Intervals|Gene Expression Profiling|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx365', '28605502', 'This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform.'),
(80894, 2017, 'bio', 'Message from the ISCB: 2017 ISCB Overton Prize Awarded to Christoph Bock', 'Fogg, Christiana N|Kovats, Diane E|Berger, Bonnie', 'Freelance Science Writer, Kensington, Maryland, United States of America.|Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Mathematics, MIT, Cambridge, MA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx290', '28605508', '-'),
(80895, 2017, 'bio', 'CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization', 'Xu, Taosheng|Le, Thuc Duy|Liu, Lin|Su, Ning|Wang, Rujing|Sun, Bingyu|Colaprico, Antonio|Bontempi, Gianluca|Li, Jiuyong', 'Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.|Interuniversity Institute of Bioinformatics in Brussels (IB)2.|Interuniversity Institute of Bioinformatics in Brussels (IB)2.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia.', 'China|Australia|Australia|China|China|China|-|-|Australia', 35, 'Computer Graphics|DNA Methylation|Gene Expression|Genomics|Humans|MicroRNAs|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx378', '28605519', 'Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in the Supplementary Material.'),
(80896, 2017, 'bio', 'runBNG: a software package for BioNano genomic analysis on the command line', 'Yuan, Yuxuan|Bayer, Philipp E|Lee, Huey-Tyng|Edwards, David', 'School of Biological Sciences, University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, University of Western Australia, Perth, WA, Australia.', 'Australia|Australia|Australia|Australia', 2, 'Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btx366', '28605539', 'We developed runBNG, an open-source software package which wraps BioNano genomic analysis tools into a single script that can be run on the command line. runBNG can complete analyses, including quality control of single molecule maps, optical map de novo assembly, comparisons between different optical maps, super-scaffolding and structural variation detection. Compared to existing software BioNano IrysView and the KSU scripts, the major advantages of runBNG are that the whole pipeline runs on one single platform and it has a high customizability.'),
(80897, 2017, 'bio', 'MotifHyades: expectation maximization for de novo DNA motif pair discovery on paired sequences', 'Wong, Ka-Chun', 'Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.', 'Hong Kong', 7, 'Algorithms|Cell Line|Chromatin|DNA|DNA-Binding Proteins|Enhancer Elements, Genetic|Genomics|Humans|K562 Cells|Models, Statistical|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx381', '28633280', 'In higher eukaryotes, protein-DNA binding interactions are the central activities in gene regulation. In particular, DNA motifs such as transcription factor binding sites are the key components in gene transcription. Harnessing the recently available chromatin interaction data, computational methods are desired for identifying the coupling DNA motif pairs enriched on long-range chromatin-interacting sequence pairs (e.g. promoter-enhancer pairs) systematically.'),
(80898, 2017, 'bio', 'Pheno4J: a gene to phenotype graph database', 'Mughal, Sajid|Moghul, Ismail|Yu, Jing|UKIRDC|Clark, Tristan|Gregory, David S|Pontikos, Nikolas', 'Globe View, London, EC4V 3PP, UK.|UCL Cancer Institute, University College London, London WC1E 6DD, UK.|Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Computer Science Department.|Computer Science Department.|UCL Genetics Institute, University College London, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Genetic|Genetic Variation|Humans|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btx397', '28633344', 'Efficient storage and querying of large amounts of genetic and phenotypic data is crucial to contemporary clinical genetic research. This introduces computational challenges for classical relational databases, due to the sparsity and sheer volume of the data. Our Java based solution loads annotated genetic variants and well phenotyped patients into a graph database to allow fast efficient storage and querying of large volumes of structured genetic and phenotypic data. This abstracts technical problems away and lets researchers focus on the science rather than the implementation. We have also developed an accompanying webserver with end-points to facilitate querying of the database.'),
(80899, 2017, 'bio', 'matter: an R package for rapid prototyping with larger-than-memory datasets on disk', 'Bemis, Kylie A|Vitek, Olga', 'College of Computer and Information Science, Northeastern University, Boston, MA USA 02115.|College of Computer and Information Science, Northeastern University, Boston, MA USA 02115.', '-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx392', '28633357', 'We introduce matter , an R package for direct interactions with larger-than-memory datasets, stored in an arbitrary number of files of any size. matter is primarily designed for datasets in new and rapidly evolving file formats, which may lack extensive software support. matter enables a wide variety of data exploration and manipulation steps, and is extensible to many bioinformatics applications. It supports reproducible research by minimizing the need of converting and storing data in multiple formats. We illustrate the performance of matter in conjunction with the Bioconductor package Cardinal for analysis of high-resolution, high-throughput mass spectrometry imaging experiments.'),
(80900, 2017, 'bio', 'TIminer: NGS data mining pipeline for cancer immunology and immunotherapy', 'Tappeiner, Elias|Finotello, Francesca|Charoentong, Pornpimol|Mayer, Clemens|Rieder, Dietmar|Trajanoski, Zlatko', 'Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.|Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.|Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.|Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.|Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.|Division of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 31, 'Data Mining|High-Throughput Nucleotide Sequencing|Humans|Immunogenetic Phenomena|Immunotherapy|Neoplasms|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx377', '28633385', 'Recently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity.'),
(80901, 2017, 'bio', 'kmerPyramid: an interactive visualization tool for nucleobase and k-mer frequencies', 'Kruppa, Jochen|van der Vries, Erhard|Jo, Wendy K|Postel, Alexander|Becher, Paul|Osterhaus, Albert|Jung, Klaus', 'Institute for Animal Breeding and Genetics.|Research Center for Emerging Infections and Zoonoses (RIZ).|Research Center for Emerging Infections and Zoonoses (RIZ).|Department of Infectious Diseases, Institute of Virology, University of Veterinary Medicine Hannover, Hannover, Germany.|Department of Infectious Diseases, Institute of Virology, University of Veterinary Medicine Hannover, Hannover, Germany.|Research Center for Emerging Infections and Zoonoses (RIZ).|Institute for Animal Breeding and Genetics.', '-|-|-|Germany|Germany|-|-', 0, 'Bacteria|Computer Graphics|Principal Component Analysis|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx385', '28633391', 'Bioinformatics methods often incorporate the frequency distribution of nulecobases or k-mers in DNA or RNA sequences, for example as part of metagenomic or phylogenetic analysis. Because the frequency matrix with sequences in the rows and nucleobases in the columns is multi-dimensional it is hard to visualize. We present the R-package kmerPyramid that allows to display each sequence, based on its nucleobase or k-mer distribution projected to the space of principal components, as a point within a 3-dimensional, interactive pyramid. Using the computer mouse, the user can turn the pyramids axes, zoom in and out and identify individual points. Additionally, the package provides the k-mer frequency matrices of about 2000 bacteria and 5000 virus reference sequences calculated from the NCBI RefSeq genbank. The kmerPyramid can particularly be used for visualization of intra- and inter species differences.'),
(80902, 2017, 'bio', 'The interfacial character of antibody paratopes: analysis of antibody-antigen structures', 'Nguyen, Minh N|Pradhan, Mohan R|Verma, Chandra|Zhong, Pingyu', 'Bioinformatics Institute, Singapore 138671.|Bioinformatics Institute, Singapore 138671.|Bioinformatics Institute, Singapore 138671.|Singapore Immunology Network (SIgN), Singapore 138648.', 'Singapore|Singapore|Singapore|Singapore', 15, 'Algorithms|Antigen-Antibody Complex|Binding Sites, Antibody|Computational Biology|Epitopes|Hydrogen Bonding|Water', 'Journal Article', '10.1093/bioinformatics/btx389', '28633399', 'In this study, computational methods are applied to investigate the general properties of antigen engaging residues of a paratope from a non-redundant dataset of 403 antibody-antigen complexes to dissect the contribution of hydrogen bonds, hydrophobic, van der Waals contacts and ionic interactions, as well as role of water molecules in the antigen-antibody interface. Consistent with previous reports using smaller datasets, we found that Tyr, Trp, Ser, Asn, Asp, Thr, Arg, Gly, His contribute substantially to the interactions between antibody and antigen. Furthermore, antibody-antigen interactions can be mediated by interfacial waters. However, there is no reported comprehensive analysis for a large number of structured waters that engage in higher ordered structures at the antibody-antigen interface. From our dataset, we have found the presence of interfacial waters in 242 complexes. We present evidence that suggests a compelling role of these interfacial waters in interactions of antibodies with a range of antigens differing in shape complementarity. Finally, we carry out 296xe2x80x89835 pairwise 3D structure comparisons of 771 structures of contact residues of antibodies with their interfacial water molecules from our dataset using CLICK method. A heuristic clustering algorithm is used to obtain unique structural similarities, and found to separate into 368 different clusters. These clusters are used to identify structural motifs of contact residues of antibodies for epitope binding.'),
(80903, 2017, 'bio', 'Detecting and removing multiplicative spatial bias in high-throughput screening technologies', 'Caraus, Iurie|Mazoure, Bogdan|Nadon, Robert|Makarenkov, Vladimir', 'Département dInformatique, Université du Québec à Montréal, Montréal, QC H3C-3P8, Canada.|Département dInformatique, Université du Québec à Montréal, Montréal, QC H3C-3P8, Canada.|McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A-0G1, Canada.|Département dInformatique, Université du Québec à Montréal, Montréal, QC H3C-3P8, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Bias|Biological Assay|Computational Biology|Drug Discovery|HIV Infections|High-Throughput Screening Assays|Humans|Software|Toxicology', 'Journal Article', '10.1093/bioinformatics/btx327', '28633418', 'Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias.'),
(80904, 2017, 'bio', 'rqt: an R package for gene-level meta-analysis', 'Zhbannikov, Ilya Y|Arbeev, Konstantin G|Yashin, Anatoliy I', 'Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC 27708, USA.|Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC 27708, USA.|Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States', 2, 'Alzheimer Disease|Genes|Genetic Variation|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btx395', '28633423', 'Despite recent advances of modern GWAS methods, it is still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant.'),
(80905, 2017, 'bio', 'The International Society for Computational Biology and WikiProject Computational Biology: celebrating 10xe2x80x89years of collaboration towards open access', 'ONeill, Kieran|Rai, Vivek|Kilpatrick, Alastair M', 'Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia.|Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.|MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, UK.', 'United Kingdom|India|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx388', '28633433', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80906, 2017, 'bio', 'SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria', 'Chevrette, Marc G|Aicheler, Fabian|Kohlbacher, Oliver|Currie, Cameron R|Medema, Marnix H', 'Department of Genetics.|Applied Bioinformatics, Department of Computer Science, Quantitative Biology Center and Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany.|Applied Bioinformatics, Department of Computer Science, Quantitative Biology Center and Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany.|Department of Bacteriology and J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53706, USA.|Bioinformatics Group, Wageningen University, 6708PB Wageningen, The Netherlands.', 'United States|Germany|Germany|United States|Netherlands', 0, 'Actinobacteria|Algorithms|Catalytic Domain|Computational Biology|Multigene Family|Peptide Synthases|Peptides|Sequence Analysis, Protein|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btx400', '28633438', 'Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool.'),
(80907, 2017, 'bio', 'BioQueue: a novel pipeline framework to accelerate bioinformatics analysis', 'Yao, Li|Wang, Heming|Song, Yuanyuan|Sui, Guangchao', 'College of Life Science, Northeast Forestry University, Harbin 150040, China.|School of Life Science and Technology, Shanghai Tech University, Shanghai 200031, China.|College of Life Science, Northeast Forestry University, Harbin 150040, China.|College of Life Science, Northeast Forestry University, Harbin 150040, China.', 'China|China|China|China', 2, 'Computational Biology|Documentation|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx403', '28633441', 'With the rapid development of Next-Generation Sequencing, a large amount of data is now available for bioinformatics research. Meanwhile, the presence of many pipeline frameworks makes it possible to analyse these data. However, these tools concentrate mainly on their syntax and design paradigms, and dispatch jobs based on users experience about the resources needed by the execution of a certain step in a protocol. As a result, it is difficult for these tools to maximize the potential of computing resources, and avoid errors caused by overload, such as memory overflow.'),
(80908, 2017, 'bio', 'BPP: a sequence-based algorithm for branch point prediction', 'Zhang, Qing|Fan, Xiaodan|Wang, Yejun|Sun, Ming-An|Shao, Jianlin|Guo, Dianjing', 'School of Life Sciences and the State Key Laboratory of Agrobiotechnology.|Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.|Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen 518060, China.|School of Life Sciences and the State Key Laboratory of Agrobiotechnology.|First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.|School of Life Sciences and the State Key Laboratory of Agrobiotechnology.', '-|China|China|-|China|-', 5, 'Algorithms|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx401', '28633445', 'Although high-throughput sequencing methods have been proposed to identify splicing branch points in the human genome, these methods can only detect a small fraction of the branch points subject to the sequencing depth, experimental cost and the expression level of the mRNA. An accurate computational model for branch point prediction is therefore an ongoing objective in human genome research.'),
(80909, 2017, 'bio', 'NucliTrack: an integrated nuclei tracking application', 'Cooper, Sam|Barr, Alexis R|Glen, Robert|Bakal, Chris', 'Division of Cancer Biology, The Institute of Cancer Research, London SW3xe2x80x896JB, UK.|Division of Cancer Biology, The Institute of Cancer Research, London SW3xe2x80x896JB, UK.|Department of Computational Systems Medicine, Imperial College, South Kensington Campus, London SW7xe2x80x892AZ, UK.|Division of Cancer Biology, The Institute of Cancer Research, London SW3xe2x80x896JB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Algorithms|Cell Nucleus|Cell Tracking|Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btx404', '28637183', 'Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTracks interactive, graphical interface makes it significantly more user friendly.'),
(80910, 2017, 'bio', 'PhylOligo: a package to identify contaminant or untargeted organism sequences in genome assemblies', 'Mallet, Ludovic|Bitard-Feildel, Tristan|Cerutti, Franck|Chiapello, Hélène', 'INRA UR875, Unité Mathématiques et Informatique Appliquées de Toulouse (MIAT), Auzeville, 31326 Castanet-Tolosan, France.|CNRS UMR7590, Sorbonne Universités, Université Pierre et Marie Curie - Paris 6, MNHN, IRD - IUC, Paris, France.|INRA UR875, Unité Mathématiques et Informatique Appliquées de Toulouse (MIAT), Auzeville, 31326 Castanet-Tolosan, France.|INRA UR875, Unité Mathématiques et Informatique Appliquées de Toulouse (MIAT), Auzeville, 31326 Castanet-Tolosan, France.', 'France|France|France|France', 6, 'Bacteria|Eukaryota|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx396', '28637232', 'Genome sequencing projects sometimes uncover more organisms than expected, especially for complex and/or non-model organisms. It is therefore useful to develop software to identify mix of organisms from genome sequence assemblies.'),
(80911, 2017, 'bio', 'omiXcore: a web server for prediction of protein interactions with large RNA', 'Armaos, Alexandros|Cirillo, Davide|Gaetano Tartaglia, Gian', 'Bioinformatics and Genomics, Gene Function and Evolution, Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology.|Bioinformatics and Genomics, Gene Function and Evolution, Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology.|Bioinformatics and Genomics, Gene Function and Evolution, Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology.', '-|-|-', 9, 'Binding Sites|Humans|Internet|Protein Binding|RNA|RNA-Binding Proteins|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx361', '28637296', 'Here we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites.'),
(80912, 2017, 'bio', 'EDEN: evolutionary dynamics within environments', 'Münch, Philipp C|Stecher, Bärbel|McHardy, Alice C', 'Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Brunswick, Germany.|Max von Pettenkofer-Institute for Hygiene and Clinical Microbiology, Ludwig-Maximilian University of Munich, 80336 Munich, Germany.|Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Brunswick, Germany.', 'Germany|Germany|Germany', 1, 'Bacteria|Biological Evolution|Metagenomics|Phenotype|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx394', '28637301', 'Metagenomics revolutionized the field of microbial ecology, giving access to Gb-sized datasets of microbial communities under natural conditions. This enables fine-grained analyses of the functions of community members, studies of their association with phenotypes and environments, as well as of their microevolution and adaptation to changing environmental conditions. However, phylogenetic methods for studying adaptation and evolutionary dynamics are not able to cope with big data. EDEN is the first software for the rapid detection of protein families and regions under positive selection, as well as their associated biological processes, from meta- and pangenome data. It provides an interactive result visualization for detailed comparative analyses.'),
(80913, 2017, 'bio', 'Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses', 'Huang, Li|Li, Xianhong|Guo, Pengfei|Yao, Yuhua|Liao, Bo|Zhang, Weiwei|Wang, Fayou|Yang, Jiasheng|Zhao, Yulong|Sun, Hailiang|He, Pingan|Yang, Jialiang', 'Department of Mathematics.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|Department of Mathematics.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Information Science and Engineering, Hunan University, Changsha 410082, China.|College of Science, East China University of Technology, Nanchang 330013, China.|School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, China.|Department of Civil and Environmental Engineering, National Universality of Singapore, Singapore 117576, Singapore.|Department of Mathematics, City University of Hong Kong, Hong Kong SAR.|College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China.|Department of Mathematics.|School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China.', '-|China|-|China|China|China|China|Singapore|Hong Kong|China|-|China', 7, 'Algorithms|Antigens, Viral|Computational Biology|Epitopes|Evolution, Molecular|Genetic Variation|Hemagglutination Inhibition Tests|Hemagglutinin Glycoproteins, Influenza Virus|Influenza A Virus, H3N2 Subtype|Influenza Vaccines|Models, Immunological|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx390', '28637337', 'Low-rank matrix completion has been demonstrated to be powerful in predicting antigenic distances among influenza viruses and vaccines from partially revealed hemagglutination inhibition table. Meanwhile, influenza hemagglutinin (HA) protein sequences are also effective in inferring antigenic distances. Thus, it is natural to integrate HA protein sequence information into low-rank matrix completion model to help infer influenza antigenicity, which is critical to influenza vaccine development.'),
(80914, 2017, 'bio', 'GfaPy: a flexible and extensible software library for handling sequence graphs in Python', 'Gonnella, Giorgio|Kurtz, Stefan', 'ZBH - Center for Bioinformatics, MIN-Fakultät, Universität Hamburg, 20146 Hamburg, Germany.|ZBH - Center for Bioinformatics, MIN-Fakultät, Universität Hamburg, 20146 Hamburg, Germany.', 'Germany|Germany', 2, 'Computer Graphics|Programming Languages|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx398', '28645150', 'GFA 1 and GFA 2 are recently defined formats for representing sequence graphs, such as assembly, variation or splicing graphs. The formats are adopted by several software tools. Here, we present GfaPy, a software package for creating, parsing and editing GFA graphs using the programming language Python. GfaPy supports GFA 1 and GFA 2, using the same interface and allows for interconversion between both formats. The software package provides a simple interface for custom record types, which is an important new feature of GFA 2 (compared to GFA 1). This enables new applications of the format.'),
(80915, 2017, 'bio', 'UpSetR: an R package for the visualization of intersecting sets and their properties', 'Conway, Jake R|Lex, Alexander|Gehlenborg, Nils', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|SCI Institute, School of Computing, University of Utah, Salt Lake City, UT 84112, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States', 289, 'Computational Biology|Genotyping Techniques|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx364', '28645171', 'Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed.'),
(80916, 2017, 'bio', 'High-speed and high-ratio referential genome compression', 'Liu, Yuansheng|Peng, Hui|Wong, Limsoon|Li, Jinyan', 'Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW 2007, Australia.|Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW 2007, Australia.|School of Computing, National University of Singapore, Singapore 117417.|Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW 2007, Australia.', 'Australia|Australia|Singapore|Australia', 12, 'Algorithms|Data Compression|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx412', '28651329', 'The rapidly increasing number of genomes generated by high-throughput sequencing platforms and assembly algorithms is accompanied by problems in data storage, compression and communication. Traditional compression algorithms are unable to meet the demand of high compression ratio due to the intrinsic challenging features of DNA sequences such as small alphabet size, frequent repeats and palindromes. Reference-based lossless compression, by which only the differences between two similar genomes are stored, is a promising approach with high compression ratio.'),
(80917, 2017, 'bio', 'TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP-SNP interactions', 'Fang, Yao-Hwei|Wang, Jie-Huei|Hsiung, Chao A', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan.', '-|-|-', 3, 'Algorithms|Arthritis, Rheumatoid|Computer Simulation|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx409', '28651334', 'Identification of single nucleotide polymorphism (SNP) interactions is an important and challenging topic in genome-wide association studies (GWAS). Many approaches have been applied to detecting whole-genome interactions. However, these approaches to interaction analysis tend to miss causal interaction effects when the individual marginal effects are uncorrelated to trait, while their interaction effects are highly associated with the trait.'),
(80918, 2017, 'bio', 'Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment', 'Domingo-Fernández, Daniel|Kodamullil, Alpha Tom|Iyappan, Anandhi|Naz, Mufassra|Emon, Mohammad Asif|Raschka, Tamara|Karki, Reagon|Springstubbe, Stephan|Ebeling, Christian|Hofmann-Apitius, Martin', 'Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 8, 'Algorithms|Humans|Internet|Knowledge Bases|Models, Biological|Neurodegenerative Diseases|Software', 'Journal Article', '10.1093/bioinformatics/btx399', '28651363', 'The concept of a mechanism-based taxonomy of human disease is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases.'),
(80919, 2017, 'bio', 'Partitioned learning of deep Boltzmann machines for SNP data', 'Hess, Moritz|Lenz, Stefan|Blätte, Tamara J|Bullinger, Lars|Binder, Harald', 'Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.|Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.|Department of Internal Medicine III, University Hospital of Ulm, 89081 Ulm, Germany.|Department of Internal Medicine III, University Hospital of Ulm, 89081 Ulm, Germany.|Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.', 'Germany|Germany|Germany|Germany|Germany', 8, 'Computational Biology|Gene Expression Regulation, Leukemic|Humans|Leukemia, Myeloid|Machine Learning|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx408', '28655145', 'Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals.'),
(80920, 2017, 'bio', 'The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics', 'Wiredja, Danica D|Koyutürk, Mehmet|Chance, Mark R', 'Center for Proteomics and Bioinformatics, Department of Nutrition, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106.|Department Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106.|Center for Proteomics and Bioinformatics, Department of Nutrition, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106.', '-|-|-', 22, '-', 'Journal Article', '10.1093/bioinformatics/btx415', '28655153', 'Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinases activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.'),
(80921, 2017, 'bio', 'CoreTracker: accurate codon reassignment prediction, applied to mitochondrial genomes', 'Noutahi, Emmanuel|Calderon, Virginie|Blanchette, Mathieu|Lang, Franz B|El-Mabrouk, Nadia', 'Département dInformatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal, QC CP 6128, Canada.|Département dInformatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal, QC CP 6128, Canada.|School of Computer Science, McGill University, McConnell Engineering Bldg., Montréal, QC H3A 0E9, Canada.|Département de Biochimie, Centre Robert Cedergren, Université de Montréal, Montréal, QC CP 6128, Canada.|Département dInformatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal, QC CP 6128, Canada.', 'Canada|Canada|Canada|Canada|Canada', 5, 'Animals|Cell Nucleus|Codon|Genetic Code|Genome, Fungal|Genome, Mitochondrial|Genomics|Phylogeny|Sequence Analysis, DNA|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx421', '28655158', 'Codon reassignments have been reported across all domains of life. With the increasing number of sequenced genomes, the development of systematic approaches for genetic code detection is essential for accurate downstream analyses. Three automated prediction tools exist so far: FACIL, GenDecoder and Bagheera, the last two respectively restricted to metazoan mitochondrial genomes and CUG reassignments in yeast nuclear genomes. These tools can only analyze a single genome at a time and are often not followed by a validation procedure, resulting in a high rate of false positives.'),
(80922, 2017, 'bio', 'Identification of transcript regulatory patterns in cell differentiation', 'Gusnanto, Arief|Gosling, John Paul|Pope, Christopher', 'Department of Statistics, University of Leeds, Leeds LS2xe2x80x899JT, UK.|Department of Statistics, University of Leeds, Leeds LS2xe2x80x899JT, UK.|Department of Statistics, University of Leeds, Leeds LS2xe2x80x899JT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Cell Differentiation|Computational Biology|Gene Expression Profiling|Humans|Leukocytes, Mononuclear|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx406', '28655167', 'Studying transcript regulatory patterns in cell differentiation is critical in understanding its complex nature of the formation and function of different cell types. This is done usually by measuring gene expression at different stages of the cell differentiation. However, if the gene expression data available are only from the mature cells, we have some challenges in identifying transcript regulatory patterns that govern the cell differentiation.'),
(80923, 2017, 'bio', 'GPU-powered model analysis with PySB/cupSODA', 'Harris, Leonard A|Nobile, Marco S|Pino, James C|Lubbock, Alexander L R|Besozzi, Daniela|Mauri, Giancarlo|Cazzaniga, Paolo|Lopez, Carlos F', 'Department of Cancer Biology.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Quantitative Systems Biology Center, Vanderbilt University, Nashville, TN 37232, USA.|Department of Cancer Biology.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|SYSBIO.IT Centre of Systems Biology, Milan, Italy.|Department of Cancer Biology.', 'United States|Italy|United States|United States|Italy|Italy|Italy|United States', 8, 'Computer Simulation|Kinetics|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btx420', '28666314', 'A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.'),
(80924, 2017, 'bio', 'Inferring transcriptional logic from multiple dynamic experiments', 'Minas, Giorgos|Jenkins, Dafyd J|Rand, David A|Finkenstädt, Bärbel', 'Mathematics Institute.|Zeeman Institute, Systems Biology and Infectious Disease Epidemiology Research.|Mathematics Institute.|Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Algorithms|Arabidopsis|Computational Biology|Data Interpretation, Statistical|Gene Expression Regulation|Gene Regulatory Networks|Logic|Markov Chains|Models, Biological|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btx407', '28666320', 'The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.'),
(80925, 2017, 'bio', 'SVM-dependent pairwise HMM: an application to protein pairwise alignments', 'Orlando, Gabriele|Raimondi, Daniele|Khan, Taushif|Lenaerts, Tom|Vranken, Wim F', 'Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.', '-|-|-|-|-', 2, 'Algorithms|Markov Chains|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx391', '28666322', 'Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.'),
(80926, 2017, 'bio', 'ALTRE: workflow for defining ALTered regulatory elements using chromatin accessibility data', 'Baskin, Elizabeth|Farouni, Rick|Mathé, Ewy A', 'Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx386', '28666331', '-'),
(80927, 2017, 'bio', 'modPhEA: model organism Phenotype Enrichment Analysis of eukaryotic gene sets', 'Weng, Meng-Pin|Liao, Ben-Yang', 'Institute of Population Health Sciences, National Health Research Institutes, Miaoli County 350, Taiwan, ROC.|Institute of Population Health Sciences, National Health Research Institutes, Miaoli County 350, Taiwan, ROC.', '-|-', 9, 'Animals|Caenorhabditis elegans|Drosophila melanogaster|Genes|Genome|Humans|Mice|Models, Animal|Mutagenesis|Phenotype|Saccharomyces cerevisiae|Software|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btx426', '28666356', 'Genome-scale phenotypic data are available for many model organisms, yet existing tools to functionally interpret gene sets from these phenotypic data are largely based on mutagenesis-derived phenotypes observed in mouse or human.'),
(80928, 2017, 'bio', 'CausalR: extracting mechanistic sense from genome scale data', 'Bradley, Glyn|Barrett, Steven J', 'Target Sciences, GSK, Medicines Research Centre, Stevenage, UK.|Target Sciences, GSK, Medicines Research Centre, Stevenage, UK.', 'United Kingdom|United Kingdom', 4, 'Computational Biology|Fibroblasts|Gene Regulatory Networks|Humans|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx425', '28666369', 'Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach.'),
(80929, 2017, 'bio', 'FALCON: a toolbox for the fast contextualization of logical networks', 'De Landtsheer, Sébastien|Trairatphisan, Panuwat|Lucarelli, Philippe|Sauter, Thomas', 'Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg.|Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg.|Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg.|Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg', 0, 'Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx380', '28673016', 'Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically.'),
(80930, 2017, 'bio', 'SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments', 'Elsts, Atis|Pentjuss, Agris|Stalidzans, Egils', 'Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8?1UB, UK.|Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.|Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.', 'United Kingdom|Latvia|Latvia', 3, 'Computational Biology|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx363', '28679158', 'Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons.'),
(80931, 2017, 'bio', 'iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals', 'Cheng, Xiang|Zhao, Shu-Guang|Xiao, Xuan|Chou, Kuo-Chen', 'Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.|Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.|Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.|Institute of Microbiology and Biotechnology, University of Latvia, Riga LV-1004, Latvia.', 'Latvia|Latvia|Latvia|Latvia', 9, '-', 'Journal Article', '10.1093/bioinformatics/btx387', '28810696', '-'),
(80932, 2017, 'bio', 'Oncopression: gene expression compendium for cancer with matched normal tissues', 'Lee, Jungsul|Choi, Chulhee', 'Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Korea.', '-|-', 8, 'Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btx121', '28881874', 'Expression profile of normal tissue is primary source to find genes showing aberrant expression pattern specific in matched cancer tissue, but sample number of normal control in public gene expression repositories is disproportionally small compared to cancer and scattered in several datasets.'),
(80933, 2017, 'bio', 'MaBoSS 20: an environment for stochastic Boolean modeling', 'Stoll, Gautier|Caron, Barthélémy|Viara, Eric|Dugourd, Aurélien|Zinovyev, Andrei|Naldi, Aurélien|Kroemer, Guido|Barillot, Emmanuel|Calzone, Laurence', 'Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France.|Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech, Paris, France.|Sysra, Yerres, France.|Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech, Paris, France.|Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech, Paris, France.|DIMNP UMR CNRS 5235, University of Montpellier, Montpellier, France.|Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France.|Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech, Paris, France.|Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech, Paris, France.', 'France|France|France|France|France|France|France|France|France', 22, 'Computational Biology|Computer Simulation|Models, Biological|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx123', '28881959', 'Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model.'),
(80934, 2017, 'bio', 'Message from the ISCB: 2017 ISCB Innovator Award Given to Aviv Regev', 'Fogg, Christiana N|Kovats, Diane E|Berger, Bonnie', 'Freelance Science Writer, Kensington, Maryland, United States of America.|Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Mathematics, MIT, Cambridge, MA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx288', '28881960', '-'),
(80935, 2017, 'bio', 'Rectified factor networks for biclustering of omics data', 'Clevert, Djork-Arné|Unterthiner, Thomas|Povysil, Gundula|Hochreiter, Sepp', 'Bioinformatics Department, Bayer AG, Berlin, Germany.|Institute of Bioinformatics, Johannes Kepler University Linz, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Linz, Austria.', 'Germany|Austria|Austria|Austria', 3, 'Computational Biology|Evolution, Molecular|Gene Expression Profiling|Genome, Human|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Unsupervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx226', '28881961', 'Biclustering has become a major tool for analyzing large datasets given as matrix of samples times features and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. actor nalysis for cluster cquisition (FABIA), one of the most successful biclustering methods, is a generative model that represents each bicluster by two sparse membership vectors: one for the samples and one for the features. However, FABIA is restricted to about 20 code units because of the high computational complexity of computing the posterior. Furthermore, code units are sometimes insufficiently decorrelated and sample membership is difficult to determine. We propose to use the recently introduced unsupervised Deep Learning approach Rectified Factor Networks (RFNs) to overcome the drawbacks of existing biclustering methods. RFNs efficiently construct very sparse, non-linear, high-dimensional representations of the input via their posterior means. RFN learning is a generalized alternating minimization algorithm based on the posterior regularization method which enforces non-negative and normalized posterior means. Each code unit represents a bicluster, where samples for which the code unit is active belong to the bicluster and features that have activating weights to the code unit belong to the bicluster.'),
(80936, 2017, 'bio', 'Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes', 'Duong, Dat|Gai, Lisa|Snir, Sagi|Kang, Eun Yong|Han, Buhm|Sul, Jae Hoon|Eskin, Eleazar', 'Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, CA, USA.|Institute of Evolution, University of Haifa, Haifa, Israel.|Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.|Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, CA, USA.', 'United States|United States|Israel|United States|United States|United States|United States', 7, 'Computational Biology|Gene Expression Profiling|Genetic Variation|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Models, Genetic|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btx227', '28881962', 'There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues.'),
(80937, 2017, 'bio', 'Deep learning with word embeddings improves biomedical named entity recognition', 'Habibi, Maryam|Weber, Leon|Neves, Mariana|Wiegandt, David Luis|Leser, Ulf', 'Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany.|Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany.|Enterprise Platform and Integration Concepts, Hasso-Plattner-Institute, Potsdam, Germany.|Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany.|Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 143, 'Animals|Data Mining|Humans|Machine Learning|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx228', '28881963', 'Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult.'),
(80938, 2017, 'bio', 'Orthologous Matrix (OMA) algorithm 20: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference', 'Train, Clément-Marie|Glover, Natasha M|Gonnet, Gaston H|Altenhoff, Adrian M|Dessimoz, Christophe', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 34, 'Algorithms|Animals|Evolution, Molecular|Genomics|Humans|Mammals|Mutation Rate|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btx229', '28881964', 'Accurate orthology inference is a fundamental step in many phylogenetics and comparative analysis. Many methods have been proposed, including OMA (Orthologous MAtrix). Yet substantial challenges remain, in particular in coping with fragmented genes or genes evolving at different rates after duplication, and in scaling to large datasets. With more and more genomes available, it is necessary to improve the scalability and robustness of orthology inference methods.'),
(80939, 2017, 'bio', 'Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms', 'Xu, Min|Chai, Xiaoqi|Muthakana, Hariank|Liang, Xiaodan|Yang, Ge|Zeev-Ben-Mordehai, Tzviya|Xing, Eric P', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Cluster Analysis|Electron Microscope Tomography|Image Processing, Computer-Assisted|Machine Learning|Molecular Structure', 'Journal Article', '10.1093/bioinformatics/btx230', '28881965', 'Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.'),
(80940, 2017, 'bio', 'DextMP: deep dive into text for predicting moonlighting proteins', 'Khan, Ishita K|Bhuiyan, Mansurul|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Department of Computer Science, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.', 'United States|India|United States', 5, 'Animals|Data Mining|Databases, Factual|Humans|Models, Biological|Molecular Sequence Annotation|Proteomics|Saccharomyces cerevisiae|Software|Unsupervised Machine Learning|Xenopus laevis', 'Journal Article', '10.1093/bioinformatics/btx231', '28881966', 'Moonlighting proteins (MPs) are an important class of proteins that perform more than one independent cellular function. MPs are gaining more attention in recent years as they are found to play important roles in various systems including disease developments. MPs also have a significant impact in computational function prediction and annotation in databases. Currently MPs are not labeled as such in biological databases even in cases where multiple distinct functions are known for the proteins. In this work, we propose a novel method named DextMP, which predicts whether a protein is a MP or not based on its textual features extracted from scientific literature and the UniProt database.'),
(80941, 2017, 'bio', 'HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddingtons epigenetic landscape', 'Guo, Jing|Zheng, Jie', 'Biomedical Informatics Laboratory, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Biomedical Informatics Laboratory, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.', 'Singapore|Singapore', 7, 'Algorithms|Animals|Epigenomics|Gene Expression Profiling|Gene Expression Regulation|Mice|Models, Genetic|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx232', '28881967', 'The interpretation of transcriptional dynamics in single-cell data, especially pseudotime estimation, could help understand the transition of gene expression profiles. The recovery of pseudotime increases the temporal resolution of single-cell transcriptional data, but is challenging due to the high variability in gene expression between individual cells. Here, we introduce HopLand, a pseudotime recovery method using continuous Hopfield network to map cells to a Waddingtons epigenetic landscape. It reveals from the single-cell data the combinatorial regulatory interactions among genes that control the dynamic progression through successive cell states.'),
(80942, 2017, 'bio', 'SnapDock-template-based docking by Geometric Hashing', 'Estrin, Michael|Wolfson, Haim J', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel', 6, 'Algorithms|Computational Biology|Molecular Docking Simulation|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx233', '28881968', 'A highly efficient template-based protein-protein docking algorithm, nicknamed SnapDock, is presented. It employs a Geometric Hashing-based structural alignment scheme to align the target proteins to the interfaces of non-redundant protein-protein interface libraries. Docking of a pair of proteins utilizing the 22xe2x80x89600 interface PIFACE library is performed inxe2x80x89<xe2x80x892xe2x80x89min on the average. A flexible version of the algorithm allowing hinge motion in one of the proteins is presented as well.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80943, 2017, 'bio', 'Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding', 'Min, Xu|Zeng, Wanwen|Chen, Ning|Chen, Ting|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China.', 'China|China|China|China|China', 27, 'Cell Line|Chromatin|Computational Biology|Humans|Neural Networks (Computer)|Software', 'Journal Article', '10.1093/bioinformatics/btx234', '28881969', 'Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning.'),
(80944, 2017, 'bio', 'Improving the performance of minimizers and winnowing schemes', 'Marçais, Guillaume|Pellow, David|Bork, Daniel|Orenstein, Yaron|Shamir, Ron|Kingsford, Carl', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.|Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|Israel|United States|United States|Israel|United States', 7, 'Algorithms|Genome, Human|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx235', '28881970', 'The minimizers scheme is a method for selecting k -mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g. too many k -mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k -mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.'),
(80945, 2017, 'bio', 'Modelling haplotypes with respect to reference cohort variation graphs', 'Rosen, Yohei|Eizenga, Jordan|Paten, Benedict', 'Baskin School of Engineering, UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA.|Baskin School of Engineering, UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA.|Baskin School of Engineering, UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA.', 'United States|United States|United States', 5, 'Algorithms|Genetic Variation|Genetics, Population|Genome, Human|Genomics|Haplotypes|Humans|Models, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx236', '28881971', 'Current statistical models of haplotypes are limited to panels of haplotypes whose genetic variation can be represented by arrays of values at linearly ordered bi- or multiallelic loci. These methods cannot model structural variants or variants that nest or overlap.'),
(80946, 2017, 'bio', 'Abundance estimation and differential testing on strain level in metagenomics data', 'Fischer, Martina|Strauch, Benjamin|Renard, Bernhard Y', 'Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.|Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.|Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.', 'Germany|Germany|Germany', 9, 'Algorithms|Bacteria|Genome, Bacterial|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx237', '28881972', 'Current metagenomics approaches allow analyzing the composition of microbial communities at high resolution. Important changes to the composition are known to even occur on strain level and to go hand in hand with changes in disease or ecological state. However, specific challenges arise for strain level analysis due to highly similar genome sequences present. Only a limited number of tools approach taxa abundance estimation beyond species level and there is a strong need for dedicated tools for strain resolution and differential abundance testing.'),
(80947, 2017, 'bio', 'BIOSSES: a semantic sentence similarity estimation system for the biomedical domain', 'Sogancioglu, Gizem|xc3x96ztxc3xbcrk, Hakime|xc3x96zgxc3xbcr, Arzucan', 'Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Computer Engineering, Bogazici University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 30, 'Biological Ontologies|Humans|Natural Language Processing|Semantics|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx238', '28881973', 'The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text.'),
(80948, 2017, 'bio', 'Large-scale structure prediction by improved contact predictions and model quality assessment', 'Michel, Mirco|Menéndez Hurtado, David|Uziela, Karolis|Elofsson, Arne', 'Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 19, 'Computational Biology|Machine Learning|Models, Molecular|Protein Conformation|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btx239', '28881974', 'Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.'),
(80949, 2017, 'bio', 'Molecular signatures that can be transferred across different omics platforms', 'Altenbuchinger, M|Schwarzfischer, P|Rehberg, T|Reinders, J|Kohler, Ch W|Gronwald, W|Richter, J|Szczepanowski, M|Masqué-Soler, N|Klapper, W|Oefner, P J|Spang, R', 'Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Department of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel/Christian-Albrecht University, Kiel, Germany.|Department of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel/Christian-Albrecht University, Kiel, Germany.|Department of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel/Christian-Albrecht University, Kiel, Germany.|Department of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel/Christian-Albrecht University, Kiel, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 8, 'Algorithms|Burkitt Lymphoma|Computational Biology|Formaldehyde|Freezing|Humans|Lymphoma, Large B-Cell, Diffuse|Models, Statistical|Paraffin Embedding|Proteome|Software|Tissue Preservation|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx241', '28881975', 'Molecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms.'),
(80950, 2017, 'bio', 'Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects', 'Lee, C H|Eskin, E|Han, B', 'Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea.|Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea.', 'United States|United States|United States', 7, 'Alzheimer Disease|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Models, Statistical|Parkinson Disease|Software', 'Journal Article', '10.1093/bioinformatics/btx242', '28881976', 'Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE.'),
(80951, 2017, 'bio', 'Denoising genome-wide histone ChIP-seq with convolutional neural networks', 'Koh, Pang Wei|Pierson, Emma|Kundaje, Anshul', 'Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.', 'United States|United States|United States', 0, 'Animals|Cell Line|Chromatin Immunoprecipitation|Epigenomics|High-Throughput Nucleotide Sequencing|Histone Code|Humans|Mice|Neural Networks (Computer)|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx243', '28881977', 'Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging.'),
(80952, 2017, 'bio', 'Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields', 'Robinson, Sean|Nevalainen, Jaakko|Pinna, Guillaume|Campalans, Anna|Radicella, J Pablo|Guyon, Laurent', 'CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.|Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Plateforme ARN Interférence (PArI), DSV/ISVFJ/SBIGEM/UMR 9198 I2BC, CEA Saclay, Gif-sur-Yvette, France.|Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.|Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.|CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.', 'France|Finland|France|France|France|France', 3, 'Algorithms|Gene Regulatory Networks|Genomics|Humans|Lymphoma|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btx244', '28881978', 'Incorporating gene interaction data into the identification of hit genes in genomic experiments is a well-established approach leveraging the guilt by association assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.'),
(80953, 2017, 'bio', 'Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis', 'Hao, Xiaoke|Li, Chanxiu|Yan, Jingwen|Yao, Xiaohui|Risacher, Shannon L|Saykin, Andrew J|Shen, Li|Zhang, Daoqiang|Alzheimer’s Disease Neuroimaging Initiative', 'School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.|School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.', 'China|China|India|India|India|India|India|China|China', 0, 'Aged|Aged, 80 and over|Alzheimer Disease|Biomarkers|Brain|Cognitive Dysfunction|Female|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Image Processing, Computer-Assisted|Machine Learning|Male|Neuroimaging|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btx245', '28881979', 'Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.'),
(80954, 2017, 'bio', 'Alignment of dynamic networks', 'Vijayan, V|Critchlow, D|Milenkovic, T', 'Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.|Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.|Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.', 'United States|United States|United States', 0, 'Computational Biology|Humans|Models, Biological|Protein Interaction Maps|Social Support|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx246', '28881980', 'Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems static network representations, as is currently done.'),
(80955, 2017, 'bio', 'TITER: predicting translation initiation sites by deep learning', 'Zhang, Sai|Hu, Hailin|Jiang, Tao|Zhang, Lei|Zeng, Jianyang', 'Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|School of Medicine, Tsinghua University, Beijing, China.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|School of Medicine, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.', 'China|China|United States|China|China', 20, 'Animals|Codon, Initiator|Computational Biology|Humans|Machine Learning|Mice|Models, Genetic|Neural Networks (Computer)|Open Reading Frames|Peptide Chain Initiation, Translational|Software', 'Journal Article', '10.1093/bioinformatics/btx247', '28881981', 'Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.'),
(80956, 2017, 'bio', 'Association testing of bisulfite-sequencing methylation data via a Laplace approximation', 'Weissbrod, Omer|Rahmani, Elior|Schweiger, Regev|Rosset, Saharon|Halperin, Eran', 'Statistics Department, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Statistics Department, Tel Aviv University, Tel Aviv, Israel.|Computer Science Department, University of California Los Angeles, Los Angeles, CA, USA.', 'Israel|Israel|Israel|Israel|United States', 4, 'DNA Methylation|Epigenomics|Humans|Markov Chains|Monte Carlo Method|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btx248', '28881982', 'Epigenome-wide association studies can provide novel insights into the regulation of genes involved in traits and diseases. The rapid emergence of bisulfite-sequencing technologies enables performing such genome-wide studies at the resolution of single nucleotides. However, analysis of data produced by bisulfite-sequencing poses statistical challenges owing to low and uneven sequencing depth, as well as the presence of confounding factors. The recently introduced Mixed model Association for Count data via data AUgmentation (MACAU) can address these challenges via a generalized linear mixed model when confounding can be encoded via a single variance component. However, MACAU cannot be used in the presence of multiple variance components. Additionally, MACAU uses a computationally expensive Markov Chain Monte Carlo (MCMC) procedure, which cannot directly approximate the model likelihood.'),
(80957, 2017, 'bio', 'A scalable moment-closure approximation for large-scale biochemical reaction networks', 'Kazeroonian, Atefeh|Theis, Fabian J|Hasenauer, Jan', 'Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Simulation|Markov Chains|Metabolic Networks and Pathways|Models, Biological|Signal Transduction|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btx249', '28881983', 'Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. The number of state variables of these approximating models, however, grows at least quadratically with the number of biochemical species. This limits their application to small- and medium-sized processes.'),
(80958, 2017, 'bio', 'Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data', 'Cinquemani, Eugenio|Laroute, Valérie|Cocaign-Bousquet, Muriel|de Jong, Hidde|Ropers, Delphine', 'Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France.|Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France.', 'France|France|France|France|France', 0, 'Bayes Theorem|Escherichia coli|Lactococcus lactis|Metabolomics|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx250', '28881984', 'Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime.'),
(80959, 2017, 'bio', 'popFBA: tackling intratumour heterogeneity with Flux Balance Analysis', 'Damiani, Chiara|Di Filippo, Marzia|Pescini, Dario|Maspero, Davide|Colombo, Riccardo|Mauri, Giancarlo', 'SYSBIO Centre of Systems Biology, Milan, Italy.|SYSBIO Centre of Systems Biology, Milan, Italy.|SYSBIO Centre of Systems Biology, Milan, Italy.|Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy.|SYSBIO Centre of Systems Biology, Milan, Italy.|SYSBIO Centre of Systems Biology, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 13, 'Cell Proliferation|Computational Biology|Computer Simulation|Humans|Metabolic Networks and Pathways|Models, Biological|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx251', '28881985', 'Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA , an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations.'),
(80960, 2017, 'bio', 'Predicting multicellular function through multi-layer tissue networks', 'Zitnik, Marinka|Leskovec, Jure', 'Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.', 'United States|United States', 74, 'Algorithms|Computational Biology|Humans|Organ Specificity|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btx252', '28881986', 'Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.'),
(80961, 2017, 'bio', 'Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems', 'Pischel, Dennis|Sundmacher, Kai|Flassig, Robert J', 'Otto-von-Guericke-University Magdeburg, Process Systems Engineering, Magdeburg, Germany.|Otto-von-Guericke-University Magdeburg, Process Systems Engineering, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, Magdeburg, Germany.', 'Germany|Germany|Germany', 3, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression|Kinetics|Metabolic Networks and Pathways|Models, Biological|Monte Carlo Method|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btx253', '28881987', 'Biological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution.'),
(80962, 2017, 'bio', 'Discovery and genotyping of novel sequence insertions in many sequenced individuals', 'Kavak, Pinar|Lin, Yen-Yi|Numanagic, Ibrahim|Asghari, Hossein|Güngör, Tunga|Alkan, Can|Hach, Faraz', 'Department of Computer Engineering, Bogaziçi University, Istanbul, Turkey.|School of Computing Science, Simon Fraser University, Burnaby, Canada.|School of Computing Science, Simon Fraser University, Burnaby, Canada.|School of Computing Science, Simon Fraser University, Burnaby, Canada.|Department of Computer Engineering, Bogaziçi University, Istanbul, Turkey.|Department of Computer Engineering, Bilkent University, Ankara, Turkey.|School of Computing Science, Simon Fraser University, Burnaby, Canada.', 'Turkey|Canada|Canada|Canada|Turkey|Turkey|Canada', 0, 'Algorithms|Genome, Human|Genomic Structural Variation|Genomics|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx254', '28881988', 'Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Therefore, characterization of novel sequence insertions is not a routine part of most sequencing projects.'),
(80963, 2017, 'bio', 'Direct AUC optimization of regulatory motifs', 'Zhu, Lin|Zhang, Hong-Bo|Huang, De-Shuang', 'Institute of Machine Learning and Systems Biology, Department of College of Electronics and Information Engineering, Tongji University, Shanghai, China.|Institute of Machine Learning and Systems Biology, Department of College of Electronics and Information Engineering, Tongji University, Shanghai, China.|Institute of Machine Learning and Systems Biology, Department of College of Electronics and Information Engineering, Tongji University, Shanghai, China.', 'China|China|China', 8, 'Algorithms|Area Under Curve|Binding Sites|Computational Biology|Humans|K562 Cells|Nucleotide Motifs|Promoter Regions, Genetic|Protein Binding|ROC Curve|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx255', '28881989', 'The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the discovery power of accumulated huge amount of high-throughput binding data. However, they have to sacrifice accuracy for speed and could fail to fully utilize the information of the input sequences.'),
(80964, 2017, 'bio', 'A new method to study the change of miRNA-mRNA interactions due to environmental exposures', 'Petralia, Francesca|Aushev, Vasily N|Gopalakrishnan, Kalpana|Kappil, Maya|W Khin, Nyan|Chen, Jia|Teitelbaum, Susan L|Wang, Pei', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Animals|Cell Line|Computational Biology|Environmental Exposure|Gene Expression Regulation|Humans|MicroRNAs|Models, Biological|RNA, Messenger|Rats|Sample Size|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx256', '28881990', 'Integrative approaches characterizing the interactions among different types of biological molecules have been demonstrated to be useful for revealing informative biological mechanisms. One such example is the interaction between microRNA (miRNA) and messenger RNA (mRNA), whose deregulation may be sensitive to environmental insult leading to altered phenotypes. The goal of this work is to develop an effective data integration method to characterize deregulation between miRNA and mRNA due to environmental toxicant exposures. We will use data from an animal experiment designed to investigate the effect of low-dose environmental chemical exposure on normal mammary gland development in rats to motivate and evaluate the proposed method.'),
(80965, 2017, 'bio', 'Exploiting sequence-based features for predicting enhancer-promoter interactions', 'Yang, Yang|Zhang, Ruochi|Singh, Shashank|Ma, Jian', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Automation, Tsinghua University, Beijing, China.|Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|China|United States|United States', 0, 'Algorithms|Cell Line|Enhancer Elements, Genetic|Gene Expression Regulation|Genome, Human|Genomics|Humans|Promoter Regions, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx257', '28881991', 'A large number of distal enhancers and proximal promoters form enhancer-promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer-promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.'),
(80966, 2017, 'bio', 'Image-based spatiotemporal causality inference for protein signaling networks', 'Ruan, Xiongtao|Wülfing, Christoph|Murphy, Robert F', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Cellular and Molecular Medicine, University of Bristol, Bristol BS, UK.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Humans|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Models, Biological|Proteins|Signal Transduction|Software|T-Lymphocytes', 'Journal Article', '10.1093/bioinformatics/btx258', '28881992', 'Efforts to model how signaling and regulatory networks work in cells have largely either not considered spatial organization or have used compartmental models with minimal spatial resolution. Fluorescence microscopy provides the ability to monitor the spatiotemporal distribution of many molecules during signaling events, but as of yet no methods have been described for large scale image analysis to learn a complex protein regulatory network. Here we present and evaluate methods for identifying how changes in concentration in one cell region influence concentration of other proteins in other regions.'),
(80967, 2017, 'bio', 'Genomes as documents of evolutionary history: a probabilistic macrosynteny model for the reconstruction of ancestral genomes', 'Nakatani, Yoichiro|McLysaght, Aoife', 'Department of Genetics, Smurfit Institute of Genetics, Trinity College Dublin, University of Dublin, Dublin 2, Ireland.|Department of Genetics, Smurfit Institute of Genetics, Trinity College Dublin, University of Dublin, Dublin 2, Ireland.', 'Ireland|Ireland', 8, 'Algorithms|Animals|Bayes Theorem|Chromosomes|Evolution, Molecular|Gene Order|Genome|Genomics|Humans|Models, Genetic|Phylogeny|Software|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btx259', '28881993', 'It has been argued that whole-genome duplication (WGD) exerted a profound influence on the course of evolution. For the purpose of fully understanding the impact of WGD, several formal algorithms have been developed for reconstructing pre-WGD gene order in yeast and plant. However, to the best of our knowledge, those algorithms have never been successfully applied to WGD events in teleost and vertebrate, impeded by extensive gene shuffling and gene losses.'),
(80968, 2017, 'bio', 'Multiple network-constrained regressions expand insights into influenza vaccination responses', 'Avey, Stefan|Mohanty, Subhasis|Wilson, Jean|Zapata, Heidi|Joshi, Samit R|Siconolfi, Barbara|Tsang, Sui|Shaw, Albert C|Kleinstein, Steven H', 'Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Computational Biology|Gene Expression Regulation|Humans|Immune System|Influenza, Human|Models, Biological|Signal Transduction|Transcriptome|Vaccination', 'Journal Article', '10.1093/bioinformatics/btx260', '28881994', 'Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology.'),
(80969, 2017, 'bio', 'deBGR: an efficient and near-exact representation of the weighted de Bruijn graph', 'Pandey, Prashant|Bender, Michael A|Johnson, Rob|Patro, Rob', 'Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx261', '28881995', 'Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using long read technologies like those offered by PacBio or Oxford Nanopore), efficient k -mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k -mer occurs, which is key in transcriptome assemblers.'),
(80970, 2017, 'bio', 'Improved data-driven likelihood factorizations for transcript abundance estimation', 'Zakeri, Mohsen|Srivastava, Avi|Almodaresi, Fatemeh|Patro, Rob', 'Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.', 'United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Likelihood Functions|Models, Biological|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx262', '28881996', 'Many methods for transcript-level abundance estimation reduce the computational burden associated with the iterative algorithms they use by adopting an approximate factorization of the likelihood function they optimize. This leads to considerably faster convergence of the optimization procedure, since each round of e.g. the EM algorithm, can execute much more quickly. However, these approximate factorizations of the likelihood function simplify calculations at the expense of discarding certain information that can be useful for accurate transcript abundance estimation.'),
(80971, 2017, 'bio', 'Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression', 'Ding, Lei|McDonald, Daniel J', 'Department of Statistics, Indiana University, Bloomington, IN, USA.|Department of Statistics, Indiana University, Bloomington, IN, USA.', 'India|India', 2, 'Computational Biology|Gene Expression Profiling|Humans|Lymphoma, B-Cell|Models, Genetic|Phenotype|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx265', '28881997', 'The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable.'),
(80972, 2017, 'bio', 'Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression', 'Ammad-Ud-Din, Muhammad|Khan, Suleiman A|Wennerberg, Krister|Aittokallio, Tero', 'Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland', 0, 'Algorithms|Antineoplastic Agents|Bayes Theorem|Computational Biology|Humans|Linear Models|Models, Biological|Neoplasms|Precision Medicine|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx266', '28881998', 'A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations.'),
(80973, 2017, 'bio', 'DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields', 'Shao, Mingfu|Ma, Jianzhu|Wang, Sheng', 'Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Medicine, University of California San Diego, La Jolla, CA, USA.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.', 'United States|United States|Saudi Arabia', 2, 'Algorithms|Area Under Curve|Computer Simulation|Exons|Humans|Introns|Models, Genetic|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx267', '28881999', 'Reconstructing the full-length expressed transcripts ( a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.'),
(80974, 2017, 'bio', 'Integrative deep models for alternative splicing', 'Jha, Anupama|Gazzara, Matthew R|Barash, Yoseph', 'Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA.|Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA.|Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States|United States', 0, 'Alternative Splicing|Animals|Bayes Theorem|Brain|Exons|Genomics|Mice|Models, Genetic|Muscle, Skeletal|Myocardium|Neural Networks (Computer)|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx268', '28882000', 'Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work, we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors.'),
(80975, 2017, 'bio', 'Efficient approximations of RNA kinetics landscape using non-redundant sampling', 'Michálik, Juraj|Touzet, Hélène|Ponty, Yann', 'AMIB project, Inria Saclay, Palaiseau, France.|CNRS, CRIStAL (UMR 9189, University of Lille), Villeneuve dAscq, France.|AMIB project, Inria Saclay, Palaiseau, France.', 'France|France|France', 0, 'Algorithms|Computational Biology|Kinetics|Nucleic Acid Conformation|RNA|Riboswitch|Software|Thermodynamics|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btx269', '28882001', 'Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computational demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, obtained using sampling strategies that strive to generate the key landmarks of the landscape topology. However, such methods are impeded by a large level of redundancy within sampled sets. Such a redundancy is uninformative, and obfuscates important intermediate states, leading to an incomplete vision of RNA dynamics.'),
(80976, 2017, 'bio', 'Tumor phylogeny inference using tree-constrained importance sampling', 'Satas, Gryte|Raphael, Benjamin J', 'Department of Computer Science, Brown University, Providence, RI, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.', 'United States|United States', 14, 'Algorithms|Clonal Evolution|Genomics|Humans|Leukemia, Lymphoid|Models, Statistical|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx270', '28882002', 'A tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80977, 2017, 'bio', 'miniMDS: 3D structural inference from high-resolution Hi-C data', 'Rieber, Lila|Mahony, Shaun', 'Department of Biochemistry and Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.|Department of Biochemistry and Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.', 'United States|United States', 0, 'Algorithms|Chromosomes, Human|Genome, Human|Genomics|Humans|Models, Molecular|Molecular Conformation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx271', '28882003', 'Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.'),
(80978, 2017, 'bio', 'When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants', 'Pagel, Kymberleigh A|Pejaver, Vikas|Lin, Guan Ning|Nam, Hyun-Jun|Mort, Matthew|Cooper, David N|Sebat, Jonathan|Iakoucheva, Lilia M|Mooney, Sean D|Radivojac, Predrag', 'Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.|Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.|Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.|Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.|Institute of Medical Genetics, Cardiff University, Cardiff, UK.|Institute of Medical Genetics, Cardiff University, Cardiff, UK.|Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.|Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.|Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.', 'India|India|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|India', 11, 'Computational Biology|Humans|Loss of Function Mutation|Machine Learning|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx272', '28882004', 'Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evolutionary conservation with little to no consideration of the structural and functional implications for the protein. They further do not provide information to the user regarding specific molecular alterations potentially causative of disease.'),
(80979, 2017, 'bio', 'CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions', 'Hallen, Mark A|Donald, Bruce R', 'Department of Computer Science, Duke University, Durham, NC, USA.|Department of Computer Science, Duke University, Durham, NC, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Models, Molecular|Mutation|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx277', '28882005', 'When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. This is especially true for combinatorial protein design algorithms, which aim to search a large sequence space efficiently and thus cannot rely on temporal simulation of each candidate sequence.'),
(80980, 2017, 'bio', 'POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles', 'Wang, Jiawei|Yang, Bingjiao|Revote, Jerico|Leier, André|Marquez-Lago, Tatiana T|Webb, Geoffrey|Song, Jiangning|Chou, Kuo-Chen|Lithgow, Trevor', 'Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Informatics Institute and Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Informatics Institute and Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Monash Centre for Data Science, Faculty of Information Technology.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Gordon Life Science Institute, Boston, MA 02478, USA.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.', 'Australia|China|Australia|United States|United States|United States|Australia|United States|Australia', 0, 'Computational Biology|Machine Learning|Position-Specific Scoring Matrices|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx302', '28903538', 'Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of protein attributes. Even though a number of algorithms have been proposed in previous studies, there is currently no universal web server or toolkit available for generating this wide variety of descriptors. Here, we present POSSUM ( Po sition- S pecific S coring matrix-based feat u re generator for m achine learning), a versatile toolkit with an online web server that can generate 21 types of PSSM-based feature descriptors, thereby addressing a crucial need for bioinformaticians and computational biologists. We envisage that this comprehensive toolkit will be widely used as a powerful tool to facilitate feature extraction, selection, and benchmarking of machine learning-based models, thereby contributing to a more effective analysis and modeling pipeline for bioinformatics research.'),
(80981, 2017, 'bio', 'Kollector: transcript-informed, targeted de novo assembly of gene loci', 'Kucuk, Erdi|Chu, Justin|Vandervalk, Benjamin P|Austin Hammond, S|Warren, René L', 'Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx405', '28903539', '-'),
(80982, 2017, 'bio', 'Molecular signatures that can be transferred across different omics platforms', 'Altenbuchinger, M|Schwarzfischer, P|Rehberg, T|Reinders, J|Kohler, Ch W|Gronwald, W|Richter, J|Szczepanowski, M|Masqué-Soler, N|Klapper, W|Oefner, P J|Spang, R', 'Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.|Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx488', '28903540', '-'),
(80983, 2017, 'bio', 'Spliceman2: a computational web server that predicts defects in pre-mRNA splicing', 'Cygan, Kamil Jan|Sanford, Clayton Hendrick|Fairbrother, William Guy', 'Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02903, USA.|Division of Applied Mathematics.|Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02903, USA.', 'United States|United States|United States', 0, 'Computational Biology|Humans|Mutation|RNA Precursors|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx343', '28911038', 'Most pre-mRNA transcripts in eukaryotic cells must undergo splicing to remove introns and join exons, and splicing elements present a large mutational target for disease-causing mutations. Splicing elements are strongly position dependent with respect to the transcript annotations. In 2012, we presented Spliceman, an online tool that used positional dependence to predict how likely distant mutations around annotated splice sites were to disrupt splicing. Here, we present an improved version of the previous tool that will be more useful for predicting the likelihood of splicing mutations. We have added industry-standard input options (i.e. Spliceman now accepts variant call format files), which allow much larger inputs than previously available. The tool also can visualize the locations-within exons and introns-of sequence variants to be analyzed and the predicted effects on splicing of the pre-mRNA transcript. In addition, Spliceman2 integrates with RNAcompete motif libraries to provide a prediction of which trans -acting factors binding sites are disrupted/created and links out to the UCSC genome browser. In summary, the new features in Spliceman2 will allow scientists and physicians to better understand the effects of single nucleotide variations on splicing.'),
(80984, 2018, 'bio', 'biospear: an R package for biomarker selection in penalized Cox regression', 'Ternès, Nils|Rotolo, Federico|Michiels, Stefan', 'Gustave Roussy, Université Paris-Saclay, Service de biostatistique et dépidémiologie, Villejuif, F-94805, France.|Gustave Roussy, Université Paris-Saclay, Service de biostatistique et dépidémiologie, Villejuif, F-94805, France.|Gustave Roussy, Université Paris-Saclay, Service de biostatistique et dépidémiologie, Villejuif, F-94805, France.', 'France|France|France', 0, 'Biomarkers|Computational Biology|Humans|Proportional Hazards Models|Software', 'Journal Article', '10.1093/bioinformatics/btx560', '28927242', 'The R package biospear allows selecting the biomarkers with the strongest impact on survival and on the treatment effect in high-dimensional Cox models, and estimating expected survival probabilities. Most of the implemented approaches are based on penalized regression techniques.'),
(80985, 2017, 'bio', 'GraphSpace: stimulating interdisciplinary collaborations in network biology', 'Bharadwaj, Aditya|Singh, Divit P|Ritz, Anna|Tegge, Allison N|Poirel, Christopher L|Kraikivski, Pavel|Adames, Neil|Luther, Kurt|Kale, Shiv D|Peccoud, Jean|Tyson, John J|Murali, T M', 'Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|Biology Department, Reed College, Portland, OR 97202, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|RedOwl Analytics, Baltimore, MD 21230, USA.|Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA.|Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|Biocomplexity Institute.|Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.|Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, 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', 0, 'Algorithms|Computational Biology|Interdisciplinary Communication|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx382', '28957495', 'Networks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks.'),
(80986, 2017, 'bio', 'Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections', 'Churas, Christopher|Perez, Alex J|Hakozaki, Hiroyuki|Wong, Willy|Lee, David|Peltier, Steven T|Ellisman, Mark H', 'National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).|National Center for Microscopy and Imaging Research (NCMIR).', '-|-|-|-|-|-|-', 0, 'Image Processing, Computer-Assisted|Microscopy, Electron|Organelles|Probability|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx376', '28957496', 'To expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging.'),
(80987, 2017, 'bio', 'A multi-scenario genome-wide medical population genetics simulation framework', 'Mugo, Jacquiline W|Geza, Ephifania|Defo, Joel|Elsheikh, Samar S M|Mazandu, Gaston K|Mulder, Nicola J|Chimusa, Emile R', 'Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Integrative Biomedical Sciences, Computational Biology Division, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town 7925, South Africa.|Department of Pathology, Division of Human Genetics, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory, Cape Town 7925, South Africa.', 'South Africa|South Africa|South Africa|South Africa|South Africa|South Africa|South Africa', 1, 'Genetic Variation|Genetics, Population|Genome|Genome-Wide Association Study|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Selection, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx369', '28957497', 'Recent technological advances in high-throughput sequencing and genotyping have facilitated an improved understanding of genomic structure and disease-associated genetic factors. In this context, simulation models can play a critical role in revealing various evolutionary and demographic effects on genomic variation, enabling researchers to assess existing and design novel analytical approaches. Although various simulation frameworks have been suggested, they do not account for natural selection in admixture processes. Most are tailored to a single chromosome or a genomic region, very few capture large-scale genomic data, and most are not accessible for genomic communities.'),
(80988, 2017, 'bio', 'ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data', 'Serviss, Jason T|Gådin, Jesper R|Eriksson, Per|Folkersen, Lasse|Grandér, Dan', 'Department of Oncology and Pathology, Karolinska University Hospital Solna, Cancer Center Karolinska, Stockholm, Sweden.|Department of Medicine, Cardiovascular Medicine Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Stockholm, Sweden.|Department of Medicine, Cardiovascular Medicine Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Stockholm, Sweden.|Department of Medicine, Cardiovascular Medicine Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Stockholm, Sweden.|Department of Oncology and Pathology, Karolinska University Hospital Solna, Cancer Center Karolinska, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Gene Expression Profiling|Hematologic Neoplasms|Humans|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btx393', '28957498', 'Multi-dimensional data generated via high-throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package, a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies.'),
(80989, 2017, 'bio', 'WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs', 'Galiez, Clovis|Siebert, Matthias|Enault, Franxc3xa7ois|Vincent, Jonathan|Sxc3xb6ding, Johannes', 'Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, 37077?Gxc3xb6ttingen, Germany.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, 37077?Gxc3xb6ttingen, Germany.|Universitxc3xa9 Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.|Universitxc3xa9 Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, 37077?Gxc3xb6ttingen, Germany.', 'Germany|Germany|France|France|Germany', 0, 'Archaea|Bacteria|Bacteriophages|Contig Mapping|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btx383', '28957499', 'WIsH predicts prokaryotic hosts of phages from their genomic sequences. It achieves 63% mean accuracy when predicting the host genus among 20 genera for 3 kbp-long phage contigs. Over the best current tool, WisH shows much improved accuracy on phage sequences of a few kbp length and runs hundreds of times faster, making it suited for metagenomics studies.'),
(80990, 2017, 'bio', 'fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample', 'Jxc3xb8rsboe, Emil|Hanghxc3xb8j, Kristian|Albrechtsen, Anders', 'Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.|Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark.|Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.', 'Denmark|Denmark|Denmark', 9, 'Genetics, Population|Genotype|High-Throughput Nucleotide Sequencing|Humans|Principal Component Analysis|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btx474', '28957500', 'Estimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases.'),
(80991, 2017, 'bio', 'Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape', 'Dai, Hanjun|Umarov, Ramzan|Kuwahara, Hiroyuki|Li, Yu|Song, Le|Gao, Xin', 'College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|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.|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.|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.|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|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.', 'Georgia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Georgia|Saudi Arabia', 11, 'Algorithms|Binding Sites|DNA|Machine Learning|Models, Statistical|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx480', '28961686', 'An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem.'),
(80992, 2017, 'bio', 'iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties', 'Chen, Wei|Yang, Hui|Feng, Pengmian|Ding, Hui|Lin, Hao', 'Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.', 'China|China|China|China|China', 143, 'Cytosine|DNA|DNA Methylation|Epigenomics|Genome|Nucleotides|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx479', '28961687', 'DNA N4-methylcytosine (4mC) is an epigenetic modification. The knowledge about the distribution of 4mC is helpful for understanding its biological functions. Although experimental methods have been proposed to detect 4mC sites, they are expensive for performing genome-wide detections. Thus, it is necessary to develop computational methods for predicting 4mC sites.'),
(80993, 2017, 'bio', 'Integrative structural and dynamical biology with PLUMED-ISDB', 'Bonomi, Massimiliano|Camilloni, Carlo', 'Department of Chemistry, University of Cambridge, Cambridge CB2xe2x80x891EW, UK.|Department of Chemistry, Institute for Advanced Study, Technische Universitxc3xa4t Mxc3xbcnchen, Garching 85747, Germany.', 'United Kingdom|Germany', 0, 'Bayes Theorem|Models, Molecular|Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx529', '28961689', 'Accurate structural models of biological systems can be obtained by properly combining experimental data with a priori physico-chemical knowledge. Here we present PLUMED-ISDB, an open-source, freely-available module of the popular PLUMED library, which enables the simultaneous determination of structure and dynamics of conformationally heterogeneous systems by integrating experimental data with a priori information. This integration is achieved using metainference, a general Bayesian framework that accounts for both noise in the data and their ensemble-averaged nature. PLUMED-ISDB implements different types of experimental data, such as several NMR observables, FRET, SAXS and cryo-electron microscopy data, and enables modelling structure and dynamics of individual proteins, protein complexes, membrane proteins, RNA and DNA, using a variety of enhanced sampling methods and resolutions of the system.'),
(80994, 2017, 'bio', 'ncDR: a comprehensive resource of non-coding RNAs involved in drug resistance', 'Dai, Enyu|Yang, Feng|Wang, Jing|Zhou, Xu|Song, Qian|An, Weiwei|Wang, Lihong|Jiang, Wei', 'Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|Department of Oncological Pharmacology, Institute of Cancer Prevention and Treatment, Heilongjiang Academy of Medical Science, Harbin Medical University, Harbin 150081, China.|Department of Pathophysiology, School of Medicine, Southeast University, Nanjing 210009, China.|Department of Biological Mathematics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.', 'China|China|China|China|China|China|China|China', 13, 'Drug Resistance|MicroRNAs|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btx523', '28961690', 'As a promising field of individualized therapy, non-coding RNA pharmacogenomics promotes the understanding of different individual responses to certain drugs and acts as a reasonable reference for clinical treatment. However, relevant information is scattered across the published literature, which is inconvenient for researchers to explore non-coding RNAs that are involved in drug resistance. To address this, we systemically identified validated and predicted drug resistance-associated microRNAs and long non-coding RNAs through manual curation and computational analysis. Subsequently, we constructed an omnibus repository named ncDR, which furnishes a user-friendly interface that allows for convenient browsing, visualization, querying and downloading of data. Given the rapidly increasing interest in precision medicine, ncDR will significantly improve our understanding of the roles of regulatory non-coding RNAs in drug resistance and has the potential to be a timely and valuable resource.'),
(80995, 2017, 'bio', '3DBIONOTES v20: a web server for the automatic annotation of macromolecular structures', 'Segura, Joan|Sanchez-Garcia, Ruben|Martinez, Marta|Cuenca-Alba, Jesus|Tabas-Madrid, Daniel|Sorzano, C O S|Carazo, J M', 'GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid 28049, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Automation|Computational Biology|Databases, Protein|Humans|Internet|Molecular Docking Simulation|Molecular Sequence Annotation|NIMA-Interacting Peptidylprolyl Isomerase|Protein Conformation|Proto-Oncogene Proteins c-akt|Software', 'Journal Article', '10.1093/bioinformatics/btx483', '28961691', 'Complementing structural information with biochemical and biomedical annotations is a powerful approach to explore the biological function of macromolecular complexes. However, currently the compilation of annotations and structural data is a feature only available for those structures that have been released as entries to the Protein Data Bank.'),
(80996, 2017, 'bio', 'An introduction to deep learning on biological sequence data: examples and solutions', 'Jurtz, Vanessa Isabell|Johansen, Alexander Rosenberg|Nielsen, Morten|Almagro Armenteros, Jose Juan|Nielsen, Henrik|Sønderby, Casper Kaae|Winther, Ole|Sønderby, Søren Kaae', 'Department of Bio and Health Informatics.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.|Department of Bio and Health Informatics.|Department of Bio and Health Informatics.|Department of Bio and Health Informatics.|Department of Biology, University of Copenhagen, Copenhagen, Denmark.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.|Department of Biology, University of Copenhagen, Copenhagen, Denmark.', '-|Denmark|-|-|-|Denmark|Denmark|Denmark', 32, 'Computational Biology|Machine Learning|Neural Networks (Computer)|Peptides|Protein Binding|Protein Structure, Secondary|Protein Transport|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx531', '28961695', 'Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition, and the development of tools, applications and code examples are in most cases centered within this field rather than within biology.'),
(80997, 2017, 'bio', 'Holmes: a graphical tool for development, simulation and analysis of Petri net based models of complex biological systems', 'Radom, Marcin|Rybarczyk, Agnieszka|Szawulak, Bartlomiej|Andrzejewski, Hubert|Chabelski, Piotr|Kozak, Adam|Formanowicz, Piotr', 'Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland', 10, 'Algorithms|Computational Biology|Computer Simulation|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx492', '28961696', 'Model development and its analysis is a fundamental step in systems biology. The theory of Petri nets offers a tool for such a task. Since the rapid development of computer science, a variety of tools for Petri nets emerged, offering various analytical algorithms. From this follows a problem of using different programs to analyse a single model. Many file formats and different representations of results make the analysis much harder. Especially for larger nets the ability to visualize the results in a proper form provides a huge help in the understanding of their significance. We present a new tool for Petri nets development and analysis called Holmes. Our program contains algorithms for model analysis based on different types of Petri nets, e.g. invariant generator, Maximum Common Transitions (MCT) sets and cluster modules, simulation algorithms or knockout analysis tools. A very important feature is the ability to visualize the results of almost all analytical modules. The integration of such modules into one graphical environment allows a researcher to fully devote his or her time to the model building and analysis.'),
(80998, 2018, 'bio', 'Orthologue chemical space and its influence on target prediction', 'Mervin, Lewis H|Bulusu, Krishna C|Kalash, Leen|Afzal, Avid M|Svensson, Fredrik|Firth, Mike A|Barrett, Ian|Engkvist, Ola|Bender, Andreas', 'Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Discovery Sciences, AstraZeneca R&D, Cambridge Science Park, Cambridge, UK.|Discovery Sciences, AstraZeneca R&D, Cambridge Science Park, Cambridge, UK.|Discovery Sciences, AstraZeneca R&D Gothenburg, Mölndal, Sweden.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Sweden|United Kingdom', 8, 'Animals|Computational Biology|Computer Simulation|Drug Discovery|Humans|Ligands|Models, Biological|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btx525', '28961699', 'In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice.'),
(80999, 2018, 'bio', 'MWASTools: an R/bioconductor package for metabolome-wide association studies', 'Rodriguez-Martinez, Andrea|Posma, Joram M|Ayala, Rafael|Neves, Ana L|Anwar, Maryam|Petretto, Enrico|Emanueli, Costanza|Gauguier, Dominique|Nicholson, Jeremy K|Dumas, Marc-Emmanuel', 'Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Division of Myocardial Function, National Heart and Lung Institute, Imperial College London, UK.|Duke-NUS Medical School, Singapore.|Division of Myocardial Function, National Heart and Lung Institute, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.|Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Singapore|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Genetic Association Studies|Humans|Metabolome|Metabolomics|Models, Biological|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btx477', '28961702', 'MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis, metabolome-wide association analysis using various models (partial correlations, generalized linear models), visualization of statistical outcomes, metabolite assignment using statistical total correlation spectroscopy (STOCSY), and biological interpretation of metabolome-wide association studies results.'),
(81000, 2017, 'bio', 'Genome-wide association studies using a penalized moving-window regression', 'Bao, Minli|Wang, Kai', 'Interdisciplinary Graduate Program in Applied Mathematical and Computational Sciences.|Department of Biostatistics, University of Iowa, Iowa City, IA 52241, USA.', 'United States|United States', 1, 'Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btx522', '28961706', 'Genome-wide association studies (GWAS) have played an important role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and presence of noise at non-causal variants. In an effort to overcome these difficulties, a previous study proposed a regularized regression method that penalizes on the difference of signal strength between two consecutive single-nucleotide polymorphisms (SNPs).'),
(81001, 2017, 'bio', 'Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment', 'Rapakoulia, Trisevgeni|Gao, Xin|Huang, Yi|de Hoon, Michiel|Okada-Hatakeyama, Mariko|Suzuki, Harukazu|Arner, Erik', 'Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Molecular Network Control Genomics Unit, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan.|RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST (DGT)), Yokohama, Kanagawa 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.|RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST (DGT)), Yokohama, Kanagawa 230-0045, Japan.|Molecular Network Control Genomics Unit, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan.', 'Saudi Arabia|Saudi Arabia|Japan|Japan|Japan|Japan|Japan', 0, 'Antineoplastic Combined Chemotherapy Protocols|Enhancer Elements, Genetic|Genome, Human|Humans|Promoter Regions, Genetic|Regression Analysis|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btx503', '28961713', 'Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations.'),
(81002, 2017, 'bio', 'Predicting novel metabolic pathways through subgraph mining', 'Sankar, Aravind|Ranu, Sayan|Raman, Karthik', 'Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences.', '-|-|-', 0, 'Algorithms|Computational Biology|Data Mining|Metabolic Engineering|Metabolic Networks and Pathways|Molecular Structure', 'Journal Article', '10.1093/bioinformatics/btx481', '28961716', 'The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping, which are often hard to obtain accurately.'),
(81003, 2018, 'bio', 'Deconvolution of multiple infections in Plasmodium falciparum from high throughput sequencing data', 'Zhu, Sha Joe|Almagro-Garcia, Jacob|McVean, Gil', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics,University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom', 17, 'Coinfection|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Malaria, Falciparum|Plasmodium falciparum|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx530', '28961721', 'The presence of multiple infecting strains of the malarial parasite Plasmodium falciparum affects key phenotypic traits, including drug resistance and risk of severe disease. Advances in protocols and sequencing technology have made it possible to obtain high-coverage genome-wide sequencing data from blood samples and blood spots taken in the field. However, analyzing and interpreting such data is challenging because of the high rate of multiple infections present.'),
(81004, 2018, 'bio', 'Novel approaches for bioinformatic analysis of salivary RNA sequencing data for development', 'Kaczor-Urbanowicz, Karolina Elzbieta|Kim, Yong|Li, Feng|Galeev, Timur|Kitchen, Rob R|Gerstein, Mark|Koyano, Kikuye|Jeong, Sung-Hee|Wang, Xiaoyan|Elashoff, David|Kang, So Young|Kim, Su Mi|Kim, Kyoung|Kim, Sung|Chia, David|Xiao, Xinshu|Rozowsky, Joel|Wong, David T W', 'Center for Oral/Head & Neck Oncology Research, School of Dentistry, Division of Oral Biology & Medicine University of California at Los Angeles, Los Angeles, CA 90095, USA.|Center for Oral/Head & Neck Oncology Research, School of Dentistry, Division of Oral Biology & Medicine University of California at Los Angeles, Los Angeles, CA 90095, USA.|Center for Oral/Head & Neck Oncology Research, School of Dentistry, Division of Oral Biology & Medicine University of California at Los Angeles, Los Angeles, CA 90095, USA.|Department of Molecular Biophysics and Biochemistry, Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Molecular Biophysics and Biochemistry, Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Molecular Biophysics and Biochemistry, Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095-1570, USA.|Department of Oral Medicine, School of Dentistry, Pusan National University, Beomeo-ri, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do 626-770, Korea.|Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA 90024, USA.|Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA 90024, USA.|Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, Korea.|Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, Korea.|Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, Korea.|Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, Korea.|Department of Pathology & Laboratory Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.|Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095-1570, USA.|Department of Molecular Biophysics and Biochemistry,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Center for Oral/Head & Neck Oncology Research, School of Dentistry, Division of Oral Biology & Medicine University of California at Los Angeles, Los Angeles, CA 90095, 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|United States|United States|United States|United States|United States', 8, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|RNA|Saliva|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx504', '28961734', 'Analysis of RNA sequencing (RNA-Seq) data in human saliva is challenging. Lack of standardization and unification of the bioinformatic procedures undermines salivas diagnostic potential. Thus, it motivated us to perform this study.'),
(81005, 2017, 'bio', 'EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing', 'Berndsen, Zachary|Bowman, Charles|Jang, Haerin|Ward, Andrew B', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Cryoelectron Microscopy|Image Processing, Computer-Assisted|Pattern Recognition, Automated|Proteins', 'Journal Article', '10.1093/bioinformatics/btx500', '28961740', 'The Electron Microscopy Hole Punch (EMHP) is a streamlined suite of tools for quick assessment, sorting and hole masking of electron micrographs. With recent advances in single-particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of protein structures using a smaller computational footprint, we saw the need for a fast and simple tool for data pre-processing that could run independent of existing high-performance computing (HPC) infrastructures. EMHP provides a data preprocessing platform in a small package that requires minimal python dependencies to function.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81006, 2017, 'bio', 'BSviewer: a genotype-preserving, nucleotide-level visualizer for bisulfite sequencing data', 'Sun, Kun|Lun, Fiona F M|Jiang, Peiyong|Sun, Hao', 'Li Ka Shing Institute of Health Sciences.|Li Ka Shing Institute of Health Sciences.|Li Ka Shing Institute of Health Sciences.|Li Ka Shing Institute of Health Sciences.', '-|-|-|-', 3, 'Computer Graphics|DNA Methylation|Genotype|Humans|Nucleotides|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btx505', '28961742', 'The bisulfite sequencing technology has been widely used to study the DNA methylation profile in many species. However, most of the current visualization tools for bisulfite sequencing data only provide high-level views (i.e. overall methylation densities) while miss the methylation dynamics at nucleotide level. Meanwhile, they also focus on CpG sites while omit other information (such as genotypes on SNP sites) which could be helpful for interpreting the methylation pattern of the data. A bioinformatics tool that visualizes the methylation statuses at nucleotide level and preserves the most essential information of the sequencing data is thus valuable and needed.'),
(81007, 2017, 'bio', 'CESAR 20 substantially improves speed and accuracy of comparative gene annotation', 'Sharma, Virag|Schwede, Peter|Hiller, Michael', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.', 'Germany|Germany|Germany', 0, 'Computational Biology|Exons|Genome|Genomics|Introns|Molecular Sequence Annotation|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx527', '28961744', 'Homology-based gene prediction is a powerful concept to annotate newly sequenced genomes. We have previously demonstrated that whole genome alignments can be utilized for accurate comparative coding gene annotation.'),
(81008, 2017, 'bio', 'ChAMP: updated methylation analysis pipeline for Illumina BeadChips', 'Tian, Yuan|Morris, Tiffany J|Webster, Amy P|Yang, Zhen|Beck, Stephan|Feber, Andrew|Teschendorff, Andrew E', 'CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, University of Chinese Academy of Science, Chinese Academy of Sciences, Shanghai 200031, China.|Cambridge Epigenetix, Jonas Webb Building, Babraham Campus, Cambridge CB22xe2x80x893AT, UK.|Medical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, University of Chinese Academy of Science, Chinese Academy of Sciences, Shanghai 200031, China.|Medical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.|Medical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, University of Chinese Academy of Science, Chinese Academy of Sciences, Shanghai 200031, China.', 'China|United Kingdom|United Kingdom|China|United Kingdom|United Kingdom|China', 0, 'DNA Methylation|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx513', '28961746', 'The Illumina Infinium HumanMethylationEPIC BeadChip is the new platform for high-throughput DNA methylation analysis, effectively doubling the coverage compared to the older 450xe2x80x89K array. Here we present a significantly updated and improved version of the Bioconductor package ChAMP, which can be used to analyze EPIC and 450k data. Many enhanced functionalities have been added, including correction for cell-type heterogeneity, network analysis and a series of interactive graphical user interfaces.'),
(81009, 2017, 'bio', 'An improved compound Poisson model for the number of motif hits in DNA sequences', 'Kopp, Wolfgang|Vingron, Martin', 'Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.', 'Germany|Germany', 0, 'Binding Sites|DNA|Models, Statistical|Nucleotide Motifs|Poisson Distribution|Position-Specific Scoring Matrices|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx539', '28961747', 'Transcription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a transcription factor can be described in terms of position frequency matrices. When scanning a sequence for matches to a position frequency matrix, one needs to determine a cut-off, which then in turn results in a certain number of hits. In this paper we describe how to compute the distribution of match scores and of the number of motif hits, which are the prerequisites to perform motif hit enrichment analysis.'),
(81010, 2017, 'bio', 'LLR: a latent low-rank approach to colocalizing genetic risk variants in multiple GWAS', 'Liu, Jin|Wan, Xiang|Wang, Chaolong|Yang, Chao|Zhou, Xiaowei|Yang, Can', 'Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.|Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.|Genome Institute of Singapore, A*STAR, Singapore, Singapore.|Baidu Inc, Shanghai, China.|Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.|Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.', 'Singapore|China|Singapore|China|United States|China', 2, 'Algorithms|Data Interpretation, Statistical|Genetic Variation|Genome-Wide Association Study|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Risk|Software', 'Journal Article', '10.1093/bioinformatics/btx512', '28961754', 'Genome-wide association studies (GWAS), which genotype millions of single nucleotide polymorphisms (SNPs) in thousands of individuals, are widely used to identify the risk SNPs underlying complex human phenotypes (quantitative traits or diseases). Most conventional statistical methods in GWAS only investigate one phenotype at a time. However, an increasing number of reports suggest the ubiquity of pleiotropy, i.e. many complex phenotypes sharing common genetic bases. This motivated us to leverage pleiotropy to develop new statistical approaches to joint analysis of multiple GWAS.'),
(81011, 2018, 'bio', '4D nucleome Analysis Toolbox: analysis of Hi-C data with abnormal karyotype and time series capabilities', 'Seaman, Laura|Rajapakse, Indika', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States', 4, 'Abnormal Karyotype|Chromosomes, Human|Gene Expression Profiling|Gene Expression Regulation|Genomics|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx484', '28961756', 'The availability of powerful analysis tools will further understanding of genome organization and its relationship to phenotype in dynamical settings.'),
(81012, 2017, 'bio', 'clustComp, a bioconductor package for the comparison of clustering results', 'Torrente, Aurora|Brazma, Alvis', 'Departamento de Ciencia e Ingenierxc3xada de Materiales e Ingenierxc3xada Quxc3xadmica, Instituto Gregorio Millxc3xa1n, Universidad Carlos III de Madrid, 28911 Leganxc3xa9s, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10xe2x80x891SD, UK.', 'Spain|United Kingdom', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Software', 'Journal Article', '10.1093/bioinformatics/btx532', '28961761', 'clustComp is an open source Bioconductor package that implements different techniques for the comparison of two gene expression clustering results. These include flat versus flat and hierarchical versus flat comparisons. The visualization of the similarities is provided by means of a bipartite graph, whose layout is heuristically optimized. Its flexibility allows a suitable visualization for both small and large datasets.'),
(81013, 2017, 'bio', 'psygenet2r: a R/Bioconductor package for the analysis of psychiatric disease genes', 'Gutiérrez-Sacristán, Alba|Hernández-Ferrer, Carles|González, Juan R|Furlong, Laura I', 'Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM).|Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF).|Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF).|Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM).', '-|-|-|-', 0, 'Data Mining|Databases, Genetic|Genes|Humans|Mental Disorders|Software', 'Journal Article', '10.1093/bioinformatics/btx506', '28961763', 'Psychiatric disorders have a great impact on morbidity and mortality. Genotype-phenotype resources for psychiatric diseases are key to enable the translation of research findings to a better care of patients. PsyGeNET is a knowledge resource on psychiatric diseases and their genes, developed by text mining and curated by domain experts.'),
(81014, 2017, 'bio', 'Cloud-based interactive analytics for terabytes of genomic variants data', 'Pan, Cuiping|McInnes, Gregory|Deflaux, Nicole|Snyder, Michael|Bingham, Jonathan|Datta, Somalee|Tsao, Philip S', 'VA Palo Alto Health Care System, Palo Alto Epidemiology Research and Information Center for Genomics, CA 94304, USA.|VA Palo Alto Health Care System, Palo Alto Epidemiology Research and Information Center for Genomics, CA 94304, USA.|Google, Mountain View, CA 94043, USA.|Department of Genetics.|Google, Mountain View, CA 94043, USA.|VA Palo Alto Health Care System, Palo Alto Epidemiology Research and Information Center for Genomics, CA 94304, USA.|VA Palo Alto Health Care System, Palo Alto Epidemiology Research and Information Center for Genomics, CA 94304, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Data Compression|Databases, Nucleic Acid|Gene Frequency|Genetic Variation|Genome, Human|Genomics|Genotype|Humans|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btx468', '28961771', 'Large scale genomic sequencing is now widely used to decipher questions in diverse realms such as biological function, human diseases, evolution, ecosystems, and agriculture. With the quantity and diversity these data harbor, a robust and scalable data handling and analysis solution is desired.'),
(81015, 2017, 'bio', 'Reference genome assessment from a population scale perspective: an accurate profile of variability and noise', 'Carbonell-Caballero, José|Amadoz, Alicia|Alonso, Roberto|Hidalgo, Marta R|Çubuk, Cankut|Conesa, David|López-Quílez, Antonio|Dopazo, Joaquín', 'Computational Genomics, Principe Felipe Research Centre, Valencia 46012.|Computational Genomics, Principe Felipe Research Centre, Valencia 46012.|Computational Genomics, Principe Felipe Research Centre, Valencia 46012.|Computational Genomics, Principe Felipe Research Centre, Valencia 46012.|Computational Genomics, Principe Felipe Research Centre, Valencia 46012.|Estadística e investigación Operativa, Universitat de València, Burjassot 46100.|Estadística e investigación Operativa, Universitat de València, Burjassot 46100.|Computational Genomics, Principe Felipe Research Centre, Valencia 46012.', '-|-|-|-|-|-|-|-', 0, 'Animals|Genetic Variation|Genome|Genomics|Genotype|Humans|Models, Statistical|Quality Control|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx482', '28961772', 'Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.'),
(81016, 2017, 'bio', 'pgRNAFinder: a web-based tool to design distance independent paired-gRNA', 'Xiong, Yuanyan|Xie, Xiaowei|Wang, Yanzhi|Ma, Wenbing|Liang, Puping|Songyang, Zhou|Dai, Zhiming', 'Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China.|School of Data and Computer Science.', 'China|China|China|China|China|China|-', 0, 'Animals|CRISPR-Cas Systems|Gene Editing|Genomics|Internet|RNA, Guide|Software', 'Journal Article', '10.1093/bioinformatics/btx472', '28961776', 'The CRISPR/Cas System has been shown to be an efficient and accurate genome-editing technique. There exist a number of tools to design the guide RNA sequences and predict potential off-target sites. However, most of the existing computational tools on gRNA design are restricted to small deletions. To address this issue, we present pgRNAFinder, with an easy-to-use web interface, which enables researchers to design single or distance-free paired-gRNA sequences. The web interface of pgRNAFinder contains both gRNA search and scoring system. After users input query sequences, it searches gRNA by 3 protospacer-adjacent motif (PAM), and possible off-targets, and scores the conservation of the deleted sequences rapidly. Filters can be applied to identify high-quality CRISPR sites. PgRNAFinder offers gRNA design functionality for 8 vertebrate genomes. Furthermore, to keep pgRNAFinder open, extensible to any organism, we provide the source package for local use.'),
(81017, 2017, 'bio', 'HaploForge: a comprehensive pedigree drawing and haplotype visualization web application', 'Tekman, Mehmet|Medlar, Alan|Mozere, Monika|Kleta, Robert|Stanescu, Horia', 'Division of Medicine, University College London, London NW3xe2x80x892PF, UK.|Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland.|Division of Medicine, University College London, London NW3xe2x80x892PF, UK.|Division of Medicine, University College London, London NW3xe2x80x892PF, UK.|Division of Medicine, University College London, London NW3xe2x80x892PF, UK.', 'United Kingdom|Finland|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Alleles|Computer Graphics|Genetic Linkage|Genotype|Haplotypes|Humans|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btx510', '28961780', 'Haplotype reconstruction is an important tool for understanding the aetiology of human disease. Haplotyping infers the most likely phase of observed genotypes conditional on constraints imposed by the genotypes of other pedigree members. The results of haplotype reconstruction, when visualized appropriately, show which alleles are identical by descent despite the presence of untyped individuals. When used in concert with linkage analysis, haplotyping can help delineate a locus of interest and provide a succinct explanation for the transmission of the trait locus. Unfortunately, the design choices made by existing haplotype visualization programs do not scale to large numbers of markers. Indeed, following haplotypes from generation to generation requires excessive scrolling back and forth. In addition, the most widely used program for haplotype visualization produces inconsistent recombination artefacts for the X chromosome.'),
(81018, 2017, 'bio', 'MetaCache: context-aware classification of metagenomic reads using minhashing', 'Mxc3xbcller, Andrxc3xa9|Hundt, Christian|Hildebrandt, Andreas|Hankeln, Thomas|Schmidt, Bertil', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Molecular Genetics and Genome Analysis Group, Department of Biology, Department of Biology, Johannes Gutenberg University, 55128 Mainz, Germany.|Department of Computer Science.', '-|-|-|Germany|-', 9, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx520', '28961782', 'Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy.'),
(81019, 2017, 'bio', 'FIRE: functional inference of genetic variants that regulate gene expression', 'Ioannidis, Nilah M|Davis, Joe R|DeGorter, Marianne K|Larson, Nicholas B|McDonnell, Shannon K|French, Amy J|Battle, Alexis J|Hastie, Trevor J|Thibodeau, Stephen N|Montgomery, Stephen B|Bustamante, Carlos D|Sieh, Weiva|Whittemore, Alice S', 'Department of Genetics.|Department of Genetics.|Department of Genetics.|Department of Health Sciences Research.|Department of Health Sciences Research.|Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN 55905, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Statistics, Stanford University, Stanford, CA 94305, USA.|Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN 55905, USA.|Department of Genetics.|Department of Genetics.|Department of Health Research & Policy.|Department of Health Research & Policy.', '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', 0, 'Gene Expression Regulation|Genetic Variation|Genomics|Humans|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btx534', '28961785', 'Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies.'),
(81020, 2017, 'bio', 'FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery', 'Lagorce, David|Bouslama, Lina|Becot, Jerome|Miteva, Maria A|Villoutreix, Bruno O', 'INSERM, U973?35 rue Helene Brion, 75013 Paris, France.|INSERM, U973?35 rue Helene Brion, 75013 Paris, France.|INSERM, U973?35 rue Helene Brion, 75013 Paris, France.|INSERM, U973?35 rue Helene Brion, 75013 Paris, France.|INSERM, U973?35 rue Helene Brion, 75013 Paris, France.', 'France|France|France|France|France', 69, 'Computational Biology|Computer Simulation|Drug Discovery|Software', 'Journal Article', '10.1093/bioinformatics/btx491', '28961788', 'Identification of small molecules that could be interesting starting points for drug discovery or to investigate a biological system as in chemical biology endeavours is both time consuming and costly. In silico approaches that assist the design of quality compound collections or help to prioritize molecules before synthesis or purchase are therefore valuable. Here quality refers to the selection of molecules that pass one or several selected filters that can be tuned by the users according to the project and the stage of the project. These filters can involve prediction of physicochemical properties, search for toxicophores or other unwanted chemical groups.'),
(81021, 2018, 'bio', 'ReMILO: reference assisted misassembly detection algorithm using short and long reads', 'Bao, Ergude|Song, Changjin|Lan, Lingxiao', 'Software Engineering Research Center, School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.|Software Engineering Research Center, School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.|Software Engineering Research Center, School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.', 'China|China|China', 0, 'Algorithms|Chromosomes, Human, Pair 14|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx524', '28961789', 'Contigs assembled from the second generation sequencing short reads may contain misassemblies, and thus complicate downstream analysis or even lead to incorrect analysis results. Fortunately, with more and more sequenced species available, it becomes possible to use the reference genome of a closely related species to detect misassemblies. In addition, long reads of the third generation sequencing technology have been more and more widely used, and can also help detect misassemblies.'),
(81022, 2017, 'bio', 'Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts', 'Zhu, Jianwei|Zhang, Haicang|Li, Shuai Cheng|Wang, Chao|Kong, Lupeng|Sun, Shiwei|Zheng, Wei-Mou|Bu, Dongbo', 'Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.', 'China|China|Hong Kong|China|China|China|China|China', 0, 'Algorithms|Neural Networks (Computer)|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx514', '28961795', 'Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge.'),
(81023, 2018, 'bio', 'In silico identification of rescue sites by double force scanning', 'Tiberti, Matteo|Pandini, Alessandro|Fraternali, Franca|Fornili, Arianna', 'School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.|Department of Computer Science, College of Engineering, Design and Physical Sciences and Synthetic Biology Theme, Institute of Environment, Health and Societies, Brunel University London, Uxbridge, London, UK.|Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Simulation|Mutation|Protein Conformation|Protein Domains|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx515', '28961796', 'A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant.'),
(81024, 2017, 'bio', 'Efficient inference for sparse latent variable models of transcriptional regulation', 'Dai, Zhenwen|Iqbal, Mudassar|Lawrence, Neil D|Rattray, Magnus', 'Department of Computer Science, University of Sheffield, Sheffield, UK.|Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine, and Health Sciences, University of Manchester, Manchester, UK.|Department of Computer Science, University of Sheffield, Sheffield, UK.|Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine, and Health Sciences, University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Mycobacterium tuberculosis|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btx508', '28961802', 'Regulation of gene expression in prokaryotes involves complex co-regulatory mechanisms involving large numbers of transcriptional regulatory proteins and their target genes. Uncovering these genome-scale interactions constitutes a major bottleneck in systems biology. Sparse latent factor models, assuming activity of transcription factors (TFs) as unobserved, provide a biologically interpretable modelling framework, integrating gene expression and genome-wide binding data, but at the same time pose a hard computational inference problem. Existing probabilistic inference methods for such models rely on subjective filtering and suffer from scalability issues, thus are not well-suited for realistic genome-scale applications.'),
(81025, 2017, 'bio', 'Conditional asymptotic inference for the kernel association test', 'Wang, Kai', 'Department of Biostatistics, University of Iowa, Iowa City, IA 52246, USA.', 'United States', 0, 'Genotype|Humans|Machine Learning|Ocular Hypertension', 'Journal Article', '10.1093/bioinformatics/btx511', '28961861', 'The kernel association test (KAT) is popular in biological studies for its ability to combine weak effects potentially of opposite direction. Its P-value is typically assessed via its (unconditional) asymptotic distribution. However, such an asymptotic distribution is known only for continuous traits and for dichotomous traits. Furthermore, the derived P-values are known to be conservative when sample size is small, especially for the important case of dichotomous traits. One alternative is the permutation test, a widely accepted approximation to the exact finite sample conditional inference. But it is time-consuming to use in practice due to stringent significance criteria commonly seen in these analyses.'),
(81026, 2017, 'bio', 'PyLasso: a PyMOL plugin to identify lassos', 'Gierut, Aleksandra M|Niemyska, Wanda|Dabrowski-Tumanski, Pawel|Sulkowski, Piotr|Sulkowska, Joanna I', 'Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw, 02-097, Poland.|Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw, 02-097, Poland.|Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw, 02-097, Poland.|Faculty of Physics, University of Warsaw, Pasteura 5, Warsaw, 02-093, Poland.|Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw, 02-097, Poland.', 'Poland|Poland|Poland|Poland|Poland', 16, 'Computational Biology|Macromolecular Substances|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx493', '28961868', 'Entanglement in macromolecules is an important phenomenon and a subject of multidisciplinary research. As recently discovered, around 4% of proteins form new entangled motifs, called lassos. Here we present the PyLasso-a PyMOL plugin to identify and analyse properties of lassos in proteins and other (bio)polymers, as well as in other biological, physical and mathematical systems. The PyLasso is a useful tool for all researchers working on modeling of macromolecules, structure prediction, properties of polymers, entanglement in fluids and fields, etc.'),
(81027, 2017, 'bio', 'HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation', 'Deng, Yue|Zenil, Hector|Tegnér, Jesper|Kiani, Narsis A', 'Algorithmic Dynamics Lab.|Algorithmic Dynamics Lab.|Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Sweden.|Algorithmic Dynamics Lab.', '-|-|Sweden|-', 0, 'Algorithms|Benchmarking|Computational Biology|Gene Expression|Gene Regulatory Networks|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btx501', '28961895', 'The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the difficulty in estimating parameters and by their lack of scalability. Here, we introduce a novel method and pipeline to reverse engineer gene regulatory networks from gene expression of time series and perturbation data based upon an improvement on the calculation scheme of the derivatives and a pre-filtration step to reduce the number of possible links. The method introduces a linear differential equation model with adaptive numerical differentiation that is scalable to extremely large regulatory networks.'),
(81028, 2017, 'bio', 'Metagene projection characterizes GEN22 and CAL-1 as relevant human plasmacytoid dendritic cell models', 'Carmona-Sáez, Pedro|Varela, Nieves|Luque, María José|Toro-Domínguez, Daniel|Martorell-Marugan, Jordi|Alarcón-Riquelme, Marta E|Marañón, Concepción', 'Bioinformatics Unit.|Genomic Medicine Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS 18016, Granada, Spain.|Genomic Medicine Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS 18016, Granada, Spain.|Bioinformatics Unit.|Bioinformatics Unit.|Genomic Medicine Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS 18016, Granada, Spain.|Genomic Medicine Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS 18016, Granada, Spain.', '-|Spain|Spain|-|-|Spain|Spain', 5, 'Cell Line|Dendritic Cells|Humans|Models, Immunological|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx502', '28961902', 'Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages.'),
(81029, 2017, 'bio', 'CRISPR-DAV: CRISPR NGS data analysis and visualization pipeline', 'Wang, Xuning|Tilford, Charles|Neuhaus, Isaac|Mintier, Gabe|Guo, Qi|Feder, John N|Kirov, Stefan', 'Translational Bioinformatics & Computational Genomics.|Translational Bioinformatics & Computational Genomics.|Translational Bioinformatics & Computational Genomics.|Leads Discovery & Optimization.|Genome Biology, Bristol-Myers Squibb Co., Pennington, NJ 08534, USA.|Leads Discovery & Optimization.|Translational Bioinformatics & Computational Genomics.', 'United States|United States|United States|United States|United States|United States|United States', 17, 'Bacteria|Clone Cells|Clustered Regularly Interspaced Short Palindromic Repeats|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|INDEL Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx518', '28961906', 'The simplicity and precision of CRISPR/Cas9 system has brought in a new era of gene editing. Screening for desired clones with CRISPR-mediated genomic edits in a large number of samples is made possible by next generation sequencing (NGS) due to its multiplexing. Here we present CRISPR-DAV (CRISPR Data Analysis and Visualization) pipeline to analyze the CRISPR NGS data in a high throughput manner. In the pipeline, Burrows-Wheeler Aligner and Assembly Based ReAlignment are used for small and large indel detection, and results are presented in a comprehensive set of charts and interactive alignment view.'),
(81030, 2017, 'bio', 'Enabling reproducible real-time quantitative PCR research: the RDML package', 'Rxc3xb6diger, Stefan|Burdukiewicz, Michal|Spiess, Andrej-Nikolai|Blagodatskikh, Konstantin', 'Institute of Biotechnology, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany.|Department of Genomics, Faculty of Biotechnology, University of Wroclaw, Wroclaw, Poland.|Department of Andrology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.|Evrogen JSC, Moscow, Russia.', 'Germany|Poland|Germany|-', 0, 'Data Interpretation, Statistical|Real-Time Polymerase Chain Reaction|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx528', '28961912', 'Reproducibility, a cornerstone of research, requires defined data formats, which include the setup and output of experiments. The real-time PCR data markup language (RDML) is a recommended standard of the minimum information for publication of quantitative real-time PCR experiments guidelines. Despite the popularity of the RDML format for analysis of quantitative PCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares.'),
(81031, 2017, 'bio', 'Towards clinically more relevant dissection of patient heterogeneity via survival-based Bayesian clustering', 'Ahmad, Ashar|Fröhlich, Holger', 'Bonn Aachen International Center for Information Technology, University of Bonn, 53127 Bonn, Germany.|Bonn Aachen International Center for Information Technology, University of Bonn, 53127 Bonn, Germany.', 'Germany|Germany', 0, 'Algorithms|Bayes Theorem|Breast Neoplasms|Cluster Analysis|Female|Gene Expression Profiling|Humans|Models, Biological|Precision Medicine|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btx464', '28961917', 'Discovery of clinically relevant disease sub-types is of prime importance in personalized medicine. Disease sub-type identification has in the past often been explored in an unsupervised machine learning paradigm which involves clustering of patients based on available-omics data, such as gene expression. A follow-up analysis involves determining the clinical relevance of the molecular sub-types such as that reflected by comparing their disease progressions. The above methodology, however, fails to guarantee the separability of the sub-types based on their subtype-specific survival curves.'),
(81032, 2017, 'bio', 'PhyloGeoTool: interactively exploring large phylogenies in an epidemiological context', 'Libin, Pieter|Vanden Eynden, Ewout|Incardona, Francesca|Nowé, Ann|Bezenchek, Antonia|EucoHIV Study Group|Sönnerborg, Anders|Vandamme, Anne-Mieke|Theys, Kristof|Baele, Guy', 'Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium.|Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium.|EuResist Network, Rome, Italy.|Department of Computer Science, Vrije Universiteit Brussel, 1050 Brussels, Belgium.|InformaPRO, Rome, Italy.|InformaPRO, Rome, Italy.|Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden.|Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium.|Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium.|Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium.', 'Belgium|Belgium|Italy|Belgium|Italy|Italy|Sweden|Belgium|Belgium|Belgium', 8, 'Cluster Analysis|Computational Biology|Databases, Nucleic Acid|Epidemiologic Methods|Humans|Phylogeny|Software|Virus Diseases', 'Journal Article', '10.1093/bioinformatics/btx535', '28961923', 'Clinicians, health officials and researchers are interested in the epidemic spread of pathogens in both space and time to support the optimization of intervention measures and public health policies. Large sequence databases of virus sequences provide an interesting opportunity to study this spread through phylogenetic analysis. To infer knowledge from large phylogenetic trees, potentially encompassing tens of thousands of virus strains, an efficient method for data exploration is required. The clades that are visited during this exploration should be annotated with strain characteristics (e.g. transmission risk group, tropism, drug resistance profile) and their geographic context.'),
(81033, 2017, 'bio', 'MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis', 'Matias Rodrigues, João F|Schmidt, Thomas S B|Tackmann, Janko|von Mering, Christian', 'Department of Molecular Life Sciences, and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.|Department of Molecular Life Sciences, and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.|Department of Molecular Life Sciences, and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.|Department of Molecular Life Sciences, and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Bacteria|Eukaryota|Genes, Microbial|RNA, Ribosomal|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx517', '28961926', 'Ribosomal RNA profiling has become crucial to studying microbial communities, but meaningful taxonomic analysis and inter-comparison of such data are still hampered by technical limitations, between-study design variability and inconsistencies between taxonomies used.'),
(81034, 2017, 'bio', 'MIRMMR: binary classification of microsatellite instability using methylation and mutations', 'Foltz, Steven M|Liang, Wen-Wei|Xie, Mingchao|Ding, Li', 'Oncology Division, Department of Medicine.|Oncology Division, Department of Medicine.|Oncology Division, Department of Medicine.|Oncology Division, Department of Medicine.', '-|-|-|-', 0, 'DNA Methylation|Genomics|Humans|Microsatellite Instability|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx507', '28961932', 'MIRMMR predicts microsatellite instability status in cancer samples using methylation and mutation information, in contrast to existing methods that rely on observed microsatellites. Additionally, MIRMMR highlights those genetic alterations contributing to microsatellite instability.'),
(81035, 2017, 'bio', 'ALIGNSEC: viewing protein secondary structure predictions within large multiple sequence alignments', 'Deléage, Gilbert', 'Lyon University-CNRS, LBTI-UMR5305, Institute of Biology and Chemistry of Proteins, 7 passage du Vercors, 69367 Lyon cedex 07, France.', 'France', 0, 'Computer Simulation|Protein Structure, Secondary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx521', '28961944', 'ALIGNSEC is a module within ANTHEPROT designed for the interactive display, edition and printing of large-scale multiple alignments integrating secondary structure predictions.'),
(81036, 2017, 'bio', 'Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers', 'Choi, Jonghwan|Park, Sanghyun|Yoon, Youngmi|Ahn, Jaegyoon', 'Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea.|Department of Computer Science, Yonsei University, Seoul, The Republic of Korea.|Department of Computer Engineering, Gachon University, Seongnam-si, Gyeonggi-do, The Republic of Korea.|Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea.', '-|-|-|-', 0, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Prognosis|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx487', '28961949', 'Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples.'),
(81037, 2017, 'bio', 'PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data', 'Gloaguen, Yoann|Morton, Fraser|Daly, Rxc3xb3nxc3xa1n|Gurden, Ross|Rogers, Simon|Wandy, Joe|Wilson, David|Barrett, Michael|Burgess, Karl', 'Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|School of Computing Science.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.|Institute of Infection, Immunity and Inflammation, Glasgow Polyomics.', '-|-|-|-|-|-|-|-|-', 9, 'Chromatography, Liquid|Internet|Mass Spectrometry|Metabolome|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btx499', '28961954', 'The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata, and the Metabolite Page, which provides a list of each metabolite accompanied by evidence cards, which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information.'),
(81038, 2017, 'bio', 'Real-time demultiplexing Nanopore barcoded sequencing data with npBarcode', 'Nguyen, Son Hoang|Duarte, Tania P S|Coin, Lachlan J M|Cao, Minh Duc', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia|Australia', 2, 'Algorithms|Electronic Data Processing|Nanopores|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx537', '28961965', 'The recent introduction of a barcoding protocol for Oxford Nanopore sequencing has increased the versatility of the technology. Several bioinformatics tools have been developed to demultiplex barcoded reads, but none of them supports streaming analysis. This limits the use of multiplexed sequencing in real-time applications, which is one of the main advantages of the technology.'),
(81039, 2017, 'bio', 'Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning', 'Jain, Tushar|Boland, Todd|Lilov, Asparouh|Burnina, Irina|Brown, Michael|Xu, Yingda|Vásquez, Maximiliano', 'Computational Biology, Adimab, Palo Alto, CA, USA.|Computational Biology, Adimab, Palo Alto, CA, USA.|Protein Analytics, Adimab, Lebanon, NH, USA.|Protein Analytics, Adimab, Lebanon, NH, USA.|Protein Analytics, Adimab, Lebanon, NH, USA.|Protein Analytics, Adimab, Lebanon, NH, USA.|Computational Biology, Adimab, Palo Alto, CA, USA.', 'United States|United States|Lebanon|Lebanon|Lebanon|Lebanon|United States', 0, 'Amino Acids|Antibodies, Monoclonal|Chromatography|Hydrophobic and Hydrophilic Interactions|Immunoglobulin Variable Region|Machine Learning|ROC Curve|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx519', '28961999', 'The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence.'),
(81040, 2018, 'bio', 'SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data', 'Shi, Xu|Wang, Xiao|Wang, Tian-Li|Hilakivi-Clarke, Leena|Clarke, Robert|Xuan, Jianhua', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA.|Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.|Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Alternative Splicing|Bayes Theorem|Breast Neoplasms|Cell Line|Cell Line, Tumor|Computational Biology|Female|High-Throughput Nucleotide Sequencing|Humans|Models, Biological|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx557', '28968634', 'Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81041, 2017, 'bio', 'A Utility Maximizing and Privacy Preserving Approach for Protecting Kinship in Genomic Databases', 'Kale, Gulce|Ayday, Erman|Tastan, Oznur', 'Department of Computer Engineering, Bilkent University.|Department of Computer Engineering, Bilkent University.|Department of Computer Engineering, Bilkent University.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx568', '28968635', 'Rapid and low cost sequencing of genomes enabled widespread use of genomic data in research studies and personalized customer applications, where genomic data is shared in public databases. Although the identities of the participants are anonymized in these databases, sensitive information about individuals can still be inferred. One such information is kinship.'),
(81042, 2017, 'bio', 'MAJIQ-SPEL: Web-tool to interrogate classical and complex splicing variations from RNA-Seq data', 'Green, Christopher J|Gazzara, Matthew R|Barash, Yoseph', 'Department of Genetics. Contact:yosephb@upenn.edu.|Department of Genetics. Contact:yosephb@upenn.edu.|Department of Genetics. Contact:yosephb@upenn.edu.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx565', '28968636', 'Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret, and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis.'),
(81043, 2018, 'bio', 'Dugong: a Docker image, based on Ubuntu Linux, focused on reproducibility and replicability for bioinformatics analyses', 'Menegidio, Fabiano B|Jabes, Daniela L|Costa de Oliveira, Regina|Nunes, Luiz R', 'Núcleo Integrado de Biotecnologia, Universidade de Mogi das Cruzes (UMC), 08780-911, Mogi das Cruzes, SP, Brazil.|Núcleo Integrado de Biotecnologia, Universidade de Mogi das Cruzes (UMC), 08780-911, Mogi das Cruzes, SP, Brazil.|Núcleo Integrado de Biotecnologia, Universidade de Mogi das Cruzes (UMC), 08780-911, Mogi das Cruzes, SP, Brazil.|Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), 09210-170, Santo André, SP, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 7, 'Computational Biology|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx554', '28968637', 'This manuscript introduces and describes Dugong, a Docker image based on Ubuntu 16.04, which automates installation of more than 3500 bioinformatics tools (along with their respective libraries and dependencies), in alternative computational environments. The software operates through a user-friendly XFCE4 graphic interface that allows software management and installation by users not fully familiarized with the Linux command line and provides the Jupyter Notebook to assist in the delivery and exchange of consistent and reproducible protocols and results across laboratories, assisting in the development of open science projects.'),
(81044, 2018, 'bio', 'tmVar 20: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine', 'Wei, Chih-Hsuan|Phan, Lon|Feltz, Juliana|Maiti, Rama|Hefferon, Tim|Lu, Zhiyong', 'National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Data Curation|Data Mining|Databases, Factual|Genetic Predisposition to Disease|Genomics|Humans|Mutation|Phenotype|Polymorphism, Genetic|Precision Medicine|PubMed|Publications|Software', 'Journal Article', '10.1093/bioinformatics/btx541', '28968638', 'Despite significant efforts in expert curation, clinical relevance about most of the 154 million dbSNP reference variants (RS) remains unknown. However, a wealth of knowledge about the variant biological function/disease impact is buried in unstructured literature data. Previous studies have attempted to harvest and unlock such information with text-mining techniques but are of limited use because their mutation extraction results are not standardized or integrated with curated data.'),
(81045, 2018, 'bio', 'NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis', 'Singer, Jochen|Ruscheweyh, Hans-Joachim|Hofmann, Ariane L|Thurnherr, Thomas|Singer, Franziska|Toussaint, Nora C|Ng, Charlotte K Y|Piscuoglio, Salvatore|Beisel, Christian|Christofori, Gerhard|Dummer, Reinhard|Hall, Michael N|Krek, Wilhelm|Levesque, Mitchell P|Manz, Markus G|Moch, Holger|Papassotiropoulos, Andreas|Stekhoven, Daniel J|Wild, Peter|Wüst, Thomas|Rinn, Bernd|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Department of Biomedicine, University of Basel, Basel, Switzerland.|Institute of Pathology, University Hospital Basel, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biomedicine, University of Basel, Basel, Switzerland.|Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.|Biozentrum, University of Basel, Basel, Switzerland.|Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.|Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.|Division of Hematology, University Hospital Zurich, Zurich, Switzerland.|Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland.|Division of Molecular Neuroscience, Department of Psychology.|SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland.|SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 0, 'Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Reproducibility of Results|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx540', '28968639', 'Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing.'),
(81046, 2018, 'bio', 'NavMol 30: enabling the representation of metabolic reactions by blind users', 'Binev, Yuri|Peixoto, Daniela|Pereira, Florbela|Rodrigues, Ian|Cavaco, Sofia|Lobo, Ana M|Aires-de-Sousa, João', 'LAQV-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|LAQV-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|LAQV-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|NOVA LINCS, Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|NOVA LINCS, Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|LAQV-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|LAQV-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 2, 'Blindness|Humans|Metabolic Networks and Pathways|Sensory Aids|Software', 'Journal Article', '10.1093/bioinformatics/btx556', '28968640', 'The representation of metabolic reactions strongly relies on visualization, which is a major barrier for blind users. The NavMol software renders the communication and interpretation of molecular structures and reactions accessible by integrating chemoinformatics and assistive technology. NavMol 3.0 provides a molecular editor for metabolic reactions. The user can start with templates of reactions and build from such cores. Atom-to-atom mapping enables changes in the reactants to be reflected in the products (and vice-versa) and the reaction centres to be automatically identified. Blind users can easily interact with the software using the keyboard and text-to-speech technology.'),
(81047, 2017, 'bio', 'MemBrain-contact 20: A new two-stage machine learning model for the prediction enhancement of transmembrane protein residue contacts in the full chain', 'Yang, Jing|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. Contact:hbshen@sjtu.edu.cn|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. Contact:hbshen@sjtu.edu.cn', 'China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx593', '28968641', 'Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models.'),
(81048, 2018, 'bio', 'CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data', 'Guo, Weilong|Zhu, Ping|Pellegrini, Matteo|Zhang, Michael Q|Wang, Xiangfeng|Ni, Zhongfu', 'State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China.|Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX 75080, USA.|Beijing Advanced Innovation Center for Food Nutrition and Human health, China Agricultural University, Beijing 100193, China.|State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.', 'China|China|United States|United States|China|China', 24, 'Alleles|Animals|DNA Methylation|Eukaryota|Genomics|Heterozygote|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btx595', '28968643', 'DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging.'),
(81049, 2018, 'bio', 'Phosphonormalizer: an R package for normalization of MS-based label-free phosphoproteomics', 'Saraei, Sohrab|Suomi, Tomi|Kauko, Otto|Elo, Laura L|Stegle, Oliver', 'Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and xc3x85bo Akademi University, Tykistxc3xb6katu 6, FI-20520 Turku, Finland.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and xc3x85bo Akademi University, Tykistxc3xb6katu 6, FI-20520 Turku, Finland.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and xc3x85bo Akademi University, Tykistxc3xb6katu 6, FI-20520 Turku, Finland.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and xc3x85bo Akademi University, Tykistxc3xb6katu 6, FI-20520 Turku, Finland.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and xc3x85bo Akademi University, Tykistxc3xb6katu 6, FI-20520 Turku, Finland.', 'Finland|Finland|Finland|Finland|Finland', 2, 'Mass Spectrometry|Phosphoproteins|Phosphorylation|Protein Processing, Post-Translational|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx573', '28968644', 'Global centering-based normalization is a commonly used normalization approach in mass spectrometry-based label-free proteomics. It scales the peptide abundances to have the same median intensities, based on an assumption that the majority of abundances remain the same across the samples. However, especially in phosphoproteomics, this assumption can introduce bias, as the samples are enriched during sample preparation which can mask the underlying biological changes. To address this possible bias, phosphopeptides quantified in both enriched and non-enriched samples can be used to calculate factors that mitigate the bias.'),
(81050, 2018, 'bio', 'Kfits: a software framework for fitting and cleaning outliers in kinetic measurements', 'Rimon, Oded|Reichmann, Dana', 'Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.', 'Israel|Israel', 0, 'Biochemistry|Computational Biology|Kinetics|Models, Biological|Protein Aggregates|Protein Aggregation, Pathological|Protein Folding|Proteins|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx577', '28968645', 'Kinetic measurements have played an important role in elucidating biochemical and biophysical phenomena for over a century. While many tools for analysing kinetic measurements exist, most require low noise levels in the data, leaving outlier measurements to be cleaned manually. This is particularly true for protein misfolding and aggregation processes, which are extremely noisy and hence difficult to model. Understanding these processes is paramount, as they are associated with diverse physiological processes and disorders, most notably neurodegenerative diseases. Therefore, a better tool for analysing and cleaning protein aggregation traces is required.'),
(81051, 2018, 'bio', 'Morphologically constrained and data informed cell segmentation of budding yeast', 'Bakker, Elco|Swain, Peter S|Crane, Matthew M', 'SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.|SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.|SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cell Proliferation|Image Cytometry|Image Processing, Computer-Assisted|Saccharomycetales|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx550', '28968663', 'Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance.'),
(81052, 2018, 'bio', 'Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm', 'Xie, Zhengwei|Zhang, Tianyu|Ouyang, Qi', 'Department of Pharmacology and Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University, Beijing 100191, China.|School of Life Science, Peking University, Beijing 100871, China.|Condensed Matter Physics, School of Physics, Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Enzymes|Humans|Metabolic Networks and Pathways|Models, Biological|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btx574', '28968667', 'One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks.'),
(81053, 2018, 'bio', 'Three-dimensional cardiovascular imaging-genetics: a mass univariate framework', 'Biffi, Carlo|de Marvao, Antonio|Attard, Mark I|Dawes, Timothy J W|Whiffin, Nicola|Bai, Wenjia|Shi, Wenzhe|Francis, Catherine|Meyer, Hannah|Buchan, Rachel|Cook, Stuart A|Rueckert, Daniel|ORegan, Declan P', 'Department of Computing, Imperial College London, South Kensington Campus, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.|Department of Computing, Imperial College London, South Kensington Campus, London, UK.|Department of Computing, Imperial College London, South Kensington Campus, London, UK.|Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.|Department of Computing, Imperial College London, South Kensington Campus, London, UK.|Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Female|Genetic Association Studies|Genetic Predisposition to Disease|Heart|Humans|Hypertrophy, Left Ventricular|Imaging, Three-Dimensional|Male|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx552', '28968671', 'Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D).'),
(81054, 2017, 'bio', 'IntPred: a structure-based predictor of protein-protein interaction sites', 'Northey, Tom|Barešic, Anja|Martin, Andrew C R', 'Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London WC1E 6BT. Contact:andrew@bioinf.org.uk -or- andrew.martin@ucl.ac.uk|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London WC1E 6BT. Contact:andrew@bioinf.org.uk -or- andrew.martin@ucl.ac.uk|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London WC1E 6BT. Contact:andrew@bioinf.org.uk -or- andrew.martin@ucl.ac.uk', '-|-|-', 15, '-', 'Journal Article', '10.1093/bioinformatics/btx585', '28968673', 'Protein-protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein-protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein-protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.'),
(81055, 2018, 'bio', 'Identification and visualization of differential isoform expression in RNA-seq time series', 'Nueda, María José|Martorell-Marugan, Jordi|Martí, Cristina|Tarazona, Sonia|Conesa, Ana', 'Mathematics Department, University of Alicante, Alicante 03690, Spain.|Genomics of Gene Expression Laboratory, Centro de Investigación Príncipe Felipe, Valencia 42012, Spain.|Genomics of Gene Expression Laboratory, Centro de Investigación Príncipe Felipe, Valencia 42012, Spain.|Genomics of Gene Expression Laboratory, Centro de Investigación Príncipe Felipe, Valencia 42012, Spain.|Genomics of Gene Expression Laboratory, Centro de Investigación Príncipe Felipe, Valencia 42012, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Animals|B-Lymphocytes|Cell Differentiation|Gene Expression Profiling|Gene Expression Regulation|Mice|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx578', '28968682', 'As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data.'),
(81056, 2018, 'bio', 'Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples', 'Wilks, Christopher|Gaddipati, Phani|Nellore, Abhinav|Langmead, Ben', 'Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States', 7, 'Exons|High-Throughput Nucleotide Sequencing|Humans|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx547', '28968689', 'As more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. These enable researchers to leverage vast datasets that would otherwise be difficult to obtain.'),
(81057, 2017, 'bio', 'regNet: An R package for network-based propagation of gene expression alterations', 'Seifert, Michael|Beyer, Andreas', 'Carl Gustav Carus Faculty of Medicine, Institute for Medical Informatics and Biometry (IMB), Technische Universität Dresden, Fetscherstr. 74, D-01307, Dresden, Germany. Contact:michael.seifert@tu-dresden.de.|Cellular Networks and Systems Biology, CECAD, University of Cologne, Joseph-Stelzmann-Str. 26, D-50931, Cologne, Germany.', 'Germany|Germany', 5, '-', 'Journal Article', '10.1093/bioinformatics/btx544', '28968690', 'Gene expression alterations and potentially underlying gene copy number mutations can be measured routinely in the wet lab, but it is still extremely challenging to quantify impacts of altered genes on clinically relevant characteristics to predict putative driver genes. We developed the R package regNet that utilizes gene expression and copy number data to learn regulatory networks for the quantification of potential impacts of individual gene expression alterations on user-defined target genes via network propagation. We demonstrate the value of regNet by identifying putative major regulators that distinguish pilocytic from diffuse astrocytomas and by predicting putative impacts of glioblastoma-specific gene copy number alterations on cell cycle pathway genes and patient survival.'),
(81058, 2018, 'bio', 'Interactive network visualization in Jupyter notebooks: visJS2jupyter', 'Rosenthal, Sara Brin|Len, Julia|Webster, Mikayla|Gary, Aaron|Birmingham, Amanda|Fisch, Kathleen M', 'Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Autistic Disorder|Computational Biology|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Protein Interaction Maps|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx581', '28968701', 'Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research.'),
(81059, 2017, 'bio', 'SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles', 'Papili Gao, Nan|Ud-Dean, S M Minhaz|Gandrillon, Olivier|Gunawan, Rudiyanto', 'Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. Contact:rudi.gunawan@chem.ethz.ch|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.|Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. Contact:rudi.gunawan@chem.ethz.ch', 'Switzerland|United States|United States|Switzerland', 16, '-', 'Journal Article', '10.1093/bioinformatics/btx575', '28968704', 'Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.'),
(81060, 2017, 'bio', 'LinkageMapView - Rendering High Resolution Linkage and QTL Maps', 'Ouellette, Lisa A|Reid, Robert W|Blanchard, Steven G|Brouwer, Cory R', 'Department of Bioinformatics and Genomics, University of North Carolina Charlotte, 150 Research Campus Drive, Suite 3333, Kannapolis, NC, USA. Contact:louellet@uncc.edu|Department of Bioinformatics and Genomics, University of North Carolina Charlotte, 150 Research Campus Drive, Suite 3333, Kannapolis, NC, USA. Contact:louellet@uncc.edu|Department of Bioinformatics and Genomics, University of North Carolina Charlotte, 150 Research Campus Drive, Suite 3333, Kannapolis, NC, USA. Contact:louellet@uncc.edu|Department of Bioinformatics and Genomics, University of North Carolina Charlotte, 150 Research Campus Drive, Suite 3333, Kannapolis, NC, USA. Contact:louellet@uncc.edu', 'United States|United States|United States|United States', 17, '-', 'Journal Article', '10.1093/bioinformatics/btx576', '28968706', 'Linkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions.'),
(81061, 2018, 'bio', 'An automated tool for obtaining QSAR-ready series of compounds using semantic web technologies', 'López-Massaguer, Oriol|Sanz, Ferran|Pastor, Manuel', 'Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar dInvestigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar dInvestigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar dInvestigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain.', 'Spain|Spain|Spain', 1, 'Computational Biology|Quantitative Structure-Activity Relationship|Semantic Web|Software', 'Journal Article', '10.1093/bioinformatics/btx566', '28968713', 'We describe an application (Collector) for obtaining series of compounds annotated with bioactivity data, ready to be used for the development of quantitative structure-activity relationships (QSAR) models. The tool extracts data from the Open Pharmacological Space (OPS) developed by the Open PHACTS project, using as input a valid name of the biological target. Collector uses the OPS ontologies for expanding the query using all known target synonyms and extracts compounds with bioactivity data against the target from multiple sources. The extracted data can be filtered to retain only drug-like compounds and the bioactivities can be automatically summarised to assign a single value per compound, yielding data ready to be used for QSAR modeling. The data obtained is locally stored facilitating the traceability and auditability of the process. Collector was used successfully for the development of models for toxicity endpoints within the eTOX project.'),
(81062, 2018, 'bio', 'FATHMM-XF: accurate prediction of pathogenic point mutations via extended features', 'Rogers, Mark F|Shihab, Hashem A|Mort, Matthew|Cooper, David N|Gaunt, Tom R|Campbell, Colin', 'Intelligent Systems Laboratory, University of Bristol, Bristol BS8?1UB, UK.|MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8?2BN, UK.|Institute of Medical Genetics, Cardiff University, Cardiff CF14?4XN, UK.|Institute of Medical Genetics, Cardiff University, Cardiff CF14?4XN, UK.|MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8?2BN, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol BS8?1UB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 63, 'Genome, Human|Genomics|Humans|Point Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx536', '28968714', 'We present FATHMM-XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM-XF outperforms competitors on benchmark tests, particularly in non-coding regions where the majority of pathogenic mutations are likely to be found.'),
(81063, 2018, 'bio', 'LightDock: a new multi-scale approach to protein-protein docking', 'Jiménez-García, Brian|Roel-Touris, Jorge|Romero-Durana, Miguel|Vidal, Miquel|Jiménez-González, Daniel|Fernández-Recio, Juan', 'Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 16, 'Computational Biology|Ligands|Molecular Docking Simulation|Protein Binding|Protein Conformation|Proteins|Software|Tryptophan Synthase', 'Journal Article', '10.1093/bioinformatics/btx555', '28968719', 'Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed.'),
(81064, 2017, 'bio', 'CCFold: rapid and accurate prediction of coiled-coil structures and application to modeling intermediate filaments', 'Guzenko, Dmytro|Strelkov, Sergei V', 'Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven 3000, Belgium.Contact:sergei.strelkov@kuleuven.be.|Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven 3000, Belgium.Contact:sergei.strelkov@kuleuven.be.', 'Belgium|Belgium', 3, '-', 'Journal Article', '10.1093/bioinformatics/btx551', '28968723', 'Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the effect of disease-related IF mutations. The dimer contains a ~300-residue long xcexb1-helical coiled coil which cannot be assessed by either direct experimental structure determination or modelling using standard approaches. At the same time, coiled coils are well-represented in structural databases.'),
(81065, 2017, 'bio', 'Meta-server for automatic analysis, scoring and ranking of docking models', 'Anashkina, Anastasia A|Kravatsky, Yuri|Kuznetsov, Eugene|Makarov, Alexander A|Adzhubei, Alexei A', 'Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia. Contact:alexei.adzhubei@eimb.ru.|Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia. Contact:alexei.adzhubei@eimb.ru.|V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia.|Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia. Contact:alexei.adzhubei@eimb.ru.|Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia. Contact:alexei.adzhubei@eimb.ru.', '-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx591', '28968724', 'Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling.'),
(81066, 2018, 'bio', 'GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking', 'Kyeong, Hyun-Ho|Choi, Yoonjoo|Kim, Hak-Sung', 'Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.|Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.|Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.', '-|-|-', 7, 'Animals|Computational Biology|Histocompatibility Antigens Class I|Humans|Molecular Docking Simulation|Peptides|Protein Binding|Software', 'Journal Article', '10.1093/bioinformatics/btx589', '28968726', 'The identification of T-cell epitopes has many profound translational applications in the areas of transplantation, disease diagnosis, vaccine/therapeutic protein development and personalized immunotherapy. While data-driven methods have been widely used for the prediction of peptide binders with notable successes, the structural modeling of peptide binding to MHC molecules is crucial for understanding the underlying molecular mechanism of the immunological processes.'),
(81067, 2017, 'bio', 'MetExploreViz: web component for interactive metabolic network visualization', 'Chazalviel, Maxime|Frainay, Clément|Poupin, Nathalie|Vinson, Florence|Merlet, Benjamin|Gloaguen, Yoann|Cottret, Ludovic|Jourdan, Fabien', 'Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.|Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.|Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.|Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.|Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.|Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Gascube Campus, Bearsden, G61 1QH, Scotland.|LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France.|Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France. Contact:contact- metexplore@inra.fr.', 'France|France|France|France|France|-|France|France', 12, '-', 'Journal Article', '10.1093/bioinformatics/btx588', '28968733', 'MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyze omics data in a biochemical context.'),
(81068, 2017, 'bio', 'NetProphet 20: Mapping Transcription Factor Networks by Exploiting Scalable Data Resources', 'Kang, Yiming|Liow, Hien-Haw|Maier, Ezekiel J|Brent, Michael R', 'Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University. One Brookings Dr. Saint Louis, MO 63108. Contact:brent@wustl.edu.|Department of Mathematics, Washington University.|Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University. One Brookings Dr. Saint Louis, MO 63108. Contact:brent@wustl.edu.|Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University. One Brookings Dr. Saint Louis, MO 63108. Contact:brent@wustl.edu.', '-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/btx563', '28968736', 'Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and integrative algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro . However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.'),
(81069, 2017, 'bio', 'SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome', 'Vera Alvarez, Roberto|Li, Shan|Landsman, David|Ovcharenko, Ivan', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA. Contact:ovcharen@nih.gov.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA. Contact:ovcharen@nih.gov.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA. Contact:ovcharen@nih.gov.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA. Contact:ovcharen@nih.gov.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btx583', '28968739', 'Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning, and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resource available for the broader functional genomics and population genetics communities, we developed SNPDelScore, which uses a panel of proposed methods for quantifying deleterious effects of noncoding mutations to precompute and compare the deleteriousness scores of all common SNPs in the human genome in 44 cell lines. The panel of deleteriousness scores of a SNP computed using different methods is supplemented by functional information from the GWAS Catalog, libraries of transcription factor binding sites, and genic characteristics of mutations. SNPDelScore comes with a genome browser capable of displaying and comparing large sets of SNPs in a genomic locus and rapidly identifying consensus SNPs with the highest deleteriousness scores making those prime candidates for phenotype-causal polymorphisms.'),
(81070, 2017, 'bio', 'Kmer-SSR: a fast and exhaustive SSR search algorithm', 'Pickett, Brandon D|Miller, Justin B|Ridge, Perry G', 'Department of Biology, BYU, Provo, UT 84602, USA.|Department of Biology, BYU, Provo, UT 84602, USA.|Department of Biology, BYU, Provo, UT 84602, USA.', 'United States|United States|United States', 6, 'Algorithms|Computational Biology|Microsatellite Repeats|Software', 'Journal Article', '10.1093/bioinformatics/btx538', '28968741', 'One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a good enough solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a good enough solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions.'),
(81071, 2018, 'bio', 'LDassoc: an online tool for interactively exploring genome-wide association study results and prioritizing variants for functional investigation', 'Machiela, Mitchell J|Chanock, Stephen J', 'Department of Biology, BYU, Provo, UT 84602, USA.|Department of Biology, BYU, Provo, UT 84602, USA.', 'United States|United States', 38, 'Databases, Genetic|Genome-Wide Association Study|Genomics|Humans|Linkage Disequilibrium|Polymorphism, Genetic|Population Groups|Software', 'Journal Article', '10.1093/bioinformatics/btx561', '28968746', 'Existing approaches to plot association results from genome-wide association studies (GWAS) are in the form of static Manhattan plots and often lack data integration with rich databases on variant regulatory potential as well as population-specific linkage disequilibrium patterns.'),
(81072, 2017, 'bio', 'RUCS: rapid identification of PCR primers for unique core sequences', 'Thomsen, Martin Christen Frølund|Hasman, Henrik|Westh, Henrik|Kaya, Hülya|Lund, Ole', 'Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.|Department of Microbiology and Infection Control, Statens Serum Institut, Copenhagen, Denmark.|Department of Clinical Microbiology, Hvidovre Hospital, Copenhagen, Denmark.|Department of Microbiology and Infection Control, Statens Serum Institut, Copenhagen, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 4, 'Base Sequence|DNA Primers|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btx526', '28968748', 'Designing PCR primers to target a specific selection of whole genome sequenced strains can be a long, arduous and sometimes impractical task. Such tasks would benefit greatly from an automated tool to both identify unique targets, and to validate the vast number of potential primer pairs for the targets in silico.');
