INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81628, 2018, 'bio', 'pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks', 'Budach, Stefan|Marsico, Annalisa', 'Otto-Warburg-Laboratory, RNA Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Otto-Warburg-Laboratory, RNA Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany', 14, 'Binding Sites|Machine Learning|Neural Networks (Computer)|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty222', '29659719', 'Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines of code. Using an RNA A-to-I editing dataset and cross-linking immunoprecipitation (CLIP)-seq binding site sequences, we demonstrate that pysster classifies sequences with higher accuracy than previous methods, such as GraphProt or ssHMM, and is able to recover known sequence and structure motifs.'),
(81629, 2018, 'bio', 'MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms', 'Yoshizoe, Kazuki|Terada, Aika|Tsuda, Koji', 'Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.|Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan.', 'Japan|Japan|Japan', 3, 'Algorithms|Cloud Computing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/bty219', '29659720', 'Exhaustive detection of multi-loci markers from genome-wide association study datasets is a computationally challenging problem. This paper presents a massively parallel algorithm for finding all significant combinations of alleles and introduces a software tool termed MP-LAMP that can be easily deployed in a cloud platform, such as Amazon Web Service, as well as in an in-house computer cluster. Multi-loci marker detection is an unbalanced tree search problem that cannot be parallelized by simple tree-splitting using generic parallel programming frameworks, such as Map-Reduce. We employ work stealing and periodic reduce-broadcast to decrease the running time almost linearly to the number of cores.'),
(81630, 2018, 'bio', 'JAMI: fast computation of conditional mutual information for ceRNA network analysis', 'Hornakova, Andrea|List, Markus|Vreeken, Jilles|Schulz, Marcel H', 'Max Planck Institute for Informatics, Saarland University, Saarland Informatics Campus, Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Saarland University, Saarland Informatics Campus, Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Saarland University, Saarland Informatics Campus, Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Saarland University, Saarland Informatics Campus, Saarbrxc3xbccken, Germany.', 'Germany|Germany|Germany|Germany', 4, 'Gene Regulatory Networks|RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty221', '29659721', 'Genome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale.'),
(81631, 2018, 'bio', 'Efficient population-scale variant analysis and prioritization with VAPr', 'Birmingham, Amanda|Mark, Adam M|Mazzaferro, Carlo|Xu, Guorong|Fisch, Kathleen M', 'Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States', 1, 'Computational Biology|Exome|Genomics|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/bty192', '29659724', 'With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies.'),
(81632, 2018, 'bio', 'CaSiAn: a Calcium Signaling Analyzer tool', 'Moein, Mahsa|Grzyb, Kamil|Gonçalves Martins, Teresa|Komoto, Shinya|Peri, Francesca|Crawford, Alexander D|Fouquier dHerouel, Aymeric|Skupin, Alexander', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.|Okinawa Institute of Science and Technology Graduate University, Onna, Japan.|Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux L-4367, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Japan|Germany|Luxembourg|Luxembourg|Luxembourg', 2, 'Calcium Signaling|Humans|Software', 'Journal Article', '10.1093/bioinformatics/bty281', '29668830', 'Ca2+ is a central second messenger in eukaryotic cells that regulates many cellular processes. Recently, we have indicated that typical Ca2+ signals are not purely oscillatory as widely assumed, but exhibit stochastic spiking with cell type and pathway specific characteristics. Here, we present the Calcium Signaling Analyzer (CaSiAn), an open source software tool that allows for quantifying these signal characteristics including individual spike properties and time course statistics in a semi-automated manner. CaSiAn provides an intuitive graphical user interface allowing experimentalists to easily process a large amount of Ca2+ signals, interactively tune peak detection, revise statistical measures and access the quantified signal properties as excel or text files.'),
(81633, 2018, 'bio', 'Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation', 'Maziarz, Marlena|Pfeiffer, Ruth M|Wan, Yunhu|Gail, Mitchell H', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty297', '29668831', 'Comparisons of microbiome communities across populations are often based on pairwise distance measures (beta-diversity). Standard analyses (principal coordinate plots, permutation tests, kernel methods) require access to primary data if another investigator wants to add or compare independent data. We propose using standard reference measurements to simplify microbiome beta-diversity analyses, to make them more transparent, and to facilitate independent validation and comparisons across studies.'),
(81634, 2018, 'bio', 'MethylMix 20: an R package for identifying DNA methylation genes', 'Cedoz, Pierre-Louis|Prunello, Marcos|Brennan, Kevin|Gevaert, Olivier', 'Stanford Center for Biomedical Informatics, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, USA.|Department of Statistics, College of Pharmaceutical and Biochemical Sciences, National University of Rosario, S2000CGK Rosario, Argentina.|Stanford Center for Biomedical Informatics, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, USA.', 'United States|Argentina|United States|United States', 18, 'Algorithms|Cluster Analysis|DNA|DNA Methylation|Genome|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/bty156', '29668835', 'DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes. Here we present a new version of MethylMix that automates the construction of DNA-methylation and gene expression datasets from The Cancer Genome Atlas (TCGA). More precisely, MethylMix 2.0 incorporates two major updates: the automated downloading of DNA methylation and gene expression datasets from TCGA and the automated preprocessing of such datasets: value imputation, batch correction and CpG sites clustering within each gene. The resulting datasets can subsequently be analyzed with MethylMix to identify transcriptionally predictive methylation states. We show that the Differential Methylation Values created by MethylMix can be used for cancer subtyping.'),
(81635, 2018, 'bio', 'GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models', 'Clarke, Thomas H|Brinkac, Lauren M|Sutton, Granger|Fouts, Derrick E', 'J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.', 'United States|United States|United States|United States', 0, 'Cluster Analysis|Genome|Genomics|Normal Distribution|Software', 'Journal Article', '10.1093/bioinformatics/bty300', '29668840', 'The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes.'),
(81636, 2018, 'bio', 'GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS', 'Ravasio, Viola|Ritelli, Marco|Legati, Andrea|Giacopuzzi, Edoardo', 'Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.|Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.|Unit of Molecular Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.|Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.', 'Italy|Italy|Italy|Italy', 6, 'Deep Learning|Genomics|High-Throughput Nucleotide Sequencing|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bty303', '29668842', 'Exome sequencing approach is extensively used in research and diagnostic laboratories to discover pathological variants and study genetic architecture of human diseases. However, a significant proportion of identified genetic variants are actually false positive calls, and this pose serious challenge for variants interpretation. Here, we propose a new tool named Genomic vARiants FIltering by dEep Learning moDels in NGS (GARFIELD-NGS), which rely on deep learning models to dissect false and true variants in exome sequencing experiments performed with Illumina or ION platforms. GARFIELD-NGS showed strong performances for both SNP and INDEL variants (AUC 0.71-0.98) and outperformed established hard filters. The method is robust also at low coverage down to 30X and can be applied on data generated with the recent Illumina two-colour chemistry. GARFIELD-NGS processes standard VCF file and produces a regular VCF output. Thus, it can be easily integrated in existing analysis pipeline, allowing application of different thresholds based on desired level of sensitivity and specificity.'),
(81637, 2018, 'bio', 'DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters', 'Taju, Semmy Wellem|Nguyen, Trinh-Trung-Duong|Le, Nguyen-Quoc-Khanh|Kusuma, Rosdyana Mangir Irawan|Ou, Yu-Yen', 'Department of Computer Science & Engineering, Yuan Ze University, Chungli, Taiwan.|Department of Computer Science & Engineering, Yuan Ze University, Chungli, Taiwan.|Department of Computer Science & Engineering, Yuan Ze University, Chungli, Taiwan.|Department of Computer Science & Engineering, Yuan Ze University, Chungli, Taiwan.|Department of Computer Science & Engineering, Yuan Ze University, Chungli, Taiwan.', '-|-|-|-|-', 0, 'Algorithms|Machine Learning|Neural Networks (Computer)|Position-Specific Scoring Matrices|Protein Transport|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty302', '29668844', 'Efflux protein plays a key role in pumping xenobiotics out of the cells. The prediction of efflux family proteins involved in transport process of compounds is crucial for understanding family structures, functions and energy dependencies. Many methods have been proposed to classify efflux pump transporters without considerations of any pump specific of efflux protein families. In other words, efflux proteins protect cells from extrusion of foreign chemicals. Moreover, almost all efflux protein families have the same structure based on the analysis of significant motifs. The motif sequences consisting of the same amount of residues will have high degrees of residue similarity and thus will affect the classification process. Consequently, it is challenging but vital to recognize the structures and determine energy dependencies of efflux protein families. In order to efficiently identify efflux protein families with considering about pump specific, we developed a 2xe2x80x89D convolutional neural network (2xe2x80x89D CNN) model called DeepEfflux. DeepEfflux tried to capture the motifs of sequences around hidden target residues to use as hidden features of families. In addition, the 2xe2x80x89D CNN model uses a position-specific scoring matrix (PSSM) as an input. Three different datasets, each for one family of efflux protein, was fed into DeepEfflux, and then a 5-fold cross validation approach was used to evaluate the training performance.'),
(81638, 2018, 'bio', 'AnnotSV: an integrated tool for structural variations annotation', 'Geoffroy, Vxc3xa9ronique|Herenger, Yvan|Kress, Arnaud|Stoetzel, Corinne|Piton, Amxc3xa9lie|Dollfus, Hxc3xa9lxc3xa8ne|Muller, Jean', 'Laboratoire de Gxc3xa9nxc3xa9tique Mxc3xa9dicale, UMR_S INSERM U1112, IGMA, Facultxc3xa9 de Mxc3xa9decine FMTS, Universitxc3xa9 de Strasbourg, Strasbourg, France.|Service de Gxc3xa9nxc3xa9tique Mxc3xa9dicale, CHU de Tours, Tours, France.|ICUBE UMR 7357, Complex Systems and Translational Bioinformatics (CSTB), Universitxc3xa9 de Strasbourg-CNRS-FMTS, Strasbourg, France.|Laboratoire de Gxc3xa9nxc3xa9tique Mxc3xa9dicale, UMR_S INSERM U1112, IGMA, Facultxc3xa9 de Mxc3xa9decine FMTS, Universitxc3xa9 de Strasbourg, Strasbourg, France.|Laboratoires de Diagnostic Gxc3xa9nxc3xa9tique, Institut de Gxc3xa9nxc3xa9tique Mxc3xa9dicale dAlsace (IGMA), Hxc3xb4pitaux Universitaires de Strasbourg, Strasbourg Cedex, France.|Laboratoire de Gxc3xa9nxc3xa9tique Mxc3xa9dicale, UMR_S INSERM U1112, IGMA, Facultxc3xa9 de Mxc3xa9decine FMTS, Universitxc3xa9 de Strasbourg, Strasbourg, France.|Laboratoire de Gxc3xa9nxc3xa9tique Mxc3xa9dicale, UMR_S INSERM U1112, IGMA, Facultxc3xa9 de Mxc3xa9decine FMTS, Universitxc3xa9 de Strasbourg, Strasbourg, France.', 'France|France|France|France|France|France|France', 17, 'Gene Frequency|Genome, Human|Genomics|Humans|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/bty304', '29669011', 'Structural Variations (SV) are a major source of variability in the human genome that shaped its actual structure during evolution. Moreover, many human diseases are caused by SV, highlighting the need to accurately detect those genomic events but also to annotate them and assist their biological interpretation. Therefore, we developed AnnotSV that compiles functionally, regulatory and clinically relevant information and aims at providing annotations useful to (i) interpret SV potential pathogenicity and (ii) filter out SV potential false positive. In particular, AnnotSV reports heterozygous and homozygous counts of single nucleotide variations (SNVs) and small insertions/deletions called within each SV for the analyzed patients, this genomic information being extremely useful to support or question the existence of an SV. We also report the computed allelic frequency relative to overlapping variants from DGV (MacDonald et al., 2014), that is especially powerful to filter out common SV. To delineate the strength of AnnotSV, we annotated the 4751 SV from one sample of the 1000 Genomes Project, integrating the sample information of four million of SNV/indel, in less than 60xe2x80x89s.'),
(81639, 2018, 'bio', 'CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling', 'Peng, Hui|Zheng, Yi|Blumenstein, Michael|Tao, Dacheng|Li, Jinyan', 'Faculty of Engineering and Information Technology, Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW, Australia.|Faculty of Engineering and Information Technology, Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW, Australia.|Faculty of Engineering and Information Technology, Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW, Australia.|Faculty of Engineering and Information Technologies, School of Information Technologies, University of Sydney, Darlington, NSW, Australia.|Faculty of Engineering and Information Technology, Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Animals|CRISPR-Cas Systems|Humans|Mice|RNA, Guide|Regression Analysis|Software|Support Vector Machine|Zebrafish', 'Journal Article', '10.1093/bioinformatics/bty298', '29672669', 'CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single-guide RNAs (sgRNAs), such that its cleavage efficiency is high meanwhile the off-target effect is low.'),
(81640, 2018, 'bio', 'TRUmiCount: correctly counting absolute numbers of molecules using unique molecular identifiers', 'Pflug, Florian G|von Haeseler, Arndt', 'Center for Integrative Bioinformatics Vienna (CIBIV), Joint Institute of the University of Vienna and Medicial University of Vienna, Max F. Perutz Laboratories (MFPL), Vienna, Austria.|Center for Integrative Bioinformatics Vienna (CIBIV), Joint Institute of the University of Vienna and Medicial University of Vienna, Max F. Perutz Laboratories (MFPL), Vienna, Austria.', 'Austria|Austria', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty283', '29672674', 'Counting molecules using next-generation sequencing (NGS) suffers from PCR amplification bias, which reduces the accuracy of many quantitative NGS-based experimental methods such as RNA-Seq. This is true even if molecules are made distinguishable using unique molecular identifiers (UMIs) before PCR amplification, and distinct UMIs are counted instead of reads: Molecules that are lost entirely during the sequencing process will still cause underestimation of the molecule count, and amplification artifacts like PCR chimeras create phantom UMIs and thus cause over-estimation.'),
(81641, 2018, 'bio', 'Parapred: antibody paratope prediction using convolutional and recurrent neural networks', 'Liberis, Edgar|Velickovic, Petar|Sormanni, Pietro|Vendruscolo, Michele|Liò, Pietro', 'Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.|Department of Chemistry, University of Cambridge, UK.|Department of Chemistry, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Antibodies|Binding Sites, Antibody|Deep Learning|Machine Learning|Models, Molecular|Neural Networks (Computer)', 'Journal Article', '10.1093/bioinformatics/bty305', '29672675', 'Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope).'),
(81642, 2018, 'bio', 'annoPeak: a web application to annotate and visualize peaks from ChIP-seq/ChIP-exo-seq', 'Tang, Xing|Srivastava, Arunima|Liu, Huayang|Machiraju, Raghu|Huang, Kun|Leone, Gustavo', 'Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.|Department of Computer Science and Technology, University of Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty226', '29672705', '-'),
(81643, 2018, 'bio', 'BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates', 'Goren, Emily|Liu, Peng|Wang, Chao|Wang, Chong', 'Department of Statistics, Iowa State University, Ames, IA, USA.|Department of Statistics, Iowa State University, Ames, IA, USA.|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, USA.|Department of Statistics, Iowa State University, Ames, IA, USA.', 'United States|United States|United States|United States', 0, 'High-Throughput Nucleotide Sequencing|Humans|Linear Models|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty227', '29684098', 'ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level.'),
(81644, 2018, 'bio', 'Comparative analysis of tissue reconstruction algorithms for 3D histology', 'Kartasalo, Kimmo|Latonen, Leena|Vihinen, Jorma|Visakorpi, Tapio|Nykter, Matti|Ruusuvuori, Pekka', 'Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Engineering Sciences, Tampere University of Technology, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 8, 'Algorithms|Histological Techniques|Imaging, Three-Dimensional', 'Comparative Study', '10.1093/bioinformatics/bty210', '29684099', 'Digital pathology enables new approaches that expand beyond storage, visualization or analysis of histological samples in digital format. One novel opportunity is 3D histology, where a three-dimensional reconstruction of the sample is formed computationally based on serial tissue sections. This allows examining tissue architecture in 3D, for example, for diagnostic purposes. Importantly, 3D histology enables joint mapping of cellular morphology with spatially resolved omics data in the true 3D context of the tissue at microscopic resolution. Several algorithms have been proposed for the reconstruction task, but a quantitative comparison of their accuracy is lacking.'),
(81645, 2018, 'bio', 'iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC', 'Liu, Bin|Weng, Fan|Huang, De-Shuang|Chou, Kuo-Chen', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.|Gordon Life Science Institute, Belmont, MA, USA.', 'China|China|China|United States', 0, 'Ascomycota|DNA|DNA Replication|Fungal Proteins|Replication Origin|Software', 'Journal Article', '10.1093/bioinformatics/bty312', '29684124', 'DNA replication is the key of the genetic information transmission, and it is initiated from the replication origins. Identifying the replication origins is crucial for understanding the mechanism of DNA replication. Although several discriminative computational predictors were proposed to identify DNA replication origins of yeast species, they could only be used to identify very tiny parts (250 or 300 bp) of the replication origins. Besides, none of the existing predictors could successfully capture the GC asymmetry bias of yeast species reported by experimental observations. Hence it would not be surprising why their power is so limited. To grasp the CG asymmetry feature and make the prediction able to cover the entire replication regions of yeast species, we develop a new predictor called iRO-3wPseKNC.'),
(81646, 2018, 'bio', 'Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping', 'Raimondi, Daniele|Orlando, Gabriele|Moreau, Yves|Vranken, Wim F', 'Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|ESAT-STADIUS, KU Leuven, Leuven, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 1, 'Algorithms|Amino Acid Sequence|Data Compression|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Proteins|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty309', '29684140', 'Evolutionary information is crucial for the annotation of proteins in bioinformatics. The amount of retrieved homologs often correlates with the quality of predicted protein annotations related to structure or function. With a growing amount of sequences available, fast and reliable methods for homology detection are essential, as they have a direct impact on predicted protein annotations.'),
(81647, 2018, 'bio', 'MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites', 'Hinshaw, Samuel J|Lee, Amy H Y|Gill, Erin E|Hancock, Robert E W', 'Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada.|Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada.|Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada.|Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Humans|Metabolic Networks and Pathways|Metabolomics|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bty331', '29688253', 'Here, we present MetaBridge, a tool that collates protein interactors (curated metabolite-enzyme interactions) that influence the levels of specific metabolites including both biosynthetic and degradative enzymes. This enables network-based integrative analysis of metabolomics data with other omics data types. MetaBridge is designed to complement a systems-biology approach to analysis, pairing well with network analysis tools such as NetworkAnalyst.ca, but can be used in any bioinformatics workflow.'),
(81648, 2018, 'bio', 'Bayesian negative binomial regression for differential expression with confounding factors', 'Dadaneh, Siamak Zamani|Zhou, Mingyuan|Qian, Xiaoning', 'Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX, USA.|Department of Information, Risk, and Operations Management, The University of Texas at Austin, Austin, TX, USA.|Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States', 4, 'Bayes Theorem|High-Throughput Nucleotide Sequencing|Humans|Research Design|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty330', '29688254', 'Rapid adoption of high-throughput sequencing technologies has enabled better understanding of genome-wide molecular profile changes associated with phenotypic differences in biomedical studies. Often, these changes are due to multiple interacting factors. Existing methods are mostly considering differential expression across two conditions studying one main factor without considering other confounding factors. In addition, they are often coupled with essential sophisticated ad-hoc pre-processing steps such as normalization, restricting their adaptability to general experimental setups. Complex multi-factor experimental design to accurately decipher genotype-phenotype relationships signifies the need for developing effective statistical tools for genome-scale sequencing data profiled under multi-factor conditions.'),
(81649, 2018, 'bio', 'CTDquerier: a bioconductor R package for Comparative Toxicogenomics DatabaseTM data extraction, visualization and enrichment of environmental and toxicological studies', 'Hernandez-Ferrer, Carles|Gonzalez, Juan R', 'Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.|Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.', 'Spain|Spain', 0, 'Data Visualization|Humans|Software|Toxicogenetics', 'Journal Article', '10.1093/bioinformatics/bty326', '29688259', 'Biomedical studies currently include a large volume of genomic and environmental factors for studying the etiology of human diseases. R/Bioconductor projects provide several tools for performing enrichment analysis at gene-pathway level, allowing researchers to develop novel hypotheses. However, there is a need to perform similar analyses at the chemicals-genes or chemicals-diseases levels to provide complementary knowledge of the causal path between chemicals and diseases. While the Comparative Toxicogenomics DatabaseTM (CTD) provides information about these relationships, there is no software for integrating it into R/Bioconductor analysis pipelines. CTDquerier helps users to easily download CTD data and integrate it in the R/Bioconductor framework. The package also contains functions for visualizing CTD data and performing enrichment analyses. We illustrate how to use the package with a real data analysis of asthma-related genes. CTDquerier is a flexible and easy-to-use Bioconductor package that provides novel hypothesis about the relationships between chemicals and diseases.'),
(81650, 2018, 'bio', 'DEsingle for detecting three types of differential expression in single-cell RNA-seq data', 'Miao, Zhun|Deng, Ke|Wang, Xiaowo|Zhang, Xuegong', 'MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.|Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.', 'China|China|China|China', 0, 'Humans|Models, Statistical|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty332', '29688277', 'The excessive amount of zeros in single-cell RNA-seq (scRNA-seq) data includes real zeros due to the on-off nature of gene transcription in single cells and dropout zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect three types of DE genes in scRNA-seq data with higher accuracy.'),
(81651, 2018, 'bio', 'Two-phase differential expression analysis for single cell RNA-seq', 'Wu, Zhijin|Zhang, Yi|Stitzel, Michael L|Wu, Hao', 'Department of Biostatistics, Brown University, Providence, RI, USA.|Department of Biostatistics, Brown University, Providence, RI, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.', 'United States|United States|United States|United States', 8, 'Gene Expression Regulation|Humans|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty329', '29688282', 'Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of differential expression. However, most computational methods still stick with the old mentality of viewing differential expression as a simple up or down phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations.'),
(81652, 2018, 'bio', 'PathwaySplice: an R package for unbiased pathway analysis of alternative splicing in RNA-Seq data', 'Yan, Aimin|Ban, Yuguang|Gao, Zhen|Chen, Xi|Wang, Lily', 'Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.|Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.|Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.|Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.|Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.', 'United States|United States|United States|United States|United States', 0, 'Alternative Splicing|Gene Ontology|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty317', '29688305', 'Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the significant gene list in alternative splicing. We present PathwaySplice, an R package that (i) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene, (ii) visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression, (iii) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets, (iv) identifies the significant genes driving pathway significance and (v) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph.'),
(81653, 2018, 'bio', 'EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy', 'Zhou, Xianxiao|Wang, Minghui|Katsyv, Igor|Irie, Hanna|Zhang, Bin', 'Department of Genetics and Genomic Sciences, 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.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Division of Hematology and Medical Oncology, Department of Medicine, 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', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty325', '29688306', 'Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods.'),
(81654, 2018, 'bio', 'RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines', 'Basu, Amrita|Mitra, Ritwik|Liu, Han|Schreiber, Stuart L|Clemons, Paul A', 'Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.|Operational Research and Financial Engineering, Princeton University, Princeton, NJ, USA.|Operational Research and Financial Engineering, Princeton University, Princeton, NJ, USA.|Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.|Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Antineoplastic Agents|Cell Line, Tumor|Elasticity|Genomics|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/bty199', '29688307', 'In recent years there have been several efforts to generate sensitivity profiles of collections of genomically characterized cell lines to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based on cellular, genetic, or expression biomarkers of cancer cells. However, a remaining challenge is an efficient way to identify accurate sets of biomarkers to validate. To address this challenge, we developed methodology using gene-expression profiles of human cancer cell lines to predict the responses of these cell lines to a panel of compounds.'),
(81655, 2018, 'bio', 'RANGER-DTL 20: rigorous reconstruction of gene-family evolution by duplication, transfer and loss', 'Bansal, Mukul S|Kellis, Manolis|Kordi, Misagh|Kundu, Soumya', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Duplication|Software', 'Journal Article', '10.1093/bioinformatics/bty314', '29688310', 'RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++xe2x80x89and Python.'),
(81656, 2018, 'bio', 'Automatic selection of verification tools for efficient analysis of biochemical models', 'Bakir, Mehmet Emin|Konur, Savas|Gheorghe, Marian|Krasnogor, Natalio|Stannett, Mike', 'Department of Computer Science, University of Sheffield, Sheffield, UK.|School of Electrical Engineering & Computer Science, University of Bradford, Bradford, UK.|School of Electrical Engineering & Computer Science, University of Bradford, Bradford, UK.|Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle, UK.|Department of Computer Science, University of Sheffield, Sheffield, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty282', '29688313', 'Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed, however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest.'),
(81657, 2018, 'bio', 'Cluster Locator, online analysis and visualization of gene clustering', 'Pazos Obregón, Flavio|Soto, Pablo|Lavín, José Luis|Cortázar, Ana Rosa|Barrio, Rosa|Aransay, Ana María|Cantera, Rafael', 'Developmental Neurobiology, IIBCE, Montevideo, Uruguay.|Developmental Neurobiology, IIBCE, Montevideo, Uruguay.|Genome Analysis Platform, Derio, Spain.|Genome Analysis Platform, Derio, Spain.|Functional Genomics Unit, CIC bioGUNE, Derio, Spain.|Genome Analysis Platform, Derio, Spain.|Developmental Neurobiology, IIBCE, Montevideo, Uruguay.', 'Uruguay|Uruguay|Spain|Spain|Spain|Spain|Uruguay', 0, 'Cluster Analysis|Eukaryota|Genome|Multigene Family|Software', 'Journal Article', '10.1093/bioinformatics/bty336', '29701747', 'Genes sharing functions, expression patterns or quantitative traits are not randomly distributed along eukaryotic genomes. In order to study the distribution of genes that share a given feature, we present Cluster Locator, an online analysis and visualization tool. Cluster Locator determines the number, size and position of all the clusters formed by the protein-coding genes on a list according to a given maximum gap, the percentage of gene clustering of the list and its statistical significance. The output includes a visual representation of the distribution of genes and gene clusters along the reference genome.'),
(81658, 2018, 'bio', 'BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction', 'Chen, Xing|Xie, Di|Wang, Lei|Zhao, Qi|You, Zhu-Hong|Liu, Hongsheng', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Mathematics, Liaoning University, Shenyang, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Mathematics, Liaoning University, Shenyang, China.|Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi, China.|Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China.', 'China|China|China|China|China|China', 126, '-', 'Journal Article', '10.1093/bioinformatics/bty333', '29701758', 'A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are expensive and time-consuming. Therefore, the computational methods to predict potential miRNA-disease associations have been paid increasing attention.'),
(81659, 2018, 'bio', 'Predicting gene structure changes resulting from genetic variants via exon definition features', 'Majoros, William H|Holt, Carson|Campbell, Michael S|Ware, Doreen|Yandell, Mark|Reddy, Timothy E', 'Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.|Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA.|Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty324', '29701825', 'Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81660, 2018, 'bio', 'Deep reinforcement learning of cell movement in the early stage of Celegans embryogenesis', 'Wang, Zi|Wang, Dali|Li, Chengcheng|Xu, Yichi|Li, Husheng|Bao, Zhirong', 'Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.|Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.|Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA.|Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty323', '29701853', 'Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images.'),
(81661, 2018, 'bio', 'GapRepairer: a server to model a structural gap and validate it using topological analysis', 'Jarmolinska, Aleksandra I|Kadlof, Michal|Dabrowski-Tumanski, Pawel|Sulkowska, Joanna I', 'Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty334', '29718096', 'Over 25% of protein structures possess unresolved fragments. On the other hand, approximately 6% of protein chains have non-trivial topology (and form knots, slipknots, lassos and links). As the topology is fundamental for the proper function of proteins, modeling of topologically correct structures is decisive in various fields, including biophysics, biotechnology and molecular biology. However, none of the currently existing tools take into account the topology of the model and those which could be modified to include topology, demand experience in bioinformatics, protein topology and knot theory.'),
(81662, 2018, 'bio', 'RBind: computational network method to predict RNA binding sites', 'Wang, Kaili|Jian, Yiren|Wang, Huiwen|Zeng, Chen|Zhao, Yunjie', 'Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, China.|Department of Physics, The George Washington University, Washington, DC, USA.|Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, China.|Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, China.|Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, China.', 'China|United States|China|China|China', 5, 'Algorithms|Binding Sites|Computational Biology|Humans|Protein Domains|Proteins|RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty345', '29718097', 'Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions.'),
(81663, 2018, 'bio', 'TMCrys: predict propensity of success for transmembrane protein crystallization', 'Varga, Julia K|Tusnády, Gábor E', 'Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Center of Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Center of Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.', 'Hungary|Hungary', 2, 'Crystallization|Humans|Membrane Proteins|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/bty342', '29718100', 'Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine--the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service.'),
(81664, 2018, 'bio', 'MetaboDiff: an R package for differential metabolomic analysis', 'Mock, Andreas|Warta, Rolf|Dettling, Steffen|Brors, Benedikt|Jäger, Dirk|Herold-Mende, Christel', 'Division of Experimental Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.|Division of Experimental Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.|Division of Experimental Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.|German Cancer Consortium (DKTK), Heidelberg, Germany.|German Cancer Consortium (DKTK), Heidelberg, Germany.|Division of Experimental Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty344', '29718102', 'Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network.'),
(81665, 2018, 'bio', 'A powerful approach reveals numerous expression quantitative trait haplotypes in multiple tissues', 'Ying, Dingge|Li, Mulin Jun|Sham, Pak Chung|Li, Miaoxin', 'Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.', 'China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty318', '29718103', 'Recently many studies showed single nucleotide polymorphisms (SNPs) affect gene expression and contribute to development of complex traits/diseases in a tissue context-dependent manner. However, little is known about haplotypes influence on gene expression and complex traits, which reflects the interaction effect between SNPs.'),
(81666, 2018, 'bio', 'Exact Bayesian inference for phylogenetic birth-death models', 'Parag, Kris V|Pybus, Oliver G', 'Department of Zoology, University of Oxford, Oxford, UK.|Department of Zoology, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty337', '29718104', 'Inferring the rates of change of a population from a reconstructed phylogeny of genetic sequences is a central problem in macro-evolutionary biology, epidemiology and many other disciplines. A popular solution involves estimating the parameters of a birth-death process (BDP), which links the shape of the phylogeny to its birth and death rates. Modern BDP estimators rely on random Markov chain Monte Carlo (MCMC) sampling to infer these rates. Such methods, while powerful and scalable, cannot be guaranteed to converge, leading to results that may be hard to replicate or difficult to validate.'),
(81667, 2018, 'bio', 'Quantification of biases in predictions of protein stability changes upon mutations', 'Pucci, Fabrizio|Bernaerts, Katrien V|Kwasigroch, Jean Marc|Rooman, Marianne', 'Department of BioModeling BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.|Department of BioModeling BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.|Department of BioModeling BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.|Department of BioModeling BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 18, '-', 'Journal Article', '10.1093/bioinformatics/bty348', '29718106', 'Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations.'),
(81668, 2018, 'bio', 'GenomicScores: seamless access to genomewide position-specific scores from R and Bioconductor', 'Puigdevall, Pau|Castelo, Robert', 'Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.|Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.', 'Spain|Spain', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty311', '29718111', 'Genomewide position-specific scores, such as those estimating conservation, constraint, fitness or mutation tolerance, are ubiquitous in current genome analyses. The diversity of sources and formats of these scores, as well as their size, increase the burden to use them. We present GenomicScores, a Bioconductor package that provides efficient storage and seamless access of genomewide position-specific scores from R, facilitating their use in genome analysis workflows.'),
(81669, 2018, 'bio', 'High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features', 'Jones, David T|Kandathil, Shaun M', 'Department of Computer Science, University College London, London, UK.|Department of Computer Science, University College London, London, UK.', 'United Kingdom|United Kingdom', 35, '-', 'Journal Article', '10.1093/bioinformatics/bty341', '29718112', 'In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation.'),
(81670, 2018, 'bio', 'Prediction of lncRNA-disease associations based on inductive matrix completion', 'Lu, Chengqian|Yang, Mengyun|Luo, Feng|Wu, Fang-Xiang|Li, Min|Pan, Yi|Li, Yaohang|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha, Peoples Republic of China.|School of Information Science and Engineering, Central South University, Changsha, Peoples Republic of China.|School of Computing, Clemson University, Clemson, SC, USA.|Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan, Canada.|School of Information Science and Engineering, Central South University, Changsha, Peoples Republic of China.|Department of Computer Science, Georgia State University, Atlanta, GA, USA.|Department of Computer Science, Old Dominion University, Norfolk, VA, USA.|School of Information Science and Engineering, Central South University, Changsha, Peoples Republic of China.', 'China|China|United States|Canada|China|Georgia|United States|China', 60, 'Algorithms|Humans|Kidney Neoplasms|RNA, Long Noncoding|Software|Stomach Neoplasms', 'Journal Article', '10.1093/bioinformatics/bty327', '29718113', 'Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA-disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA-disease associations, especially for novel lncRNAs, remains challenging.'),
(81671, 2018, 'bio', 'MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA-disease association', 'Yang, Yang|Fu, Xiaofeng|Qu, Wenhao|Xiao, Yiqun|Shen, Hong-Bin', 'Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|China', 14, 'Humans|Intracellular Space|MicroRNAs|Phenotype|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/bty343', '29718114', 'Benefiting from high-throughput experimental technologies, whole-genome analysis of microRNAs (miRNAs) has been more and more common to uncover important regulatory roles of miRNAs and identify miRNA biomarkers for disease diagnosis. As a complementary information to the high-throughput experimental data, domain knowledge like the Gene Ontology and KEGG pathway is usually used to guide gene function analysis. However, functional annotation for miRNAs is scarce in the public databases. Till now, only a few methods have been proposed for measuring the functional similarity between miRNAs based on public annotation data, and these methods cover a very limited number of miRNAs, which are not applicable to large-scale miRNA analysis.'),
(81672, 2018, 'bio', 'Efficient flexible backbone protein-protein docking for challenging targets', 'Marze, Nicholas A|Roy Burman, Shourya S|Sheffler, William|Gray, Jeffrey J', 'Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.|Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.|Department of Biochemistry, University of Washington, Seattle, WA, USA.|Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States', 16, '-', 'Journal Article', '10.1093/bioinformatics/bty355', '29718115', 'Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates.'),
(81673, 2018, 'bio', 'D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information', 'Dang, Thanh Hai|Le, Hoang-Quynh|Nguyen, Trang M|Vu, Sinh T', 'Department of Computational Science and Engineering, Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam.|Knowledge Technology Laboratory (KTLab), Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam.|Department of Computational Science and Engineering, Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam.|Department of Computational Science and Engineering, Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam.', '-|-|-|-', 21, 'Benchmarking|Humans|Linguistics|Molecular Sequence Annotation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty356', '29718118', 'Recognition of biomedical named entities in the textual literature is a highly challenging research topic with great interest, playing as the prerequisite for extracting huge amount of high-valued biomedical knowledge deposited in unstructured text and transforming them into well-structured formats. Long Short-Term Memory (LSTM) networks have recently been employed in various biomedical named entity recognition (NER) models with great success. They, however, often did not take advantages of all useful linguistic information and still have many aspects to be further improved for better performance.'),
(81674, 2018, 'bio', 'ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis', 'Wang, Anliang|Yan, Xiaolong|Wei, Zhijun', 'Division of Marine Disaster Forecasting and Warning, National Marine Environmental Forecasting Center, Haidian District, Beijing, China.|Division of Marine Disaster Forecasting and Warning, National Marine Environmental Forecasting Center, Haidian District, Beijing, China.|State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China.', 'China|China|China', 7, 'Algorithms|Image Interpretation, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/bty313', '29718132', 'This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.'),
(81675, 2018, 'bio', 'hts-nim: scripting high-performance genomic analyses', 'Pedersen, Brent S|Quinlan, Aaron R', 'Department of Human Genetics, Department of Biomedical Informatics, and USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA.|Department of Human Genetics, Department of Biomedical Informatics, and USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty358', '29718142', 'Extracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets.'),
(81676, 2018, 'bio', 'lncFunTK: a toolkit for functional annotation of long noncoding RNAs', 'Zhou, Jiajian|Huang, Yile|Ding, Yingzhe|Yuan, Jie|Wang, Huating|Sun, Hao', 'Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.', 'China|China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty339', '29718162', 'Thousands of long noncoding RNAs (lncRNAs) were newly identified from high throughput RNA-seq data. Functional annotation and prioritization of these lncRNAs for further experimental validation as well as the functional investigation is the bottleneck step for many noncoding RNA studies.'),
(81677, 2018, 'bio', 'BlockFeST: Bayesian calculation of region-specific FST to detect local adaptation', 'Pfeifer, Bastian|Lercher, Martin J', 'Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany.|Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty299', '29718170', 'The fixation index FST can be used to identify non-neutrally evolving loci from genome-scale SNP data across two or more populations. Recent years have seen the development of sophisticated approaches to estimate FST based on Markov-Chain Monte-Carlo simulations. Here, we present a vectorized R implementation of an extension of the widely used BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing SNPs that evolved under directional selection.'),
(81678, 2018, 'bio', 'JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions', 'Deng, Wenping|Zhang, Kui|Liu, Sanzhen|Zhao, Patrick X|Xu, Shizhong|Wei, Hairong', 'School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA.|Department of Mathematics, Michigan Technological University, Houghton, MI, USA.|Department of Plant Pathology, Kansas State University, Manhattan, KS, USA.|Plant Biology Division, Bioinformatics and Computational Biology Lab, Noble Research Institute, Ardmore, OK, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty354', '29718177', 'Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges.'),
(81679, 2018, 'bio', 'Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code', 'Goz, Eli|Zafrir, Zohar|Tuller, Tamir', 'Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.|Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.|Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty351', '29718236', 'Understanding how viruses co-evolve with their hosts and adapt various genomic level strategies in order to ensure their fitness may have essential implications in unveiling the secrets of viral evolution, and in developing new vaccines and therapeutic approaches. Here, based on a novel genomic analysis of 2625 different viruses and 439 corresponding host organisms, we provide evidence of universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code.'),
(81680, 2018, 'bio', 'Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition', 'Wu, Han-Ming|Tien, Yin-Jing|Ho, Meng-Ru|Hwu, Hai-Gwo|Lin, Wen-Chang|Tao, Mi-Hua|Chen, Chun-Houh', 'Department of Statistics, National Taipei University, New Taipei City, Taiwan, R.O.C.|Digital Transformation Institute, Institute for Information Industry, Taipei, Taiwan, R.O.C.|Digital Transformation Institute, Institute for Information Industry, Taipei, Taiwan, R.O.C.|Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, R.O.C.|Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, R.O.C.|Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, R.O.C.|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, R.O.C.', '-|-|-|-|-|-|-', 1, 'Computational Biology|Female|Humans|Male|Software', 'Journal Article', '10.1093/bioinformatics/bty335', '29718246', 'Heatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high-dimensional large-scale datasets effectively without dimension reduction. The benefit of the proposed covariate-adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV.'),
(81681, 2018, 'bio', 'Improved enzyme annotation with EC-specific cutoffs using DETECT v2', 'Nursimulu, Nirvana|Xu, Leon L|Wasmuth, James D|Krukov, Ivan|Parkinson, John', 'Program in Molecular Medicine, The Hospital for Sick Children, 21-9709 PGCRL, 686 Bay Street, Toronto, ON, Canada.|Program in Molecular Medicine, The Hospital for Sick Children, 21-9709 PGCRL, 686 Bay Street, Toronto, ON, Canada.|Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.|Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.|Program in Molecular Medicine, The Hospital for Sick Children, 21-9709 PGCRL, 686 Bay Street, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty368', '29722785', 'We present DETECT v2-an enzyme annotation tool which considers the effect of sequence diversity when assigning enzymatic function [as an Enzyme Commission (EC) number] to a protein sequence. In addition to capturing more enzyme classes than the previous version, we now provide EC-specific cutoffs that greatly increase precision and recall of assignments and show its performance in the context of pathways.'),
(81682, 2018, 'bio', 'polymapR-linkage analysis and genetic map construction from F1 populations of outcrossing polyploids', 'Bourke, Peter M|van Geest, Geert|Voorrips, Roeland E|Jansen, Johannes|Kranenburg, Twan|Shahin, Arwa|Visser, Richard G F|Arens, Paul|Smulders, Marinus J M|Maliepaard, Chris', 'Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Biometris, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.|Plant Breeding, Wageningen University & Research, Wageningen, PB, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 18, '-', 'Journal Article', '10.1093/bioinformatics/bty371', '29722786', 'Polyploid species carry more than two copies of each chromosome, a condition found in many of the worlds most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realize all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops.'),
(81683, 2018, 'bio', 'AlphaMate: a program for optimizing selection, maintenance of diversity and mate allocation in breeding programs', 'Gorjanc, Gregor|Hickey, John M', 'The Roslin Institute, Royal Dick School of Veterinary Studies, The University of Edinburgh, Scotland, UK.|The Roslin Institute, Royal Dick School of Veterinary Studies, The University of Edinburgh, Scotland, UK.', 'United Kingdom|United Kingdom', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty375', '29722792', 'AlphaMate is a flexible program that optimizes selection, maintenance of genetic diversity and mate allocation in breeding programs. It can be used in animal and cross- and self-pollinating plant populations. These populations can be subject to selective breeding or conservation management. The problem is formulated as a multi-objective optimization of a valid mating plan that is solved with an evolutionary algorithm. A valid mating plan is defined by a combination of mating constraints (the number of matings, the maximal number of parents, the minimal/equal/maximal number of contributions per parent, or allowance for selfing) that are gender specific or generic. The optimization can maximize genetic gain, minimize group coancestry, minimize inbreeding of individual matings, or maximize genetic gain for a given increase in group coancestry or inbreeding. Users provide a list of candidate individuals with associated gender and selection criteria information (if applicable) and coancestry matrix. Selection criteria and coancestry matrix can be based on pedigree or genome-wide markers. Additional individual or mating specific information can be included to enrich optimization objectives. An example of rapid recurrent genomic selection in wheat demonstrates how AlphaMate can double the efficiency of converting genetic diversity into genetic gain compared to truncation selection. Another example demonstrates the use of genome editing to expand the gain-diversity frontier.'),
(81684, 2018, 'bio', 'Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation', 'Usmanova, Dinara R|Bogatyreva, Natalya S|Ariño Bernad, Joan|Eremina, Aleksandra A|Gorshkova, Anastasiya A|Kanevskiy, German M|Lonishin, Lyubov R|Meister, Alexander V|Yakupova, Alisa G|Kondrashov, Fyodor A|Ivankov, Dmitry N', 'Department of Systems Biology, Columbia University Medical Center, New York, NY, USA.|Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Spain.|Centre de Formació Interdisciplinària Superior, Universitat Politècnica de Catalunya, Barcelona, Spain.|School of Biological Sciences, College of Science and Engineering, University of Edinburgh, Edinburgh, UK.|Biological Faculty, Lomonosov Moscow State University, Moscow, Russia.|Higher Chemical College of the Russian Academy of Sciences, Moscow, Russia.|Faculty of Technical Physics, Institute of Physics, Nanotechnology and Telecommunications, Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russia.|Department of Medicine, Novosibirsk State University, Novosibirsk, Russia.|Biological Faculty, Lomonosov Moscow State University, Moscow, Russia.|Institute of Science and Technology, Klosterneuburg, Austria.|Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, Pushchino, Moscow Region, Russia.', 'United Kingdom|Spain|Spain|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Austria|United Kingdom', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty340', '29722803', 'Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations.'),
(81685, 2018, 'bio', 'The UEA sRNA Workbench (version 44): a comprehensive suite of tools for analyzing miRNAs and sRNAs', 'Stocks, Matthew B|Mohorianu, Irina|Beckers, Matthew|Paicu, Claudia|Moxon, Simon|Thody, Joshua|Dalmay, Tamas|Moulton, Vincent', 'School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'MicroRNAs|RNA Interference|RNA, Small Interfering|Sequence Analysis, RNA|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/bty338', '29722807', 'RNA interference, a highly conserved regulatory mechanism, is mediated via small RNAs (sRNA). Recent technical advances enabled the analysis of larger, complex datasets and the investigation of microRNAs and the less known small interfering RNAs. However, the size and intricacy of current data requires a comprehensive set of tools, able to discriminate the patterns from the low-level, noise-like, variation, numerous and varied suggestions from the community represent an invaluable source of ideas for future tools, the ability of the community to contribute to this software is essential.'),
(81686, 2018, 'bio', 'Knoto-ID: a tool to study the entanglement of open protein chains using the concept of knotoids', 'Dorier, Julien|Goundaroulis, Dimos|Benedetti, Fabrizio|Stasiak, Andrzej', 'Vital-IT, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.|Vital-IT, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty365', '29722808', 'The backbone of most proteins forms an open curve. To study their entanglement, a common strategy consists in searching for the presence of knots in their backbones using topological invariants. However, this approach requires to close the curve into a loop, which alters the geometry of curve. Knoto-ID allows evaluating the entanglement of open curves without the need to close them, using the recent concept of knotoids which is a generalization of the classical knot theory to open curves. Knoto-ID can analyse the global topology of the full chain as well as the local topology by exhaustively studying all subchains or only determining the knotted core. Knoto-ID permits to localize topologically non-trivial protein folds that are not detected by informatics tools detecting knotted protein folds.'),
(81687, 2018, 'bio', 'Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks', 'Pan, Xiaoyong|Shen, Hong-Bin', 'Department of Medical informatics, Erasmus Medical Center, CE Rotterdam, The Netherlands.|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, China.', 'Netherlands|China', 39, '-', 'Journal Article', '10.1093/bioinformatics/bty364', '29722865', 'RNA-binding proteins (RBPs) take over 5-10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using patterns learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process.'),
(81688, 2018, 'bio', 'Modeling one thousand intron length distributions with fitild', 'Gotoh, Osamu', 'Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, Koto-ku, Tokyo, Japan.', 'Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty353', '29722882', 'Intron length distribution (ILD) is a specific feature of a genome that exhibits extensive species-specific variation. Whereas ILD contributes to up to 30% of the total information content for intron recognition in some species, rendering it an important component of computational gene prediction, very few studies have been conducted to quantitatively characterize ILDs of various species.'),
(81689, 2018, 'bio', 'Applying graph theory to protein structures: an Atlas of coiled coils', 'Heal, Jack W|Bartlett, Gail J|Wood, Christopher W|Thomson, Andrew R|Woolfson, Derek N', 'School of Chemistry, University of Bristol, Bristol, UK.|School of Chemistry, University of Bristol, Bristol, UK.|School of Chemistry, University of Bristol, Bristol, UK.|School of Chemistry, University of Bristol, Bristol, UK.|School of Chemistry, University of Bristol, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty347', '29722888', 'To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Here, we use tools from graph theory to define an Atlas classification scheme for automatically categorizing certain protein substructures.'),
(81690, 2018, 'bio', 'Sorting cancer karyotypes using double-cut-and-joins, duplications and deletions', 'Zeira, Ron|Shamir, Ron', 'School of Chemistry, University of Bristol, Bristol, UK.|Blavatnik School of Computer Science, Tel Aviv university, Tel Aviv, 6997801, Israel.', 'United Kingdom|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty381', '29726899', 'Problems of genome rearrangement are central in both evolution and cancer research. Most genome rearrangement models assume that the genome contains a single copy of each gene and the only changes in the genome are structural, i.e., reordering of segments. In contrast, tumor genomes also undergo numerical changes such as deletions and duplications, and thus the number of copies of genes varies. Dealing with unequal gene content is a very challenging task, addressed by few algorithms to date. More realistic models are needed to help trace genome evolution during tumorigenesis.'),
(81691, 2018, 'bio', 'Edge-group sparse PCA for network-guided high dimensional data analysis', 'Min, Wenwen|Liu, Juan|Zhang, Shihua', 'School of Computer Science, Wuhan University, Wuhan, China.|School of Computer Science, Wuhan University, Wuhan, China.|NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty362', '29726900', 'Principal component analysis (PCA) has been widely used to deal with high-dimensional gene expression data. In this study, we proposed an Edge-group Sparse PCA (ESPCA) model by incorporating the group structure from a prior gene network into the PCA framework for dimension reduction and feature interpretation. ESPCA enforces sparsity of principal component (PC) loadings through considering the connectivity of gene variables in the prior network. We developed an alternating iterative algorithm to solve ESPCA. The key of this algorithm is to solve a new k-edge sparse projection problem and a greedy strategy has been adapted to address it. Here we adopted ESPCA for analyzing multiple gene expression matrices simultaneously. By incorporating prior knowledge, our method can overcome the drawbacks of sparse PCA and capture some gene modules with better biological interpretations.'),
(81692, 2018, 'bio', 'MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers', 'Minami, Shintaro|Sawada, Kengo|Ota, Motonori|Chikenji, George', 'Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan.|Department of Applied Physics, Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan.|Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan.|Department of Applied Physics, Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan.', 'Japan|Japan|Japan|Japan', 1, 'Algorithms|Amino Acid Sequence|Protein Multimerization|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty369', '29726907', 'Protein structure alignment is a significant tool to understand evolutionary processes and physicochemical properties of proteins. Important targets of structure alignment are not only monomeric but also oligomeric proteins that sometimes include domain swapping or fusions. Although various protein structural alignment programs have been developed, no method is applicable to any protein pair regardless of the number of chain components and oligomeric states with retaining sequential restrictions: structurally equivalent regions must be aligned in the same order along protein sequences.'),
(81693, 2018, 'bio', 'VarExp: estimating variance explained by genome-wide GxE summary statistics', 'Laville, Vincent|Bentley, Amy R|Privé, Florian|Zhu, Xiaofeng|Gauderman, Jim|Winkler, Thomas W|Province, Mike|Rao, D C|Aschard, Hugues', 'Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.|Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, MD, USA.|Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.|Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.|Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.|Division of Statistical Genetics, Washington University in St. Louis, St. Louis, MO, USA.|Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.|Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.', 'France|United States|France|United States|United States|Germany|United States|United States|France', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty379', '29726908', 'Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, VarExp, that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users.'),
(81694, 2018, 'bio', 'Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading', 'Rahn, Renxc3xa9|Budach, Stefan|Costanza, Pascal|Ehrhardt, Marcel|Hancox, Jonny|Reinert, Knut', 'Department of Mathematics and Computer Science, Freie Universitxc3xa4t Berlin, Berlin, Germany.|Otto-Warburg-Laboratory, RNA Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany.|ExaScience Lab, IMEC, Leuven, Belgium.|Department of Mathematics and Computer Science, Freie Universitxc3xa4t Berlin, Berlin, Germany.|Health & Life Sciences, Intel Corporation, London, UK.|Department of Mathematics and Computer Science, Freie Universitxc3xa4t Berlin, Berlin, Germany.', 'Germany|Germany|Belgium|Germany|United Kingdom|Germany', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty380', '29726911', 'Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (single instruction multiple data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we (a) distribute many independent alignments on multiple threads and (b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal.'),
(81695, 2018, 'bio', 'NGScloud: RNA-seq analysis of non-model species using cloud computing', 'Mora-Márquez, Fernando|Vázquez-Poletti, José Luis|López de Heredia, Unai', 'GI Genética, Fisiología e Historia Forestal, Dpto. Sistemas y Recursos Naturales, ETSI Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, Spain.|GI Arquitectura de Sistemas Distribuidos, Dpto. Arquitectura de Computadores y Automática, Facultad de Informática, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid, Spain.|GI Genética, Fisiología e Historia Forestal, Dpto. Sistemas y Recursos Naturales, ETSI Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, Spain.', 'Spain|Spain|Spain', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty363', '29726914', 'RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazons hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81696, 2018, 'bio', 'canvasDesigner: a versatile interactive high-resolution scientific multi-panel visualization toolkit', 'Zhang, Baohong|Zhao, Shanrong|Neuhaus, Isaac', 'Early Clinical Development, Pfizer Inc., Cambridge, MA, USA.|Early Clinical Development, Pfizer Inc., Cambridge, MA, USA.|Computational Genomics, Bristol-Myers Squibb Company, Pennington, NJ, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty377', '29726919', 'We present a bioinformatics and systems biology visualization toolkit harmonizing real time interactive exploring and analyzing of big data, full-fledged customizing of look-n-feel and producing multi-panel publication-ready figures in PDF format simultaneously.'),
(81697, 2018, 'bio', 'Recombinational DSBs-intersected genes converge on specific disease- and adaptability-related pathways', 'Yang, Zhi-Kai|Luo, Hao|Zhang, Yanming|Wang, Baijing|Gao, Feng', 'Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.|SinoGenoMax Co., Ltd./Chinese National Human Genome Center, Beijing, China.|SinoGenoMax Co., Ltd./Chinese National Human Genome Center, Beijing, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty376', '29726921', 'The budding yeast Saccharomyces cerevisiae is a model species powerful for studying the recombination of eukaryotes. Although many recombination studies have been performed for this species by experimental methods, the population genomic study based on bioinformatics analyses is urgently needed to greatly increase the range and accuracy of recombination detection. Here, we carry out the population genomic analysis of recombination in S.cerevisiae to reveal the potential rules between recombination and evolution in eukaryotes.'),
(81698, 2018, 'bio', 'MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data', 'Khan, Waqasuddin|Varma Saripella, Ganapathi|Ludwig, Thomas|Cuppens, Tania|Thibord, Florian|Génin, Emmanuelle|Deleuze, Jean-Francois|Trégouët, David-Alexandre', 'Sorbonne Universités, UPMC Université Paris 06, INSERM UMR_S 1166, Paris, France.|Sorbonne Universités, UPMC Université Paris 06, INSERM UMR_S 1166, Paris, France.|INSERM U1078, Génétique, Génomique Fonctionnelle et Biotechnologies, Université de Bretagne Occidentale, CHU Brest, Brest, France.|INSERM U1078, Génétique, Génomique Fonctionnelle et Biotechnologies, Université de Bretagne Occidentale, CHU Brest, Brest, France.|Sorbonne Universités, UPMC Université Paris 06, INSERM UMR_S 1166, Paris, France.|INSERM U1078, Génétique, Génomique Fonctionnelle et Biotechnologies, Université de Bretagne Occidentale, CHU Brest, Brest, France.|Centre National de Recherche en Génomique Humaine (CNRGH), Direction de la Recherche Fondamentale, CEA, Institut de Biologie François Jacob, Evry, France.|Sorbonne Universités, UPMC Université Paris 06, INSERM UMR_S 1166, Paris, France.', 'France|France|France|France|France|France|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty382', '29726922', 'Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon. Applied to the whole exome dataset of 573 individuals, MACARON identifies 114 situations where multiple SNVs within a genetic codon induce an amino acid change that is different from those predicted by standard single SNV annotation tool. Such events are not uncommon and deserve to be studied in sequencing projects with inconclusive findings.'),
(81699, 2018, 'bio', 'PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence', 'Iqbal, Sumaiya|Hoque, Md Tamjidul', 'Computer Science, University of New Orleans, New Orleans, LA, USA.|Computer Science, University of New Orleans, New Orleans, LA, USA.', 'United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty352', '29726965', 'Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study.'),
(81700, 2018, 'bio', 'Distinguishing prognostic and predictive biomarkers: an information theoretic approach', 'Sechidis, Konstantinos|Papangelou, Konstantinos|Metcalfe, Paul D|Svensson, David|Weatherall, James|Brown, Gavin', 'School of Computer Science, University of Manchester, Manchester, UK.|School of Computer Science, University of Manchester, Manchester, UK.|Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Cambridge, UK.|Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Cambridge, UK.|Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Cambridge, UK.|School of Computer Science, University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Biomarkers|Humans|Precision Medicine|Prognosis', 'Journal Article', '10.1093/bioinformatics/bty357', '29726967', 'The identification of biomarkers to support decision-making is central to personalized medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies, and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework.'),
(81701, 2018, 'bio', 'CAVER Analyst 20: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories', 'Jurcik, Adam|Bednar, David|Byska, Jan|Marques, Sergio M|Furmanova, Katarina|Daniel, Lukas|Kokkonen, Piia|Brezovsky, Jan|Strnad, Ondrej|Stourac, Jan|Pavelka, Antonin|Manak, Martin|Damborsky, Jiri|Kozlikova, Barbora', 'Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Visualization Group, Department of Informatics, University of Bergen, Bergen, Norway.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.|Department of Computer Graphics and Design, Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic.', '-|-|Norway|-|-|-|-|-|-|-|-|-|-|-', 38, 'Algorithms|Molecular Dynamics Simulation|Protein Conformation|Protein Engineering|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty386', '29741570', 'Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications.'),
(81702, 2018, 'bio', 'ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms proteomes', 'Razban, Rostam M|Gilson, Amy I|Durfee, Niamh|Strobelt, Hendrik|Dinkla, Kasper|Choi, Jeong-Mo|Pfister, Hanspeter|Shakhnovich, Eugene I', 'Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA.|Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA.|Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA.|School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA.|School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA.|Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA.|School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA.|Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Amino Acid Sequence|Carrier Proteins|Escherichia coli|Escherichia coli Proteins|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty370', '29741573', 'Protein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the Saccharomyces cerevisiae and Escherichia coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution are studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.'),
(81703, 2018, 'bio', 'REGGAE: a novel approach for the identification of key transcriptional regulators', 'Kehl, Tim|Schneider, Lara|Kattler, Kathrin|Stöckel, Daniel|Wegert, Jenny|Gerstner, Nico|Ludwig, Nicole|Distler, Ute|Schick, Markus|Keller, Ulrich|Tenzer, Stefan|Gessler, Manfred|Walter, Jörn|Keller, Andreas|Graf, Norbert|Meese, Eckart|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.|Department of Genetics, Saarland University, Saarbrücken D-66041, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.|Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Würzburg University, Würzburg, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.|Department of Human Genetics, Medical School, Saarland University, Homburg, Germany.|Institute for Immunology, Johannes Gutenberg University Mainz, Mainz, Germany.|Department of Internal Medicine III, School of Medicine, Technische Universität München, Munich, Germany.|Department of Internal Medicine III, School of Medicine, Technische Universität München, Munich, Germany.|Institute for Immunology, Johannes Gutenberg University Mainz, Mainz, Germany.|Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Würzburg University, Würzburg, Germany.|Department of Genetics, Saarland University, Saarbrücken D-66041, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.|Department of Pediatric Oncology and Hematology, Medical School, Saarland University, Homburg, Germany.|Department of Human Genetics, Medical School, Saarland University, Homburg, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken D-66041, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 0, 'Female|Gene Expression Regulation|Humans|Neoplasms|Probability|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bty372', '29741575', 'Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes.'),
(81704, 2018, 'bio', 'Jwalk and MNXL web server: model validation using restraints from crosslinking mass spectrometry', 'Bullock, Joshua M A|Thalassinos, Konstantinos|Topf, Maya', 'Institute for Structural and Molecular Biology, Birkbeck College, University of London, London, UK.|Institute for Structural and Molecular Biology, Birkbeck College, University of London, London, UK.|Institute for Structural and Molecular Biology, Birkbeck College, University of London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Macromolecular Substances|Mass Spectrometry|Proteins|Software|Solvents', 'Journal Article', '10.1093/bioinformatics/bty366', '29741581', 'Crosslinking Mass Spectrometry generates restraints that can be used to model proteins and protein complexes. Previously, we have developed two methods, to help users achieve better modelling performance from their crosslinking restraints: Jwalk, to estimate solvent accessible distances between crosslinked residues and MNXL, to assess the quality of the models based on these distances.'),
(81705, 2018, 'bio', 'PROBE: analysis and visualization of protein block-level evolution', 'Kress, Arnaud|Lecompte, Odile|Poch, Olivier|Thompson, Julie D', 'Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg, Strasbourg, France.', 'France|France|France|France', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty367', '29741582', 'Comparative studies of protein sequences are widely used in evolutionary and comparative genomics studies, but there is a lack of efficient tools to identify conserved regions ab initio within a protein multiple alignment. PROBE provides a fully automatic analysis of protein family conservation, to identify conserved regions, or blocks, that may correspond to structural/functional domains or motifs. Conserved blocks are identified at two different levels: (i) family level blocks indicate sites that are probably of central importance to the proteins structure or function, and (ii) sub-family level blocks highlight regions that may signify functional specialization, such as binding partners, etc. All conserved blocks are mapped onto a phylogenetic tree and can also be visualized in the context of the multiple sequence alignment. PROBE thus facilitates in-depth studies of sequence-structure-function-evolution relationships, and opens the way to block-level phylogenetic profiling.'),
(81706, 2018, 'bio', 'LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification', 'Filatov, Gleb|Bauwens, Bruno|Kertész-Farkas, Attila', 'Faculty of Computer Science, Department of Data Analysis and Artificial Intelligence, Moscow, Russia.|Faculty of Computer Science, Department of Big Data and Information Retrieval, Moscow, Russia.|Faculty of Computer Science, Department of Data Analysis and Artificial Intelligence, Moscow, Russia.', '-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty349', '29741583', 'Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis.'),
(81707, 2018, 'bio', 'Two C++ libraries for counting trees on a phylogenetic terrace', 'Biczok, R|Bozsoky, P|Eisenmann, P|Ernst, J|Ribizel, T|Scholz, F|Trefzer, A|Weber, F|Hamann, M|Stamatakis, A', 'Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty384', '29746618', 'The presence of terraces in phylogenetic tree space, i.e. a potentially large number of distinct tree topologies that have exactly the same analytical likelihood score, was first described by Sanderson et al. However, popular software tools for maximum likelihood and Bayesian phylogenetic inference do not yet routinely report, if inferred phylogenies reside on a terrace, or not. We believe, this is due to the lack of an efficient library to (i) determine if a tree resides on a terrace, (ii) calculate how many trees reside on a terrace and (iii) enumerate all trees on a terrace.'),
(81708, 2018, 'bio', 'Grouper: graph-based clustering and annotation for improved de novo transcriptome analysis', 'Malik, Laraib|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.', 'United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty378', '29746620', 'De novo transcriptome analysis using RNA-seq offers a promising means to study gene expression in non-model organisms. Yet, the difficulty of transcriptome assembly means that the contigs provided by the assembler often represent a fractured and incomplete view of the transcriptome, complicating downstream analysis. We introduce Grouper, a new method for clustering contigs from de novo assemblies that are likely to belong to the same transcripts and genes, these groups can subsequently be analyzed more robustly. When provided with access to the genome of a related organism, Grouper can transfer annotations to the de novo assembly, further improving the clustering.'),
(81709, 2018, 'bio', 'Minimap2: pairwise alignment for nucleotide sequences', 'Li, Heng', 'Department of Medical Population Genetics Program, Broad Institute, Cambridge, MA, USA.', 'United States', 0, 'Algorithms|Base Sequence|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty191', '29750242', 'Recent advances in sequencing technologies promise ultra-long reads of xe2x88xbc100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.'),
(81710, 2018, 'bio', 'Base-pair resolution detection of transcription factor binding site by deep deconvolutional network', 'Salekin, Sirajul|Zhang, Jianqiu Michelle|Huang, Yufei', 'Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA.|Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA.|Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty383', '29757349', 'Transcription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise TF binding sites (TFBSs) is essential for understanding the detailed mechanisms of TF-mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair resolution prediction of TFBS.'),
(81711, 2018, 'bio', 'Development and evaluation of a deep learning model for protein-ligand binding affinity prediction', 'Stepniewska-Dziubinska, Marta M|Zielenkiewicz, Piotr|Siedlecki, Pawel', 'Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.', 'Poland|Poland|Poland', 38, '-', 'Journal Article', '10.1093/bioinformatics/bty374', '29757353', 'Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to learn to extract features that are relevant for the task at hand.'),
(81712, 2018, 'bio', 'The revival of the Gini importance?', 'Nembrini, Stefano|König, Inke R|Wright, Marvin N', 'Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.', 'United States|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty373', '29757357', 'Random forests are fast, flexible and represent a robust approach to analyze high dimensional data. A key advantage over alternative machine learning algorithms are variable importance measures, which can be used to identify relevant features or perform variable selection. Measures based on the impurity reduction of splits, such as the Gini importance, are popular because they are simple and fast to compute. However, they are biased in favor of variables with many possible split points and high minor allele frequency.'),
(81713, 2018, 'bio', 'Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA)', 'Zhang, Le|Xiao, Ming|Zhou, Jingsong|Yu, Jun', 'College of Computer Science, Sichuan University, Chengdu, China.|School of Computer and Information Science, Southwest University, Chongqing, China.|College of Computer Science, Sichuan University, Chengdu, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty392', '29762634', 'This study addresses several important questions related to naturally underrepresented sequences: (i) are there permutations of real genomic DNA sequences in a defined length (k-mer) and a given lineage that do not actually exist or underrepresented? (ii) If there are such sequences, what are their characteristics in terms of k-mer length and base composition? (iii) Are they related to CpG or TpA underrepresentation known for human sequences? We propose that the answers to these questions are of great significance for the study of sequence-associated regulatory mechanisms, such cytosine methylation and chromosomal structures in physiological or pathological conditions such as cancer.'),
(81714, 2018, 'bio', 'TimeXNet Web: identifying cellular response networks from diverse omics time-course data', 'Tan, Phit Ling|López, Yosvany|Nakai, Kenta|Patil, Ashwini', 'Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.|Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty393', '29762638', 'Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms.'),
(81715, 2018, 'bio', 'MRMAssayDB: an integrated resource for validated targeted proteomics assays', 'Bhowmick, Pallab|Mohammed, Yassene|Borchers, Christoph H', 'University of Victoria - Genome British Columbia Proteomics Centre, Victoria, BC, Canada.|University of Victoria - Genome British Columbia Proteomics Centre, Victoria, BC, Canada.|University of Victoria - Genome British Columbia Proteomics Centre, Victoria, BC, Canada.', 'Canada|Canada|Canada', 0, 'Biological Assay|Humans|Neoplasms|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/bty385', '29762640', 'Multiple Reaction Monitoring (MRM)-based targeted proteomics is increasingly being used to study the molecular basis of disease. When combined with an internal standard, MRM allows absolute quantification of proteins in virtually any type of sample but the development and validation of an MRM assay for a specific protein is laborious. Therefore, several public repositories now host targeted proteomics MRM assays, including NCIs Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb and PeptideTracker, with all of which contain different levels of information.'),
(81716, 2018, 'bio', 'Indexed variation graphs for efficient and accurate resistome profiling', 'Rowe, Will P M|Winn, Martyn D', 'Institute of Integrative Biology, The University of Liverpool, Liverpool, UK.|Scientific Computing Department, The Hartree Centre, STFC Daresbury Laboratory, Warrington, UK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty387', '29762644', 'Antimicrobial resistance (AMR) remains a major threat to global health. Profiling the collective AMR genes within a metagenome (the resistome) facilitates greater understanding of AMR gene diversity and dynamics. In turn, this can allow for gene surveillance, individualized treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools.'),
(81717, 2018, 'bio', 'FlexiDot: highly customizable, ambiguity-aware dotplots for visual sequence analyses', 'Seibt, Kathrin M|Schmidt, Thomas|Heitkam, Tony', 'Technische Universität Dresden, School of Science, Faculty of Biology, Institute of Botany, Dresden, Germany.|Technische Universität Dresden, School of Science, Faculty of Biology, Institute of Botany, Dresden, Germany.|Technische Universität Dresden, School of Science, Faculty of Biology, Institute of Botany, Dresden, Germany.', 'Germany|Germany|Germany', 0, 'Humans|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty395', '29762645', 'FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of ambiguities and substitutions. Our shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of large sequence sets, enabling dotplot use for routine analyses.'),
(81718, 2018, 'bio', 'Correcting mistakes in predicting distributions', 'Marot-Lassauzaie, Valérie|Bernhofer, Michael|Rost, Burkhard', 'Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany.|Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany.|Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany.', 'Germany|Germany|Germany', 0, 'Humans|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/bty346', '29762646', 'Many applications monitor predictions of a whole range of features for biological datasets, e.g. the fraction of secreted human proteins in the human proteome. Results and error estimates are typically derived from publications.'),
(81719, 2018, 'bio', 'Transform-MinER: transforming molecules in enzyme reactions', 'Tyzack, Jonathan D|Ribeiro, Antonio J M|Borkakoti, Neera|Thornton, Janet M', 'EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Enzymes|Software', 'Journal Article', '10.1093/bioinformatics/bty394', '29762650', 'One goal of synthetic biology is to make new enzymes to generate new products, but identifying the starting enzymes for further investigation is often elusive and relies on expert knowledge, intensive literature searching and trial and error.'),
(81720, 2018, 'bio', 'RecPhyloXML: a format for reconciled gene trees', 'Duchemin, Wandrille|Gence, Guillaume|Arigon Chifolleau, Anne-Muriel|Arvestad, Lars|Bansal, Mukul S|Berry, Vincent|Boussau, Bastien|Chevenet, François|Comte, Nicolas|Davín, Adrián A|Dessimoz, Christophe|Dylus, David|Hasic, Damir|Mallo, Diego|Planel, Rémi|Posada, David|Scornavacca, Celine|Szöllosi, Gergely|Zhang, Louxin|Tannier, Éric|Daubin, Vincent', 'Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.|Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.|LIRMM, Université de Montpellier, CNRS, Montpellier, France.|Department of Mathematics, Stockholm University, Stockholm, Sweden.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.|LIRMM, Université de Montpellier, CNRS, Montpellier, France.|Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.|LIRMM, Université de Montpellier, CNRS, Montpellier, France.|INRIA Grenoble Rhône-Alpes, F-38334 Montbonnot, France.|Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Department of Mathematics, Faculty of Science, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.|Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Laboratoire dAnalyse Bio-informatique en Génomique et Métabolisme CNRS-UMR 8030, Commissariat à lÉnergie Atomique (CEA), Institut de Génomique, Genoscope, Evry, France.|Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Institut de Biologie Computationnelle (IBC), Montpellier, France.|MTA-ELTE Lendület Evolutionary Genomics Research Group, Budapest, Hungary.|Department of Mathematics, National University of Singapore, Singapore, Singapore.|Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.|Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France.', 'France|France|France|Sweden|United States|France|France|France|France|France|Switzerland|Switzerland|Bosnia and ', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty389', '29762653', 'A reconciliation is an annotation of the nodes of a gene tree with evolutionary events-for example, speciation, gene duplication, transfer, loss, etc.-along with a mapping onto a species tree. Many algorithms and software produce or use reconciliations but often using different reconciliation formats, regarding the type of events considered or whether the species tree is dated or not. This complicates the comparison and communication between different programs.'),
(81721, 2018, 'bio', 'pyABC: distributed, likelihood-free inference', 'Klinger, Emmanuel|Rickert, Dennis|Hasenauer, Jan', 'Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg.', '-|-|-', 9, 'Bayes Theorem|Monte Carlo Method|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bty361', '29762723', 'Likelihood-free methods are often required for inference in systems biology. While approximate Bayesian computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models, we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements a scalable, runtime-minimizing parallelization strategy for multi-core and distributed environments scaling to thousands of cores. The framework is accessible to non-expert users and also enables advanced users to experiment with and to custom implement many options of ABC-SMC schemes, such as acceptance threshold schedules, transition kernels and distance functions without alteration of pyABCs source code. pyABC includes a web interface to visualize ongoing and finished ABC-SMC runs and exposes an API for data querying and post-processing.'),
(81722, 2018, 'bio', 'ConSpeciFix: classifying prokaryotic species based on gene flow', 'Bobay, Louis-Marie|Ellis, Brian Shin-Hua|Ochman, Howard', 'Department of Biology, University of North Carolina at Greensboro, Greensboro, NC, USA.|Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA.|Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty400', '29771275', 'Classification of prokaryotic species is usually based on sequence similarity thresholds, which are easy to apply but lack a biologically-relevant foundation. Here, we present ConSpeciFix, a program that classifies prokaryotes into species using criteria set forth by the Biological Species Concept, thereby unifying species definition in all domains of life.'),
(81723, 2018, 'bio', 'Identification and characterization of moonlighting long non-coding RNAs based on RNA and protein interactome', 'Cheng, Lixin|Leung, Kwong-Sak', 'Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.', 'Hong Kong|Hong Kong', 0, 'Exons|Genomics|Humans|Proteins|RNA, Long Noncoding|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bty399', '29771280', 'Moonlighting proteins are a class of proteins having multiple distinct functions, which play essential roles in a variety of cellular and enzymatic functioning systems. Although there have long been calls for computational algorithms for the identification of moonlighting proteins, research on approaches to identify moonlighting long non-coding RNAs (lncRNAs) has never been undertaken. Here, we introduce a novel methodology, MoonFinder, for the identification of moonlighting lncRNAs. MoonFinder is a statistical algorithm identifying moonlighting lncRNAs without a priori knowledge through the integration of protein interactome, RNA-protein interactions and functional annotation of proteins.'),
(81724, 2018, 'bio', 'AC-DIAMOND v1: accelerating large-scale DNA-protein alignment', 'Mai, Huijun|Zhang, Yifan|Li, Dinghua|Leung, Henry Chi-Ming|Luo, Ruibang|Wong, Chi-Kwong|Ting, Hing-Fung|Lam, Tak-Wah', 'Department of Computer Science, University of Hong Kong, Hong Kong.|L3 Bioinformatics Limited, Hong Kong.|Department of Computer Science, University of Hong Kong, Hong Kong.|Department of Computer Science, University of Hong Kong, Hong Kong.|Department of Computer Science, University of Hong Kong, Hong Kong.|L3 Bioinformatics Limited, Hong Kong.|Department of Computer Science, University of Hong Kong, Hong Kong.|Department of Computer Science, University of Hong Kong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty391', '29771282', 'AC-DIAMOND (v1) is a DNA-protein alignment tool designed to tackle the efficiency challenge of aligning large amount of reads or contigs to protein databases. When compared with the previously most efficient method DIAMOND, AC-DIAMOND gains a 6- to 7-fold speed-up, while retaining a similar degree of sensitivity. The improvement is rooted at two aspects: first, using a compressed index of seeds with adaptive-length to speed-up the matching between query and reference sequences, second, adopting a compact form of dynamic programing to fully utilize the parallelism of the SIMD capability.'),
(81725, 2018, 'bio', 'Single cell clustering based on cell-pair differentiability correlation and variance analysis', 'Jiang, Hao|Sohn, Lydia L|Huang, Haiyan|Chen, Luonan', 'Department of Mathematics, School of Information, Renmin University of China, Beijing, China.|Department of Mechanical Engineering, University of California, Berkeley, CA, USA.|Department of Statistics, University of California, Berkeley, CA, USA.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.', 'China|United States|United States|China', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty390', '29771290', 'The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies.'),
(81726, 2018, 'bio', 'Palimpsest: an R package for studying mutational and structural variant signatures along clonal evolution in cancer', 'Shinde, Jayendra|Bayard, Quentin|Imbeaud, Sandrine|Hirsch, Théo Z|Liu, Feng|Renault, Victor|Zucman-Rossi, Jessica|Letouzé, Eric', 'INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|Laboratory for Bioinformatics, Fondation Jean Dausset - CEPH, Paris, France.|Laboratory for Bioinformatics, Fondation Jean Dausset - CEPH, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.', 'France|France|France|France|France|France|France|France', 0, 'Evolution, Molecular|Exome|Humans|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/bty388', '29771315', 'Cancer genomes are altered by various mutational processes and, like palimpsests, bear the signatures of these different processes. The Palimpsest R package provides a complete workflow for the characterization and visualization of mutational signatures and their evolution along tumor development. The package covers a wide range of functions for extracting both base substitution and structural variant signatures, inferring the clonality of each alteration and analyzing the evolution of mutational processes between early clonal and late subclonal events. Palimpsest also estimates the probability of each mutation being due to each process to predict the mechanisms at the origin of driver events. Palimpsest is an easy-to-use toolset for reconstructing the natural history of a tumor using whole exome or whole genome sequencing data.'),
(81727, 2018, 'bio', 'Snakemake-a scalable bioinformatics workflow engine', 'Köster, Johannes|Rahmann, Sven', 'INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.|INSERM, UMR-1162, Génomique Fonctionnelle des Tumeurs Solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire dHématologie, Faculté de Médecine, Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Médecine, Biologie Humaine, Université Paris 13, Sorbonne Paris Cité, Bobigny, France, Université Paris Diderot, Paris, France.', 'France|France', 29, '-', 'Journal Article', '10.1093/bioinformatics/bty350', '29788404', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81728, 2018, 'bio', 'GSCALite: a web server for gene set cancer analysis', 'Liu, Chun-Jie|Hu, Fei-Fei|Xia, Meng-Xuan|Han, Leng|Zhang, Qiong|Guo, An-Yuan', 'Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, Peoples Republic of China.|Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, Peoples Republic of China.|Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, Peoples Republic of China.|Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, USA.|Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, Peoples Republic of China.|Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, Peoples Republic of China.', 'China|China|China|United States|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty411', '29790900', 'The availability of cancer genomic data makes it possible to analyze genes related to cancer. Cancer is usually the result of a set of genes and the signal of a single gene could be covered by background noise. Here, we present a web server named Gene Set Cancer Analysis (GSCALite) to analyze a set of genes in cancers with the following functional modules. (i) Differential expression in tumor versus normal, and the survival analysis, (ii) Genomic variations and their survival analysis, (iii) Gene expression associated cancer pathway activity, (iv) miRNA regulatory network for genes, (v) Drug sensitivity for genes, (vi) Normal tissue expression and eQTL for genes. GSCALite is a user-friendly web server for dynamic analysis and visualization of gene set in cancer and drug sensitivity correlation, which will be of broad utilities to cancer researchers.'),
(81729, 2018, 'bio', 'Combining probabilistic alignments with read pair information improves accuracy of split-alignments', 'Shrestha, Anish M S|Yoshikawa, Naruki|Asai, Kiyoshi', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, Japan.', 'Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty398', '29790902', 'Split-alignments provide base-pair-resolution evidence of genomic rearrangements. In practice, they are found by first computing high-scoring local alignments, parts of which are then combined into a split-alignment. This approach is challenging when aligning a short read to a large and repetitive reference, as it tends to produce many spurious local alignments leading to ambiguities in identifying the correct split-alignment. This problem is further exacerbated by the fact that rearrangements tend to occur in repeat-rich regions.'),
(81730, 2018, 'bio', 'Exploring drug space with ChemMapscom', 'Borrel, Alexandre|Kleinstreuer, Nicole C|Fourches, Denis', 'Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.|Division of Intramural Research/Biostatistics and Computational Biology Branch.|Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.', 'United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty412', '29790904', 'Easily navigating chemical space has become more important due to the increasing size and diversity of publicly-accessible databases such as DrugBank, ChEMBL or Tox21. To do so, modelers typically rely on complex projection techniques using molecular descriptors computed for all the chemicals to be visualized. However, the multiple cheminformatics steps required to prepare, characterize, compute and explore those molecules, are technical, typically necessitate scripting skills, and thus represent a real obstacle for non-specialists.'),
(81731, 2018, 'bio', 'bcSeq: an R package for fast sequence mapping in high-throughput shRNA and CRISPR screens', 'Lin, Jiaxing|Gresham, Jeremy|Wang, Tongrong|Kim, So Young|Alvarez, James|Damrauer, Jeffrey S|Floyd, Scott|Granek, Joshua|Allen, Andrew|Chan, Cliburn|Xie, Jichun|Owzar, Kouros', 'Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, USA.|Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.|Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, 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|Clustered Regularly Interspaced Short Palindromic Repeats|Gene Library|High-Throughput Nucleotide Sequencing|RNA, Small Interfering|Software', 'Journal Article', '10.1093/bioinformatics/bty402', '29790906', 'CRISPR-Cas9 and shRNA high-throughput sequencing screens have abundant applications for basic and translational research. Methods and tools for the analysis of these screens must properly account for sequencing error, resolve ambiguous mappings among similar sequences in the barcode library in a statistically principled manner, and be computationally efficient. Herein we present bcSeq, an open source R package that implements a fast and parallelized algorithm for mapping high-throughput sequencing reads to a barcode library while tolerating sequencing error. The algorithm uses a Trie data structure for speed and resolves ambiguous mappings by using a statistical sequencing error model based on Phred scores for each read.'),
(81732, 2018, 'bio', 'Application of network smoothing to glycan LC-MS profiling', 'Klein, Joshua|Carvalho, Luis|Zaia, Joseph', 'Program for Bioinformatics, Boston University, Boston, MA, USA.|Program for Bioinformatics, Boston University, Boston, MA, USA.|Program for Bioinformatics, Boston University, Boston, MA, USA.', 'United States|United States|United States', 0, 'Algorithms|Chromatography, Liquid|Glycomics|Humans|Polysaccharides|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bty397', '29790907', 'Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity.'),
(81733, 2018, 'bio', 'EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery', 'Orzechowski, Patryk|Sipper, Moshe|Huang, Xiuzhen|Moore, Jason H', 'Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel.|Department of Computer Science, Arkansas State University, Jonesboro, AR, USA.|Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|Israel|United States|United States', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty401', '29790909', 'Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance.'),
(81734, 2018, 'bio', 'SoS Notebook: an interactive multi-language data analysis environment', 'Peng, Bo|Wang, Gao|Ma, Jun|Leong, Man Chong|Wakefield, Chris|Melott, James|Chiu, Yulun|Du, Di|Weinstein, John N', 'Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Human Genetics, The University of Chicago, Chicago, IL, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Statistics, Rice University, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty405', '29790910', 'Complex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking.'),
(81735, 2018, 'bio', 'PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data', 'Chang, Cheng|Xu, Kaikun|Guo, Chaoping|Wang, Jinxia|Yan, Qi|Zhang, Jian|He, Fuchu|Zhu, Yunping', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, Peoples Republic of China.|Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.', 'China|China|China|China|China|China|China|China', 10, 'Cluster Analysis|Data Interpretation, Statistical|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/bty408', '29790911', 'Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data.'),
(81736, 2018, 'bio', 'YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU', 'Quang, Daniel|Guan, Yuanfang|Parker, Stephen C J', '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.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States', 2, 'Algorithms|Computer Graphics|Deep Learning|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty396', '29790915', 'Motif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME.'),
(81737, 2018, 'bio', 'MASCOT: parameter and state inference under the marginal structured coalescent approximation', 'Müller, Nicola F|Rasmussen, David|Stadler, Tanja', '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.', 'Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty406', '29790921', 'The structured coalescent is widely applied to study demography within and migration between sub-populations from genetic sequence data. Current methods are either exact but too computationally inefficient to analyse large datasets with many sub-populations, or make strong approximations leading to severe biases in inference. We recently introduced an approximation based on weaker assumptions to the structured coalescent enabling the analysis of larger datasets with many different states. We showed that our approximation provides unbiased migration rate and population size estimates across a wide parameter range.'),
(81738, 2018, 'bio', 'Using meshes for MeSH term enrichment and semantic analyses', 'Yu, Guangchuang', 'Institute of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.', 'China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty410', '29790928', 'Medical Subject Headings (MeSH) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH provides unique and comprehensive annotations for life science. The meshes package implements measurement of the semantic similarity of MeSH terms and gene products to help using MeSH vocabulary in knowledge mining. Enrichment analysis to extract the biological meanings from gene list, expression profile and genomic regions is also provided using MeSH annotation. Meshes supports more than 70 species and provides high quality visualization methods to help interpreting analysis results.'),
(81739, 2018, 'bio', 'SimExTargId: a comprehensive package for real-time LC-MS data acquisition and analysis', 'Edmands, William M B|Hayes, Josie|Rappaport, Stephen M', 'Program in Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.|Program in Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.|Program in Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.', 'United States|United States|United States', 0, 'Chromatography, Liquid|Metabolomics|Software|Tandem Mass Spectrometry|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty218', '29790936', 'Liquid chromatography mass spectrometry (LC-MS) is the favored method for untargeted metabolomic analysis of small molecules in biofluids. Here we present SimExTargId, an open-source R package for autonomous analysis of metabolomic data and real-time observation of experimental runs. This simultaneous, fully automated and multi-threaded (optional) package is a wrapper for vendor-independent format conversion (ProteoWizard), xcms- and CAMERA- based peak-picking, MetMSLine-based pre-processing and covariate-based statistical analysis. Users are notified of detrimental instrument drift or errors by email. Also included are two shiny applications, targetId for real-time MS2 target identification, and peakMonitor to monitor targeted metabolites.'),
(81740, 2018, 'bio', 'Nextstrain: real-time tracking of pathogen evolution', 'Hadfield, James|Megill, Colin|Bell, Sidney M|Huddleston, John|Potter, Barney|Callender, Charlton|Sagulenko, Pavel|Bedford, Trevor|Neher, Richard A', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Max Planck Institute for Developmental Biology, Tübingen, Germany.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Max Planck Institute for Developmental Biology, Tübingen, Germany.', 'United States|United States|United States|United States|United States|United States|Germany|United States|Germany', 151, '-', 'Journal Article', '10.1093/bioinformatics/bty407', '29790939', 'Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike.'),
(81741, 2018, 'bio', 'PyCoTools: a Python toolbox for COPASI', 'Welsh, Ciaran M|Fullard, Nicola|Proctor, Carole J|Martinez-Guimera, Alvaro|Isfort, Robert J|Bascom, Charles C|Tasseff, Ryan|Przyborski, Stefan A|Shanley, Daryl P', 'Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.|Department of Biosciences, Durham University, Durham, UK.|Institute of Cellular Medicine, Newcastle University, Newcastle, UK.|Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.|The Proctor & Gamble Company, Cincinnati, USA.|The Proctor & Gamble Company, Cincinnati, USA.|The Proctor & Gamble Company, Cincinnati, USA.|Department of Biosciences, Durham University, Durham, UK.|Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty409', '29790940', 'COPASI is an open source software package for constructing, simulating and analyzing dynamic models of biochemical networks. COPASI is primarily intended to be used with a graphical user interface but often it is desirable to be able to access COPASI features programmatically, with a high level interface.'),
(81742, 2018, 'bio', 'Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator', 'Tchechmedjiev, Andon|Abdaoui, Amine|Emonet, Vincent|Melzi, Soumia|Jonnagaddala, Jitendra|Jonquet, Clement', 'Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.|Faculty of Medicine, University of New South Wales, Sydney, New South Wales 2052, Australia.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.', 'France|France|France|France|Australia|France', 0, 'Biological Ontologies|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/bty009', '29846492', 'Second use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014).'),
(81743, 2018, 'bio', 'mEBT: multiple-matching evidence-based translator of murine genomic responses for human immunity studies', 'Tae, Donghyun|Seok, Junhee', 'Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty431', '29850767', 'In this paper, we introduce multiple-matching Evidence-based Translator (mEBT) to discover genomic responses from murine expression data for human immune studies, which are significant in the given condition of mice and likely have similar responses in the corresponding condition of human. mEBT is evaluated over multiple datasets and shows improved inter-species agreement. mEBT is expected to be useful for research groups who use murine models to study human immunity.'),
(81744, 2018, 'bio', 'Bio-knowledge-based filters improve residue-residue contact prediction accuracy', 'Wozniak, P P|Pelc, J|Skrzypecki, M|Vriend, G|Kotulska, M', 'Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.|Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.|Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.|Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, The Netherlands.|Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.', 'Poland|Poland|Poland|Netherlands|Poland', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty416', '29850768', 'Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general.'),
(81745, 2018, 'bio', 'The size matters? A computational tool to design bivalent ligands', 'Pérez-Benito, Laura|Henry, Andrew|Matsoukas, Minos-Timotheos|Lopez, Laura|Pulido, Daniel|Royo, Miriam|Cordomí, Arnau|Tresadern, Gary|Pardo, Leonardo', 'Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain.|Chemical Computing Group, St Johns Innovation Centre Cowley Road, Cambridge, UK.|Department of Pharmacy, University Campus, University of Patras, School of Health Sciences, Rion, Patras, Greece.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain.|Combinatorial Chemistry Unit, Barcelona Science Park, Barcelona, Spain.|Combinatorial Chemistry Unit, Barcelona Science Park, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain.|Janssen Research and Development, Beerse, Belgium.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain.', 'Spain|United Kingdom|Greece|Spain|Spain|Spain|Spain|Belgium|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty422', '29850769', 'Bivalent ligands are increasingly important such as for targeting G protein-coupled receptor (GPCR) dimers or proteolysis targeting chimeras (PROTACs). They contain two pharmacophoric units that simultaneously bind in their corresponding binding sites, connected with a spacer chain. Here, we report a molecular modelling tool that links the pharmacophore units via the shortest pathway along the receptors van der Waals surface and then scores the solutions providing prioritization for the design of new bivalent ligands.'),
(81746, 2018, 'bio', 'MODE-TASK: large-scale protein motion tools', 'Ross, Caroline|Nizami, Bilal|Glenister, Michael|Sheik Amamuddy, Olivier|Atilgan, Ali Rana|Atilgan, Canan|Tastan Bishop, Özlem', 'Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa.|Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Turkey.|Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Turkey.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa.', 'South Africa|South Africa|South Africa|South Africa|Turkey|Turkey|South Africa', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty427', '29850770', 'MODE-TASK, a novel and versatile software suite, comprises Principal Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using Molecular Dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive. Beside the command line function, a GUI has been developed as a PyMOL plugin.'),
(81747, 2018, 'bio', 'TomoEED: fast edge-enhancing denoising of tomographic volumes', 'Moreno, J J|Martínez-Sánchez, A|Martínez, J A|Garzón, E M|Fernández, J J', 'Department of Informatics, University of Almeria, Almeria, Spain.|Max-Planck Institute for Biochemistry, Martinsried, Germany.|Department of Informatics, University of Almeria, Almeria, Spain.|Department of Informatics, University of Almeria, Almeria, Spain.|Spanish National Research Council (CNB-CSIC), Madrid, Spain.', 'Spain|Germany|Spain|Spain|Spain', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty435', '29850773', 'TomoEED is an optimized software tool for fast feature-preserving noise filtering of large 3D tomographic volumes on CPUs and GPUs. The tool is based on the anisotropic nonlinear diffusion method. It has been developed with special emphasis in the reduction of the computational demands by using different strategies, from the algorithmic to the high performance computing perspectives. TomoEED manages to filter large volumes in a matter of minutes in standard computers.'),
(81748, 2018, 'bio', 'geck: trio-based comparative benchmarking of variant calls', 'Kómár, Péter|Kural, Deniz', 'Seven Bridges Inc, Cambridge, MA, USA.|Totient Inc, Cambridge, MA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty415', '29850774', 'Classical methods of comparing the accuracies of variant calling pipelines are based on truth sets of variants whose genotypes are previously determined with high confidence. An alternative way of performing benchmarking is based on Mendelian constraints between related individuals. Statistical analysis of Mendelian violations can provide truth set-independent benchmarking information, and enable benchmarking less-studied variants and diverse populations.'),
(81749, 2018, 'bio', 'LncRNAnet: long non-coding RNA identification using deep learning', 'Baek, Junghwan|Lee, Byunghan|Kwon, Sunyoung|Yoon, Sungroh', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|Electrical and Computer Engineering, Seoul National University, Seoul, Korea.|Electrical and Computer Engineering, Seoul National University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty418', '29850775', 'Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biological studies. The development of next-generation sequencing has helped in the discovery of lncRNA transcripts. However, the experimental verification of numerous transcriptomes is time consuming and costly. To alleviate these issues, a computational approach is needed to distinguish lncRNAs from the transcriptomes.'),
(81750, 2018, 'bio', 'NGL viewer: web-based molecular graphics for large complexes', 'Rose, Alexander S|Bradley, Anthony R|Valasatava, Yana|Duarte, Jose M|Prlic, Andreas|Rose, Peter W', 'RCSB Protein Data Bank.|RCSB Protein Data Bank.|San Diego Supercomputer Center, UC San Diego, CA, USA.|RCSB Protein Data Bank.|RCSB Protein Data Bank.|RCSB Protein Data Bank.', 'United States|United States|United States|United States|United States|United States', 125, '-', 'Journal Article', '10.1093/bioinformatics/bty419', '29850778', 'The interactive visualization of very large macromolecular complexes on the web is becoming a challenging problem as experimental techniques advance at an unprecedented rate and deliver structures of increasing size.'),
(81751, 2018, 'bio', 'RIFRAF: a frame-resolving consensus algorithm', 'Eren, Kemal|Murrell, Ben', 'Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.|Department of Medicine, University of California San Diego, La Jolla, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty426', '29850783', 'Protein coding genes can be studied using long-read next generation sequencing. However, high rates of indel sequencing errors are problematic, corrupting the reading frame. Even the consensus of multiple independent sequence reads retains indel errors. To solve this problem, we introduce Reference-Informed Frame-Resolving multiple-Alignment Free template inference algorithm (RIFRAF), a sequence consensus algorithm that takes a set of error-prone reads and a reference sequence and infers an accurate in-frame consensus. RIFRAF uses a novel structure, analogous to a two-layer hidden Markov model: the consensus is optimized to maximize alignment scores with both the set of noisy reads and with a reference. The template-to-reads component of the model encodes the preponderance of indels, and is sensitive to the per-base quality scores, giving greater weight to more accurate bases. The reference-to-template component of the model penalizes frame-destroying indels. A local search algorithm proceeds in stages to find the best consensus sequence for both objectives.'),
(81752, 2018, 'bio', 'GateFinder: projection-based gating strategy optimization for flow and mass cytometry', 'Aghaeepour, Nima|Simonds, Erin F|Knapp, David J H F|Bruggner, Robert V|Sachs, Karen|Culos, Anthony|Gherardini, Pier Federico|Samusik, Nikolay|Fragiadakis, Gabriela K|Bendall, Sean C|Gaudilliere, Brice|Angst, Martin S|Eaves, Connie J|Weiss, William A|Fantl, Wendy J|Nolan, Garry P', 'Baxter Laboratory in Stem Cell Biology.|Department of Neurology, University of California, San Francisco, CA, USA.|Terry Fox Laboratory, British Columbia Cancer Research Center, Vancouver, BC, Canada.|Baxter Laboratory in Stem Cell Biology.|Baxter Laboratory in Stem Cell Biology.|Department of Anesthesiology, Stanford University, Stanford, CA, USA.|Baxter Laboratory in Stem Cell Biology.|Baxter Laboratory in Stem Cell Biology.|Baxter Laboratory in Stem Cell Biology.|Baxter Laboratory in Stem Cell Biology.|Baxter Laboratory in Stem Cell Biology.|Department of Anesthesiology, Stanford University, Stanford, CA, USA.|Terry Fox Laboratory, British Columbia Cancer Research Center, Vancouver, BC, Canada.|Department of Neurology, University of California, San Francisco, CA, USA.|Department of Obstetrics and Gynecology, Stanford University, Stanford, CA, USA.|Baxter Laboratory in Stem Cell Biology.', 'United States|United States|Canada|United States|United States|United States|United States|United States|United States|United States|United States|United States|Canada|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty430', '29850785', 'High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging.'),
(81753, 2018, 'bio', 'PEA: an integrated R toolkit for plant epitranscriptome analysis', 'Zhai, Jingjing|Song, Jie|Cheng, Qian|Tang, Yunjia|Ma, Chuang', 'State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences.', '-|-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty421', '29850798', 'The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.'),
(81754, 2018, 'bio', 'LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning', 'Yang, Cheng|Yang, Longshu|Zhou, Man|Xie, Haoling|Zhang, Chengjiu|Wang, May D|Zhu, Huaiqiu', 'Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.|Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.|Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.|Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.|Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.|Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.|Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.', 'China|China|China|China|China|Georgia|China', 21, '-', 'Journal Article', '10.1093/bioinformatics/bty428', '29850816', 'To characterize long non-coding RNAs (lncRNAs), both identifying and functionally annotating them are essential to be addressed. Moreover, a comprehensive construction for lncRNA annotation is desired to facilitate the research in the field.'),
(81755, 2018, 'bio', 'Pathway-structured predictive modeling for multi-level drug response in multiple myeloma', 'Zhang, Xinyan|Li, Bingzong|Han, Huiying|Song, Sha|Xu, Hongxia|Yi, Zixuan|Hong, Yating|Zhuang, Wenzhuo|Yi, Nengjun', 'Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.|Department of Hematology, The Second Affiliated Hospital of Soochow University, Suzhou, China.|Department of Cell Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|Department of Cell Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|Department of Cell Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|School of Medicine, Eastern Virginia Medical School, Norfork, VA, USA.|Department of Hematology, The Second Affiliated Hospital of Soochow University, Suzhou, China.|Department of Cell Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.', 'Georgia|China|China|China|China|United States|China|China|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty436', '29850860', 'Molecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet.'),
(81756, 2018, 'bio', 'Motif scraper: a cross-platform, open-source tool for identifying degenerate nucleotide motif matches in FASTA files', 'Roberson, Elisha D O', 'Department of Medicine, Division of Rheumatology, Washington University, St. Louis, MO, USA.', 'United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty437', '29850891', 'Many genomic features are defined not by exact sequence matches, but by degenerate nucleotide motifs that represent multiple compatible matches. While there are databases cataloging genomic features, such as the location of transcription factor motifs, for commonly used model species, identifying the locations of novel motifs, known motifs in non-model genomes, or known motifs in personal whole-genomes is difficult. I designed motif scraper to overcome this limitation, allowing for efficient, multiprocessor motif searches in any FASTA file.'),
(81757, 2018, 'bio', 'A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data', 'Kong, Yunchuan|Yu, Tianwei', 'Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty429', '29850911', 'Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This nxe2x89xaap property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks.'),
(81758, 2018, 'bio', 'DeepMirTar: a deep-learning approach for predicting human miRNA targets', 'Wen, Ming|Cong, Peisheng|Zhang, Zhimin|Lu, Hongmei|Li, Tonghua', 'College of Chemistry and Chemical Engineering, Central South University, Changsha, Peoples Republic of China.|School of Chemical Science and Engineering, Tongji University, Shanghai, Peoples Republic of China.|College of Chemistry and Chemical Engineering, Central South University, Changsha, Peoples Republic of China.|College of Chemistry and Chemical Engineering, Central South University, Changsha, Peoples Republic of China.|School of Chemical Science and Engineering, Tongji University, Shanghai, Peoples Republic of China.', 'China|China|China|China|China', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty424', '29868708', 'MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates.'),
(81759, 2018, 'bio', '2018 ISCB Innovator Award recognizes M Madan Babu', 'Fogg, Christiana N|Kovats, Diane E|Shamir, Ron', 'Kennsington, MD, USA.|Bethesda, MD, USA.|Computational Genomics Group, Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'United States|United States|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty328', '29868709', '-'),
(81760, 2018, 'bio', 'The Rényi divergence enables accurate and precise cluster analysis for localization microscopy', 'Staszowska, Adela D|Fox-Roberts, Patrick|Hirvonen, Liisa M|Peddie, Christopher J|Collinson, Lucy M|Jones, Gareth E|Cox, Susan', 'Randall Centre for Cell and Molecular Biophysics, Kings College London, London, UK.|Randall Centre for Cell and Molecular Biophysics, Kings College London, London, UK.|Randall Centre for Cell and Molecular Biophysics, Kings College London, London, UK.|Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK.|Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK.|Randall Centre for Cell and Molecular Biophysics, Kings College London, London, UK.|Randall Centre for Cell and Molecular Biophysics, Kings College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty403', '29868717', 'Clustering analysis is a key technique for quantitatively characterizing structures in localization microscopy images. To build up accurate information about biological structures, it is critical that the quantification is both accurate (close to the ground truth) and precise (has small scatter and is reproducible).'),
(81761, 2018, 'bio', 'Comparing complex variants in family trios', 'Toptas, Berke Ç|Rakocevic, Goran|Kómár, Péter|Kural, Deniz', 'R&D Seven Bridges Genomics, Cambridge, MA, USA.|R&D Seven Bridges Genomics, Cambridge, MA, USA.|R&D Seven Bridges Genomics, Cambridge, MA, USA.|R&D Seven Bridges Genomics, Cambridge, MA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty443', '29868720', 'Several tools exist to count Mendelian violations in family trios by comparing variants at the same genomic positions. This naive variant comparison, however, fails to assess regions where multiple variants need to be examined together, resulting in reduced accuracy of existing Mendelian violation checking tools.'),
(81762, 2018, 'bio', 'Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints', 'Chan, Siu H J|Wang, Lin|Dash, Satyakam|Maranas, Costas D', 'Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.|Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.', 'United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty446', '29868725', 'Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81763, 2018, 'bio', 'Improved design and analysis of CRISPR knockout screens', 'Chen, Chen-Hao|Xiao, Tengfei|Xu, Han|Jiang, Peng|Meyer, Clifford A|Li, Wei|Brown, Myles|Liu, X Shirley', 'Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty450', '29868757', 'Genome-wide clustered, regularly interspaced, short palindromic repeat (CRISPR)-Cas9 screen has been widely used to interrogate gene functions. However, the rules to design better libraries beg further refinement.'),
(81764, 2018, 'bio', 'deepNF: deep network fusion for protein function prediction', 'Gligorijevic, Vladimir|Barot, Meet|Bonneau, Richard', 'Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.|Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.|Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.', 'United States|United States|United States', 25, '-', 'Journal Article', '10.1093/bioinformatics/bty440', '29868758', 'The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly non-linear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks.'),
(81765, 2018, 'bio', 'PREQUAL: detecting non-homologous characters in sets of unaligned homologous sequences', 'Whelan, Simon|Irisarri, Iker|Burki, Fabien', 'Department of Evolutionary Genetics, Program in Evolutionary Biology, Uppsala University, Uppsala, Sweden.|Department of Organismal Biology, Program in Systematic Biology, Uppsala University, Uppsala, Sweden.|Department of Organismal Biology, Program in Systematic Biology, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty448', '29868763', 'Phylogenomic datasets invariably contain undetected stretches of non-homologous characters due to poor-quality sequences or erroneous gene models. The large-scale multi-gene nature of these datasets renders impractical or impossible detailed manual curation of sequences, but few tools exist that can automate this task. To address this issue, we developed a new method that takes as input a set of unaligned homologous sequences and uses an explicit probabilistic approach to identify and mask regions with non-homologous adjacent characters. These regions are defined as sharing no statistical support for homology with any other sequence in the set, which can result from e.g. sequencing errors or gene prediction errors creating frameshifts. Our methodology is implemented in the program PREQUAL, which is a fast and accurate tool for high-throughput filtering of sequences. The program is primarily aimed at amino acid sequences, although it can handle protein coding DNA sequences as well. It is fully customizable to allow fine-tuning of the filtering sensitivity.'),
(81766, 2018, 'bio', 'ggCyto: next generation open-source visualization software for cytometry', 'Van, Phu|Jiang, Wenxin|Gottardo, Raphael|Finak, Greg', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.', 'United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty441', '29868771', 'Open source software for computational cytometry has gained in popularity over the past few years. Efforts such as FlowCAP, the Lyoplate and Euroflow projects have highlighted the importance of efforts to standardize both experimental and computational aspects of cytometry data analysis. The R/BioConductor platform hosts the largest collection of open source cytometry software covering all aspects of data analysis and providing infrastructure to represent and analyze cytometry data with all relevant experimental, gating and cell population annotations enabling fully reproducible data analysis. Data visualization frameworks to support this infrastructure have lagged behind.'),
(81767, 2018, 'bio', 'Message from the ISCB: 2018 ISCB Accomplishments by a Senior Scientist Award', 'Fogg, Christiana N|Kovats, Diane E|Shamir, Ron', 'Kennsington, MD, USA.|International Society for Computational Biology, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'United States|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty284', '29868787', '-'),
(81768, 2018, 'bio', 'An automated method for detecting alternatively spliced protein domains', 'Coelho, Vitor|Sammeth, Michael', 'Molecular and Structural Biology Department, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Molecular and Structural Biology Department, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.', 'Brazil|Brazil', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty425', '29868795', 'Alternative splicing (AS) has been demonstrated to play a role in shaping eukaryotic gene diversity at the transcriptional level. However, the impact of AS on the proteome is still controversial. Studies that seek to explore the effect of AS at the proteomic level are hampered by technical difficulties in the cumbersome process of casting forth and back between genome, transcriptome and proteome space coordinates, and the naïve prediction of protein domains in the presence of AS suffers many redundant sequence scans that emerge from constitutively spliced regions that are shared between alternative products of a gene.'),
(81769, 2018, 'bio', 'Message from the ISCB: 2018 Outstanding Contributions to ISCB Award: Russ Altman', 'Fogg, Christiana N|Kovats, Diane E|Shamir, Ron', 'Freelance Writer, Kennsington, MD, USA.|International Society for Computational Biology, Bethesda, MD, USA.|Computational Genomics Group, Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'United States|United States|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty308', '29868819', '-'),
(81770, 2018, 'bio', 'Predicting Cancer Drug Response using a Recommender System', 'Suphavilai, Chayaporn|Bertrand, Denis|Nagarajan, Niranjan', 'Department of Computer Science, School of Computing, National University of Singapore, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Singapore.', 'Singapore|Singapore|Singapore', 15, '-', 'Journal Article', '10.1093/bioinformatics/bty452', '29868820', 'As we move toward an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.'),
(81771, 2018, 'bio', 'Axe: rapid, competitive sequence read demultiplexing using a trie', 'Murray, Kevin D|Borevitz, Justin O', 'ARC Centre of Excellence in Plant Energy Biology, Department of Plant Science, Research School of Biology, ANU, Canberra, Australia.|ARC Centre of Excellence in Plant Energy Biology, Department of Plant Science, Research School of Biology, ANU, Canberra, Australia.', 'Australia|Australia', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty432', '29868827', 'We describe a rapid algorithm for demultiplexing DNA sequence reads with in-read indices. Axe selects the optimal index present in a sequence read, even in the presence of sequencing errors. The algorithm is able to handle combinatorial indexing, indices of differing length and several mismatches per index sequence.'),
(81772, 2018, 'bio', 'Transfer learning for biomedical named entity recognition with neural networks', 'Giorgi, John M|Bader, Gary D', 'Department of Computer Science, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.', 'Canada|Canada', 29, '-', 'Journal Article', '10.1093/bioinformatics/bty449', '29868832', 'The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER.'),
(81773, 2018, 'bio', 'An in silico proteomics screen to predict and prioritize protein-protein interactions dependent on post-translationally modified motifs', 'Schmoker, Anna M|Driscoll, Heather E|Geiger, Stefanie R|Vincent, James J|Ebert, Alicia M|Ballif, Bryan A', 'Department of Biology, University of Vermont, Burlington, VT, USA.|Vermont Genetics Network Bioinformatics Core, University of Vermont, Burlington, VT, USA.|Department of Biology, University of Vermont, Burlington, VT, USA.|Department of Biology, University of Vermont, Burlington, VT, USA.|Department of Biology, University of Vermont, Burlington, VT, USA.|Department of Biology, University of Vermont, Burlington, VT, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty434', '29868839', 'The development of proteomic methods for the characterization of domain/motif interactions has greatly expanded our understanding of signal transduction. However, proteomics-based binding screens have limitations including that the queried tissue or cell type may not harbor all potential interacting partners or post-translational modifications (PTMs) required for the interaction. Therefore, we sought a generalizable, complementary in silico approach to identify potentially novel motif and PTM-dependent binding partners of high priority.'),
(81774, 2018, 'bio', 'LiveKraken--real-time metagenomic classification of illumina data', 'Tausch, Simon H|Strauch, Benjamin|Andrusch, Andreas|Loka, Tobias P|Lindner, Martin S|Nitsche, Andreas|Renard, Bernhard Y', 'Bioinformatics Unit (MF1).|Bioinformatics Unit (MF1).|Bioinformatics Unit (MF1).|Bioinformatics Unit (MF1).|Bioinformatics Unit (MF1).|Centre for Biological Threats and Special Pathogens (ZBS1), Robert Koch Institute, Berlin, Germany.|Bioinformatics Unit (MF1).', '-|-|-|-|-|Germany|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty433', '29868852', 'In metagenomics, Kraken is one of the most widely used tools due to its robustness and speed. Yet, the overall turnaround time of metagenomic analysis is hampered by the sequential paradigm of wet and dry lab. In urgent experiments, it can be crucial to gain a timely insight into a dataset.'),
(81775, 2018, 'bio', 'TriPoly: haplotype estimation for polyploids using sequencing data of related individuals', 'Motazedi, Ehsan|de Ridder, Dick|Finkers, Richard|Baldwin, Samantha|Thomson, Susan|Monaghan, Katrina|Maliepaard, Chris', 'Bioinformatics Group, Wageningen University and Research, Postbus 633, AP, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University and Research, Postbus 633, AP, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Postbus 386, AJ, Wageningen, The Netherlands.|New Zealand Institute for Plant and Food Research, Private Bag, Christchurch, New Zealand.|New Zealand Institute for Plant and Food Research, Private Bag, Christchurch, New Zealand.|New Zealand Institute for Plant and Food Research, Private Bag, Christchurch, New Zealand.|Wageningen UR Plant Breeding, Postbus 386, AJ, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|New Zealand|New Zealand|New Zealand|Netherlands', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty442', '29868858', 'Knowledge of haplotypes, i.e. phased and ordered marker alleles on a chromosome, is essential to answer many questions in genetics and genomics. By generating short pieces of DNA sequence, high-throughput modern sequencing technologies make estimation of haplotypes possible for single individuals. In polyploids, however, haplotype estimation methods usually require deep coverage to achieve sufficient accuracy. This often renders sequencing-based approaches too costly to be applied to large populations needed in studies of Quantitative Trait Loci.'),
(81776, 2018, 'bio', 'TEcandidates: prediction of genomic origin of expressed transposable elements using RNA-seq data', 'Valdebenito-Maturana, Braulio|Riadi, Gonzalo', 'Department of Bioinformatics, University of Talca, Calle 1 Poniente, Talca, Chile.|Department of Bioinformatics, University of Talca, Calle 1 Poniente, Talca, Chile.', 'Chile|Chile', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty423', '29868859', 'In recent years, Transposable Elements (TEs) have been related to gene regulation. However, estimating the origin of expression of TEs through RNA-seq is complicated by multi-mapping reads coming from their repetitive sequences. Current approaches that address multi-mapping reads are focused in expression quantification and not in finding the origin of expression. Addressing the genomic origin of expressed TEs could further aid in understanding the role that TEs might have in the cell.'),
(81777, 2018, 'bio', 'ProAcePred: prokaryote lysine acetylation sites prediction based on elastic net feature optimization', 'Chen, Guodong|Cao, Man|Luo, Kun|Wang, Lina|Wen, Pingping|Shi, Shaoping', 'Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences.|Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China.|Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences.', '-|-|-|China|China|-', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty444', '29868863', 'Lysine acetylation exists extensively in prokaryotes, and plays a vital role in function adjustment. Recent progresses in the identification of prokaryote acetylation substrates and sites provide a great opportunity to explore the difference of substrate site specificity between prokaryotic and eukaryotic acetylation. Motif analysis suggests that prokaryotic and eukaryotic acetylation sites have distinct location-specific difference, and it is necessary to develop a prokaryote-specific acetylation sites prediction tool.'),
(81778, 2018, 'bio', 'Knotty: efficient and accurate prediction of complex RNA pseudoknot structures', 'Jabbari, Hosna|Wark, Ian|Montemagno, Carlo|Will, Sebastian', 'Department of Computer Science, University of Vermont, Burlington, VT, USA.|Ingenuity Lab, Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada.|Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL, USA.|Theoretical Biochemistry Group (TBI), Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.', 'United States|Canada|United States|Austria', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty420', '29868872', 'The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots has O(n6) time and O(n4) space complexity. The algorithm CCJ dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (xe2x88xbc200 bases already require 256xc2xa0GB space).'),
(81779, 2018, 'bio', '2018 ISCB Overton Prize awarded to Cole Trapnell', 'Fogg, Christiana N|Kovats, Diane E|Shamir, Ron', 'Kensington, MD, USA.|Bethesda, MD, USA.|Computational Genomics Group, Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'United States|United States|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty360', '29868884', '-'),
(81780, 2018, 'bio', 'ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides', 'Wei, Leyi|Zhou, Chen|Chen, Huangrong|Song, Jiangning|Su, Ran', 'School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology.|School of Computer Software, Tianjin University, Tianjin, China.', 'China|China|China|-|China', 85, '-', 'Journal Article', '10.1093/bioinformatics/bty451', '29868903', 'Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.'),
(81781, 2018, 'bio', 'Lost in translation: bioinformatic analysis of variations affecting the translation initiation codon in the human genome', 'Abad-Navarro, Francisco|de la Morena-Barrio, María Eugenia|Fernández-Breis, Jesualdo Tomás|Corral, Javier', 'Departamento de Informática y Sistemas, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.|Servicio de Hematología y Oncología Médica, Hospital Universitario Morales Meseguer, Centro Regional de Hemodonación, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.|Departamento de Informática y Sistemas, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.|Servicio de Hematología y Oncología Médica, Hospital Universitario Morales Meseguer, Centro Regional de Hemodonación, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty453', '29868922', 'Translation is a key biological process controlled in eukaryotes by the initiation AUG codon. Variations affecting this codon may have pathological consequences by disturbing the correct initiation of translation. Unfortunately, there is no systematic study describing these variations in the human genome. Moreover, we aimed to develop new tools for in silico prediction of the pathogenicity of gene variations affecting AUG codons, because to date, these gene defects have been wrongly classified as missense.'),
(81782, 2019, 'bio', 'Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus', 'Han, Lei|Li, Lei|Wen, Feng|Zhong, Lei|Zhang, Tong|Wan, Xiu-Feng', 'Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.|Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.|Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.|Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.|Tencent AI Lab, Tencent, Shenzhen, China.|Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.', 'United States|United States|United States|United States|China|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty457', '29878046', 'Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies and protocol implementation. A novel method without such limitations is needed for antigenic variant identification.'),
(81783, 2018, 'bio', 'ImmunomeBrowser: a tool to aggregate and visualize complex and heterogeneous epitopes in reference proteins', 'Dhanda, Sandeep Kumar|Vita, Randi|Ha, Brendan|Grifoni, Alba|Peters, Bjoern|Sette, Alessandro', 'Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty463', '29878047', 'Datasets that are derived from different studies (e.g. MHC ligand elution, MHC binding, B/T cell epitope screening etc.) often vary in terms of experimental approaches, sizes of peptides tested, including partial and or nested overlapping peptides and in the number of donors tested.'),
(81784, 2018, 'bio', 'snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions', 'Nousiainen, Kari|Kanduri, Kartiek|Ricaño-Ponce, Isis|Wijmenga, Cisca|Lahesmaa, Riitta|Kumar, Vinod|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.|Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.|Department of Genetics, UMCG, University of Groningen, AB Groningen, the Netherlands.|Department of Genetics, UMCG, University of Groningen, AB Groningen, the Netherlands.|Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.|Department of Genetics, UMCG, University of Groningen, AB Groningen, the Netherlands.|Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.', 'Finland|Finland|Netherlands|Netherlands|Finland|Netherlands|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty460', '29878048', 'Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed.'),
(81785, 2018, 'bio', 'Detection of multi-dimensional co-exclusion patterns in microbial communities', 'Albayrak, Levent|Khanipov, Kamil|Golovko, George|Fofanov, Yuriy', 'Department of Pharmacology and Toxicology, University of Texas Medical Branch-Galveston, Galveston, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch-Galveston, Galveston, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch-Galveston, Galveston, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch-Galveston, Galveston, USA.', 'United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty414', '29878050', 'Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting micro-organisms intolerance to each others presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond micro-organism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.'),
(81786, 2018, 'bio', 'Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis', 'Tian, Mingyuan|Reed, Jennifer L', 'Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.|Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty445', '29878053', 'Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.'),
(81787, 2018, 'bio', 'MIDORI server: a webserver for taxonomic assignment of unknown metazoan mitochondrial-encoded sequences using a curated database', 'Leray, Matthieu|Ho, Shian-Lei|Lin, I-Jeng|Machida, Ryuji J', 'Smithsonian Tropical Research Institute, Smithsonian Institution, Panama City, Republic of Panama.|Biodiversity Research Centre, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Centre, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Centre, Academia Sinica, Taipei, Taiwan.', 'Panama|-|-|-', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty454', '29878054', 'We present MIDORI server, a user-friendly web platform that uses a curated reference dataset, MIDORI, for high throughput taxonomic classification of unknown metazoan mitochondrial-encoded gene sequences. Currently three methods of taxonomic assignments: RDP Classifier, SPINGO and SINTAX, are implemented.'),
(81788, 2018, 'bio', 'MetQy-an R package to query metabolic functions of genes and genomes', 'Martinez-Vernon, Andrea S|Farrell, Frederick|Soyer, Orkun S', 'Synthetic Biology Centre for Doctoral Training.|School of Life Sciences.|Synthetic Biology Centre for Doctoral Training.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty447', '29878056', 'With the rapid accumulation of sequencing data from genomic and metagenomic studies, there is an acute need for better tools that facilitate their analyses against biological functions. To this end, we developed MetQy, an open-source R package designed for query-based analysis of functional units in [meta]genomes and/or sets of genes using the The Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, MetQy contains visualization and analysis tools and facilitates KEGGs flat file manipulation. Thus, MetQy enables better understanding of metabolic capabilities of known genomes or user-specified [meta]genomes by using the available information and can help guide studies in microbial ecology, metabolic engineering and synthetic biology.'),
(81789, 2018, 'bio', 'AA9int: SNP interaction pattern search using non-hierarchical additive model set', 'Lin, Hui-Yi|Huang, Po-Yu|Chen, Dung-Tsa|Tung, Heng-Yuan|Sellers, Thomas A|Pow-Sang, Julio M|Eeles, Rosalind|Easton, Doug|Kote-Jarai, Zsofia|Amin Al Olama, Ali|Benlloch, Sara|Muir, Kenneth|Giles, Graham G|Wiklund, Fredrik|Gronberg, Henrik|Haiman, Christopher A|Schleutker, Johanna|Nordestgaard, Børge G|Travis, Ruth C|Hamdy, Freddie|Neal, David E|Pashayan, Nora|Khaw, Kay-Tee|Stanford, Janet L|Blot, William J|Thibodeau, Stephen N|Maier, Christiane|Kibel, Adam S|Cybulski, Cezary|Cannon-Albright, Lisa|Brenner, Hermann|Kaneva, Radka|Batra, Jyotsna|Teixeira, Manuel R|Pandha, Hardev|Lu, Yong-Jie|PRACTICAL Consortium|Park, Jong Y', 'Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.|Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.|Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.|Department of Cancer Epidemiology Moffitt Cancer Center and Research Institute, Tampa, FL, USA.|Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.|The Institute of Cancer Research, London, UK.|Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.|The Institute of Cancer Research, London, UK.|Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.|Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.|Institute of Population Health, University of Manchester, Manchester, UK.|Division of Cancer Epidemiology and Intelligence, Cancer Council Victoria, Melbourne, VIC, Australia.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.|Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku and Tyks Microbiology and Genetics.|Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, DK-2730 Herlev, Denmark.|Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, UK.|Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.|Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.|Strangeways Research Laboratory, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.|Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge, UK.|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|International Epidemiology Institute, Rockville, MD, USA.|Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.|Institute of Human Genetics, University Hospital Ulm, Ulm, Germany.|Brigham and Womens Hospital/Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland.|Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, UT, USA.|Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431 Sofia, Bulgaria.|Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia.|Department of Genetics, Portuguese Oncology Institute, Porto, Portugal.|The University of Surrey, Guildford, Surrey, UK.|Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.|Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.|Department of Cancer Epidemiology Moffitt Cancer Center and Research Institute, Tampa, FL, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Australia|Sweden|Sweden|United Kingdom|United Kingdom|Denmark|United Kingdo', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty461', '29878078', 'The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions.'),
(81790, 2018, 'bio', 'iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach', 'Liu, Bin|Li, Kai|Huang, De-Shuang|Chou, Kuo-Chen', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.|Gordon Life Science Institute, Belmont, MA, USA.', 'China|China|China|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty458', '29878118', 'Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from non-enhancers only. Recently, a two-layer predictor called iEnhancer-2L was developed that can be used to predict the enhancers strength as well. However, its prediction quality needs further improvement to enhance the practical application value.'),
(81791, 2018, 'bio', 'Modelling BioNano optical data and simulation study of genome map assembly', 'Chen, Ping|Jing, Xinyun|Ren, Jian|Cao, Han|Hao, Pei|Li, Xuan', 'Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China.|Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China.|Ultravision Technology, Beijing, China.|BioNano Genomics, San Diego, CA, USA.|University of Chinese Academy of Sciences, Beijing, China.|Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|United States|China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty456', '29893801', 'The launch of the BioNano next-generation mapping system has greatly enhanced the performance of physical map construction, thus rapidly expanding the application of optical mapping in genome research. Data biases have profound implications for downstream applications. However, very little is known about the properties and biases of BioNano data, and the very factors that contribute to whole-genome optical map assembly.'),
(81792, 2018, 'bio', 'SSMART: sequence-structure motif identification for RNA-binding proteins', 'Munteanu, Alina|Mukherjee, Neelanjan|Ohler, Uwe', 'Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.|Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.|Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty404', '29893814', 'RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized.'),
(81793, 2018, 'bio', 'comoRbidity: an R package for the systematic analysis of disease comorbidities', 'Gutiérrez-Sacristán, Alba|Bravo, Àlex|Giannoula, Alexia|Mayer, Miguel A|Sanz, Ferran|Furlong, Laura I', 'Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Comorbidity|Data Mining|Databases, Factual|Electronic Health Records|Female|Humans|Male|Software', 'Journal Article', '10.1093/bioinformatics/bty315', '29897411', 'The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis.'),
(81794, 2018, 'bio', 'miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer', 'Tong, Yin|Ru, Beibei|Zhang, Jiangwen', 'School of Biological Sciences, The University of Hong Kong, Hong Kong, China.|School of Biological Sciences, The University of Hong Kong, Hong Kong, China.|School of Biological Sciences, The University of Hong Kong, Hong Kong, China.', 'China|China|China', 5, 'Algorithms|Binding Sites|Computational Biology|Data Visualization|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Neoplasms|RNA, Long Noncoding|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty320', '29897412', 'MicroRNAs play critical roles in oncogenesis by targeting a few key regulators or a large cohort of genes impinging on downstream signaling pathways. Conversely, miRNA activity is also titrated by competitive endogenous RNA such as lncRNA with sponge effect. Web-based server, miRNACancerMap, aims to unravel lncRNA-miRNA-mRNA tripartite complexity to predict the function and clinical relevance of miRNA with network perspective. In conjunction with large-scale data and information integration, miRNACancerMap implements various algorithms and pipelines to construct dynamic miRNA-centered network with rigorous Systems Biology approaches and the state-of-the-art visualization tool. The capability of the server to generate testable hypotheses was exemplified with cases to identify hub miRNAs regulating most of the differentially-expressed genes involved in cancer stage transition, miRNA-TF pairs shared by pan-cancers and lncRNA sponges validated by multiple datasets. LncRNAs sharing the same miRNAs binding sites as mRNAs can sequester miRNAs and indirectly regulate the activity of the related mRNAs. We have re-annotated traditional microarray chips, and included these datasets in the server to enable validation of the predicted lncRNA-miRNA-mRNA regulations derived from TCGA RNA-seq data. Of note, our server enables identifying miRNAs associated with cancer signaling pathways, and related lncRNA sponges from pan-cancers with only a few mouse clicks.'),
(81795, 2018, 'bio', 'CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones', 'Müller, Sören|Cho, Ara|Liu, Siyuan J|Lim, Daniel A|Diaz, Aaron', 'Department of Neurological Surgery and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Gene Expression Profiling|Humans|Neoplasms|RNA, Small Cytoplasmic|Sequence Analysis, DNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty316', '29897414', 'Single-cell RNA-sequencing (scRNA-seq) has enabled studies of tissue composition at unprecedented resolution. However, the application of scRNA-seq to clinical cancer samples has been limited, partly due to a lack of scRNA-seq algorithms that integrate genomic mutation data.'),
(81796, 2018, 'bio', 'Spatial quantification of the synaptic activity phenotype across large populations of neurons with Markov random fields', 'Robinson, Sean|Courtney, Michael J', 'Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Neuronal Signalling Lab, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.', 'Finland|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty322', '29897415', 'The collective and co-ordinated synaptic activity of large neuronal populations is relevant to neuronal development as well as a range of neurological diseases. Quantification of synaptically-mediated neuronal signalling permits further downstream analysis as well as potential application in target validation and in vitro screening assays. Our aim is to develop a phenotypic quantification for neuronal activity imaging data of large populations of neurons, in particular relating to the spatial component of the activity.'),
(81797, 2018, 'bio', 'SciApps: a cloud-based platform for reproducible bioinformatics workflows', 'Wang, Liya|Lu, Zhenyuan|Van Buren, Peter|Ware, Doreen', 'Cold Spring Harbor Laboratories, Cold Spring Harbor, Ithaca, NY, USA.|Cold Spring Harbor Laboratories, Cold Spring Harbor, Ithaca, NY, USA.|Cold Spring Harbor Laboratories, Cold Spring Harbor, Ithaca, NY, USA.|Cold Spring Harbor Laboratories, Cold Spring Harbor, Ithaca, NY, USA.', 'United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty439', '29897418', 'The rapid accumulation of both sequence and phenotype data generated by high-throughput methods has increased the need to store and analyze data on distributed storage and computing systems. Efficient data management across these heterogeneous systems requires a workflow management system to simplify the task of analysis through automation and make large-scale bioinformatics analyses accessible and reproducible.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81798, 2018, 'bio', 'MARDy: Mycology Antifungal Resistance Database', 'Nash, Anthony|Sewell, Thomas|Farrer, Rhys A|Abdolrasouli, Alireza|Shelton, Jennifer M G|Fisher, Matthew C|Rhodes, Johanna', 'Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.|MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.|National Heart and Lung Institute, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Antifungal Agents|Databases, Genetic|Drug Resistance, Fungal|Genes, Fungal|Humans|Internet|Polymorphism, Genetic', 'Journal Article', '10.1093/bioinformatics/bty321', '29897419', 'The increase of antifungal drug resistance is a major global human health concern and threatens agriculture and food security, in order to tackle these concerns, it is important to understand the mechanisms that cause antifungal resistance. The curated Mycology Antifungal Resistance Database (MARDy) is a web-service of antifungal drug resistance mechanisms, including amino acid substitutions, tandem repeat sequences and genome ploidy. MARDy is implemented on a Linux, Apache, MySQL and PHP web development platform and includes a local installation of BLASTn of the database of curated genes.'),
(81799, 2018, 'bio', 'GlycoStore: a database of retention properties for glycan analysis', 'Zhao, Sophie|Walsh, Ian|Abrahams, Jodie L|Royle, Louise|Nguyen-Khuong, Terry|Spencer, Daniel|Fernandes, Daryl L|Packer, Nicolle H|Rudd, Pauline M|Campbell, Matthew P', 'Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.|Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.|Institute for Glycomics, Griffith University, Gold Coast, Australia.|Ludger Ltd., Culham Science Centre, Oxfordshire, UK.|Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.|Ludger Ltd., Culham Science Centre, Oxfordshire, UK.|Ludger Ltd., Culham Science Centre, Oxfordshire, UK.|Institute for Glycomics, Griffith University, Gold Coast, Australia.|Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.|Institute for Glycomics, Griffith University, Gold Coast, Australia.', 'Singapore|Singapore|Australia|United Kingdom|Singapore|United Kingdom|United Kingdom|Australia|Singapore|Australia', 0, 'Chromatography, High Pressure Liquid|Databases, Chemical|Electrophoresis, Capillary|Glycolipids|Glycomics|Glycoproteins|Hydrophobic and Hydrophilic Interactions|Molecular Structure|Oligosaccharides|Polysaccharides|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bty319', '29897488', 'GlycoStore is a curated chromatographic, electrophoretic and mass-spectrometry composition database of N-, O-, glycosphingolipid (GSL) glycans and free oligosaccharides associated with a range of glycoproteins, glycolipids and biotherapeutics. The database is built on publicly available experimental datasets from GlycoBase developed in the Oxford Glycobiology Institute and then the National Institute for Bioprocessing Research and Training (NIBRT). It has now been extended to include recently published and in-house data collections from the Bioprocessing Technology Institute (BTI) A*STAR, Macquarie University and Ludger Ltd. GlycoStore provides access to approximately 850 unique glycan structure entries supported by over 8500 retention positions determined by: (i) hydrophilic interaction chromatography (HILIC) ultra-high performance liquid chromatography (U/HPLC) and reversed phase (RP)-U/HPLC with fluorescent detection, (ii) porous graphitized carbon (PGC) chromatography in combination with ESI-MS/MS detection, and (iii) capillary electrophoresis with laser induced fluorescence detection (CE-LIF). GlycoStore enhances many features previously available in GlycoBase while addressing the limitations of the data collections and model of this popular resource. GlycoStore aims to support detailed glycan analysis by providing a resource that underpins current workflows. It will be regularly updated by expert annotation of published data and data obtained from the project partners.'),
(81800, 2018, 'bio', 'IDP-denovo: de novo transcriptome assembly and isoform annotation by hybrid sequencing', 'Fu, Shuhua|Ma, Yingke|Yao, Hui|Xu, Zhichao|Chen, Shilin|Song, Jingyuan|Au, Kin Fai', 'Department of Internal Medicine, University of Iowa, Iowa City, USA.|Department of Internal Medicine, University of Iowa, Iowa City, USA.|Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.|Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.|Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.|Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.|Department of Internal Medicine, University of Iowa, Iowa City, USA.', 'United States|United States|China|China|China|China|United States', 0, 'Alternative Splicing|Dendrobium|Gene Expression Profiling|Gene Library|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bty098', '29905763', 'In the past years, the long read (LR) sequencing technologies, such as Pacific Biosciences and Oxford Nanopore Technologies, have been demonstrated to substantially improve the quality of genome assembly and transcriptome characterization. Compared to the high cost of genome assembly by LR sequencing, it is more affordable to generate LRs for transcriptome characterization. That is, when informative transcriptome LR data are available without a high-quality genome, a method for de novo transcriptome assembly and annotation is of high demand.'),
(81801, 2018, 'bio', 'Improved genomic island predictions with IslandPath-DIMOB', 'Bertelli, Claire|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.', 'Canada|Canada', 12, 'Algorithms|Archaea|Bacteria|Base Composition|Genome, Archaeal|Genome, Bacterial|Genomic Islands|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty095', '29905770', 'Genomic islands (GIs) are clusters of genes of probable horizontal origin that play a major role in bacterial and archaeal genome evolution and microbial adaptability. They are of high medical and industrial interest, due to their enrichment in virulence factors, some antimicrobial resistance genes and adaptive metabolic pathways. The development of more sensitive but precise prediction tools, using either sequence composition-based methods or comparative genomics, is needed as large-scale analyses of microbial genomes increase.'),
(81802, 2018, 'bio', 'Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects', 'Choi, Leena|Carroll, Robert J|Beck, Cole|Mosley, Jonathan D|Roden, Dan M|Denny, Joshua C|Van Driest, Sara L', 'Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.|Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.|Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.|Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Datasets as Topic|Drug Discovery|Drug-Related Side Effects and Adverse Reactions|Electronic Health Records|Humans|Logistic Models|Probability', 'Journal Article', '10.1093/bioinformatics/bty306', '29912272', 'Phenome-wide association studies (PheWAS) have been used to discover many genotype-phenotype relationships and have the potential to identify therapeutic and adverse drug outcomes using longitudinal data within electronic health records (EHRs). However, the statistical methods for PheWAS applied to longitudinal EHR medication data have not been established.'),
(81803, 2018, 'bio', 'In silico read normalization using set multi-cover optimization', 'Durai, Dilip A|Schulz, Marcel H', 'Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.|Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.', 'Germany|Germany', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty307', '29912280', 'De Bruijn graphs are a common assembly data structure for sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph.'),
(81804, 2018, 'bio', 'gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations', 'Irrgang, M Eric|Hays, Jennifer M|Kasson, Peter M', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty484', '29912282', 'Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles.'),
(81805, 2018, 'bio', 'ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts', 'Laajala, Teemu D|Murtojärvi, Mika|Virkki, Arho|Aittokallio, Tero', 'Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Department of Future Technologies, University of Turku, Turku, Finland.|Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Department of Mathematics and Statistics, University of Turku, Turku, Finland.', 'Finland|Finland|Finland|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty477', '29912284', 'Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data.'),
(81806, 2018, 'bio', 'ClusterScan: simple and generalistic identification of genomic clusters', 'Volpe, Massimiliano|Miralto, Marco|Gustincich, Stefano|Sanges, Remo', 'Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy.|Department of Research Infrastructures for Marine Biological Resources, Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy.|Department of Neuroscience and Brain Technologies, Italian Institute of Technologies (IIT), Genova, Italy.|Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy.', 'Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty486', '29912285', 'Studies on gene clusters proved to be an excellent source of information to understand genomes evolution and identifying specific metabolic pathways or gene families. Improvements in sequencing methods have resulted in a large increase of sequenced genomes for which cluster annotation could be performed and standardized. Currently available programs are developed to search for specific cluster types and none of them is suitable for a broad range of user-based choices. We have developed ClusterScan which allows identifying clusters of any kind of feature simply based on their genomic coordinates and user-defined categorical annotations.'),
(81807, 2019, 'bio', 'TranscriptClean: variant-aware correction of indels, mismatches and splice junctions in long-read transcripts', 'Wyman, Dana|Mortazavi, Ali', 'Department of Developmental and Cell Biology, UC Irvine, Irvine, CA, USA.|Department of Developmental and Cell Biology, UC Irvine, Irvine, CA, USA.', 'United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty483', '29912287', 'Long-read, single-molecule sequencing platforms hold great potential for isoform discovery and characterization of multi-exon transcripts. However, their high error rates are an obstacle to distinguishing novel transcript isoforms from sequencing artifacts. Therefore, we developed the package TranscriptClean to correct mismatches, microindels and noncanonical splice junctions in mapped transcripts using the reference genome while preserving known variants.'),
(81808, 2018, 'bio', 'RiboSketch: versatile visualization of multi-stranded RNA and DNA secondary structure', 'Lu, Jacob S|Bindewald, Eckart|Kasprzak, Wojciech K|Shapiro, Bruce A', 'RNA Biology Laboratory, National Cancer Institute, Frederick, MD, USA.|Basic Science Program, RNA Biology Laboratory, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA.|Basic Science Program, RNA Biology Laboratory, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA.|RNA Biology Laboratory, National Cancer Institute, Frederick, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty468', '29912310', 'Creating clear, visually pleasing 2D depictions of RNA and DNA strands and their interactions is important to facilitate and communicate insights related to nucleic acid structure. Here we present RiboSketch, a secondary structure image production application that enables the visualization of multistranded structures via layout algorithms, comprehensive editing capabilities, and a multitude of simulation modes. These interactive features allow RiboSketch to create publication quality diagrams for structures with a wide range of composition, size and complexity. The program may be run in any web browser without the need for installation, or as a standalone Java application.'),
(81809, 2018, 'bio', 'The ancestral KH peptide at the root of a domain family with three different folds', 'Pereira, Joana|Lupas, Andrei N', 'Department of Protein Evolution, Max-Planck-Institute for Developmental Biology, Tübingen, Germany.|Department of Protein Evolution, Max-Planck-Institute for Developmental Biology, Tübingen, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty480', '29912332', 'The direct ancestor of the DNA-protein world of today is considered to have been an RNA-peptide world, in which peptides were co-factors of RNA-mediated catalysis and replication. Evidence for these ancestral peptides, from which folded proteins evolved, can be derived even today from regions of local sequence similarity within globally dissimilar folds. One of these is the 45-residue motif common to both folds of the hnRNP K homology (KH) domain.'),
(81810, 2018, 'bio', 'Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile', 'Guan, Yuanfang|Li, Tingyang|Zhang, Hongjiu|Zhu, Fan|Omenn, Gilbert S', '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.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|China|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty467', '29912344', 'Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed.'),
(81811, 2018, 'bio', 'Identifying differentially methylated sites in samples with varying tumor purity', 'Häkkinen, Antti|Alkodsi, Amjad|Facciotto, Chiara|Zhang, Kaiyang|Kaipio, Katja|Leppä, Sirpa|Carpén, Olli|Grénman, Seija|Hynninen, Johanna|Hietanen, Sakari|Lehtonen, Rainer|Hautaniemi, Sampsa', 'Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Department of Pathology and Forensic Medicine, Institute of Biomedicine, University of Turku, Turku, Finland.|Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland.|Department of Pathology, Medicum, University of Helsinki and HUSLAB, Helsinki University Hospital, Helsinki, Finland.|Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland.|Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland.|Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland.|Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Research Programs Unit, Genome-Scale Biology, Medicum and Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'DNA Methylation|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/bty310', '29912358', 'DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples.'),
(81812, 2018, 'bio', 'TiSAn: estimating tissue-specific effects of coding and non-coding variants', 'Vervier, Kévin|Michaelson, Jacob J', 'Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.|Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.', 'United States|United States', 2, 'Autism Spectrum Disorder|Genome-Wide Association Study|Heart Diseases|Humans|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/bty301', '29912365', 'Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations.'),
(81813, 2019, 'bio', 'Chemical shift-based identification of monosaccharide spin-systems with NMR spectroscopy to complement untargeted glycomics', 'Klukowski, Piotr|Schubert, Mario', 'Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.|Department of Biosciences, University of Salzburg, Salzburg, Austria.', 'Poland|Austria', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty465', '29912372', 'A better understanding of oligosaccharides and their wide-ranging functions in almost every aspect of biology and medicine promises to uncover hidden layers of biology and will support the development of better therapies. Elucidating the chemical structure of an unknown oligosaccharide remains a challenge. Efficient tools are required for non-targeted glycomics. Chemical shifts are a rich source of information about the topology and configuration of biomolecules, whose potential is however not fully explored for oligosaccharides. We hypothesize that the chemical shifts of each monosaccharide are unique for each saccharide type with a certain linkage pattern, so that correlated data measured by NMR spectroscopy can be used to identify the chemical nature of a carbohydrate.'),
(81814, 2018, 'bio', 'iMetaLab 10: a web platform for metaproteomics data analysis', 'Liao, Bo|Ning, Zhibin|Cheng, Kai|Zhang, Xu|Li, Leyuan|Mayne, Janice|Figeys, Daniel', 'Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 13, '-', 'Journal Article', '10.1093/bioinformatics/bty466', '29912378', 'The human gut microbiota, a complex, dynamic and biodiverse community, has been increasingly shown to influence many aspects of health and disease. Metaproteomic analysis has proven to be a powerful approach to study the functionality of the microbiota. However, the processing and analyses of metaproteomic mass spectrometry data remains a daunting task in metaproteomics data analysis. We developed iMetaLab, a web based platform to provide a user-friendly and comprehensive data analysis pipeline with a focus on lowering the technical barrier for metaproteomics data analysis.'),
(81815, 2019, 'bio', 'GeneSpy, a user-friendly and flexible genomic context visualizer', 'Garcia, Pierre Simon|Jauffrit, Frédéric|Grangeasse, Christophe|Brochier-Armanet, Céline', 'Univ Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France.|Univ Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France.|MMSB Molecular Microbiology and Structural Biochemistry, Institut de Biologie et de Chimie des Protéines 7 passage du Vercors, LYON cedex, France.|Univ Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France.', 'France|France|France|France', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty459', '29912383', 'The exploration and comparison of genome organization is routinely used in the frame of genomic and phylogenomic analyses. As a consequence, in the past few years, various tools allowing visualizing genomic contexts have been developed. However, their use is often hampered by a lack of flexibility, particularly concerning associated databases input formats and figure customization. Here we present GeneSpy, a graphical user interface that allows the visualization and dynamic exploration of eukaryotic and prokaryotic annotated genomes. GeneSpy relies on user-friendly manageable local databases and allows the easy customization and production of figures in a multitude of formats.'),
(81816, 2018, 'bio', 'Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker', 'Bengtsson-Palme, Johan|Richardson, Rodney T|Meola, Marco|Wurzbacher, Christian|Tremblay, Émilie D|Thorell, Kaisa|Kanger, Kärt|Eriksson, K Martin|Bilodeau, Guillaume J|Johnson, Reed M|Hartmann, Martin|Nilsson, R Henrik', 'Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.|Department of Entomology, The Ohio State University-Ohio Agricultural Research and Development Center, Wooster, OH, USA.|Fermentation Organisms, Methods Development and Analytics, Agroscope, CH-3003 Bern, Switzerland.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.|Canadian Food Inspection Agency, Ottawa Laboratory Fallowfield, Ottawa, ON, Canada.|Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.|Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.|Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden.|Canadian Food Inspection Agency, Ottawa Laboratory Fallowfield, Ottawa, ON, Canada.|Department of Entomology, The Ohio State University-Ohio Agricultural Research and Development Center, Wooster, OH, USA.|Forest Soils and Biogeochemistry, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.', 'United States|United States|Switzerland|Sweden|Canada|Sweden|Estonia|Sweden|Canada|United States|Switzerland|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty482', '29912385', 'Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data.'),
(81817, 2018, 'bio', 'DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches', 'Olayan, Rawan S|Ashoor, Haitham|Bajic, Vladimir B', 'Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.', 'Sweden|Sweden|Sweden', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty417', '29917050', '-'),
(81818, 2018, 'bio', 'Optimality and identification of dynamic models in systems biology: an inverse optimal control framework', 'Tsiantis, Nikolaos|Balsa-Canto, Eva|Banga, Julio R', 'Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.|Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.', 'Sweden|Sweden|Sweden', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty438', '29917059', '-'),
(81819, 2018, 'bio', 'HUME: large-scale detection of causal genetic factors of adverse drug reactions', 'Mansouri, Mehrdad|Yuan, Bowei|Ross, Colin J D|Carleton, Bruce C|Ester, Martin', 'Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.|Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.|Child and Family Research Institute, Childrens and Womens Health Research Centre of British Columbia, Vancouver, British Columbia, Canada.|Child and Family Research Institute, Childrens and Womens Health Research Centre of British Columbia, Vancouver, British Columbia, Canada.|Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.', 'Canada|Canada|Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty475', '29931042', 'Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously.'),
(81820, 2018, 'bio', 'mol2sphere: spherical decomposition of multi-domain molecules for visualization and coarse grained spatial modeling', 'Masison, Joseph|Michalski, Paul J|Loew, Leslie M|Schuyler, Adam D', 'R. D. Berlin Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, USA.|R. D. Berlin Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, USA.|R. D. Berlin Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, USA.|Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty487', '29931043', 'Proteins, especially those involved in signaling pathways are composed of functional modules connected by linker domains with varying degrees of flexibility. To understand the structure-function relationships in these macromolecules, it is helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways.'),
(81821, 2019, 'bio', 'Using tree-based methods for detection of gene-gene interactions in the presence of a polygenic signal: simulation study with application to educational attainment in the Generation Scotland Cohort Study', 'Meijsen, Joeri J|Rammos, Alexandros|Campbell, Archie|Hayward, Caroline|Porteous, David J|Deary, Ian J|Marioni, Riccardo E|Nicodemus, Kristin K', 'Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.|Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.|Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty462', '29931044', 'The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of a polygenic signal. We sought to apply decision tree-based methods, C5.0 and logic regression, to detect epistasis under several simulated conditions, varying strength of interaction and linkage disequilibrium (LD) structure. We then applied the same methods to the phenotype of educational attainment in a large population cohort.'),
(81822, 2019, 'bio', 'GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies', 'Sanyal, Nilotpal|Lo, Min-Tzu|Kauppi, Karolina|Djurovic, Srdjan|Andreassen, Ole A|Johnson, Valen E|Chen, Chi-Hua', 'Department of Radiology, University of California, San Diego, La Jolla, CA, USA.|Department of Radiology, University of California, San Diego, La Jolla, CA, USA.|Department of Radiation Sciences, Umeå University, Umeå, Sweden.|Department of Medical Genetics, NORMENT, KG Jebsen Centre, University of Bergen, Bergen, Oslo University Hospital, Oslo, Norway.|Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.|Department of Statistics, Texas A&M University, College Station, TX, USA.|Department of Radiology, University of California, San Diego, La Jolla, CA, USA.', 'United States|United States|Sweden|Norway|Norway|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty472', '29931045', 'Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non-local priors in an iterative variable selection framework.'),
(81823, 2018, 'bio', 'Predicting clone genotypes from tumor bulk sequencing of multiple samples', 'Miura, Sayaka|Gomez, Karen|Murillo, Oscar|Huuki, Louise A|Vu, Tracy|Buturla, Tiffany|Kumar, Sudhir', 'Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.|Institute for Genomics and Evolutionary Medicine.', '-|-|-|-|-|-|-', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty469', '29931046', 'Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.'),
(81824, 2018, 'bio', 'Modelling signalling networks from perturbation data', 'Dorel, Mathurin|Klinger, Bertram|Gross, Torsten|Sieber, Anja|Prahallad, Anirudh|Bosdriesz, Evert|Wessels, Lodewyk F A|Blüthgen, Nils', 'Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, CX, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, CX, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, CX, Amsterdam, The Netherlands.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Netherlands|Netherlands|Netherlands|Germany', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty473', '29931053', 'Intracellular signalling is realized by complex signalling networks, which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.'),
(81825, 2019, 'bio', 'htsget: a protocol for securely streaming genomic data', 'Kelleher, Jerome|Lin, Mike|Albach, C H|Birney, Ewan|Davies, Robert|Gourtovaia, Marina|Glazer, David|Gonzalez, Cristina Y|Jackson, David K|Kemp, Aaron|Marshall, John|Nowak, Andrew|Senf, Alexander|Tovar-Corona, Jaime M|Vikhorev, Alexander|Keane, Thomas M|GA4GH Streaming Task Team', 'Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.|DNAnexus, 1975 West El Camino Real, Suite 101, Mountain View, CA, USA.|Verily Life Sciences LLC, 269 East Grand Avenue, South San Francisco, CA, USA.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Verily Life Sciences LLC, 269 East Grand Avenue, South San Francisco, CA, USA.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Google Inc.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, 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|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty492', '29931085', 'Standardized interfaces for efficiently accessing high-throughput sequencing data are a fundamental requirement for large-scale genomic data sharing. We have developed htsget, a protocol for secure, efficient and reliable access to sequencing read and variation data. We demonstrate four independent client and server implementations, and the results of a comprehensive interoperability demonstration.'),
(81826, 2018, 'bio', 'panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data', 'Treepong, Panisa|Guyeux, Christophe|Meunier, Alexandre|Couchoud, Charlotte|Hocquet, Didier|Valot, Benoit', 'Département DISC, UMR CNRS 6174 Institut FEMTO-ST, Université de Bourgogne, Franche-Comté, Besançon, France.|Département DISC, UMR CNRS 6174 Institut FEMTO-ST, Université de Bourgogne, Franche-Comté, Besançon, France.|Laboratoire dHygiène Hospitalière, Centre Hospitalier Régional Universitaire, Besançon, France.|Laboratoire dHygiène Hospitalière, Centre Hospitalier Régional Universitaire, Besançon, France.|Laboratoire dHygiène Hospitalière, Centre Hospitalier Régional Universitaire, Besançon, France.|UMR CNRS 6249, Chrono-environnement, Université de Bourgogne Franche-Comté, Besançon, France.', 'France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty479', '29931098', 'The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data.'),
(81827, 2018, 'bio', 'Operon-mapper: a web server for precise operon identification in bacterial and archaeal genomes', 'Taboada, Blanca|Estrada, Karel|Ciria, Ricardo|Merino, Enrique', 'Department of Developmental Genetics and Molecular Physiology, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Department of Molecular Microbiology, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Department of Molecular Microbiology, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Department of Molecular Microbiology, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.', '-|-|-|-', 21, '-', 'Journal Article', '10.1093/bioinformatics/bty496', '29931111', 'Operon-mapper is a web server that accurately, easily and directly predicts the operons of any bacterial or archaeal genome sequence. The operon predictions are based on the intergenic distance of neighboring genes as well as the functional relationships of their protein-coding products. To this end, Operon-mapper finds all the ORFs within a given nucleotide sequence, along with their genomic coordinates, orthology groups and functional relationships. We believe that Operon-mapper, due to its accuracy, simplicity and speed, as well as the relevant information that it generates, will be a useful tool for annotating and characterizing genomic sequences.'),
(81828, 2018, 'bio', 'Deep convolutional networks for quality assessment of protein folds', 'Derevyanko, Georgy|Grudinin, Sergei|Bengio, Yoshua|Lamoureux, Guillaume', 'Department of Chemistry and Biochemistry and Centre for Research in Molecular Modeling (CERMM), Concordia University, Montréal, Québec, Canada.|Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France.|Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada.|Department of Chemistry and Biochemistry and Centre for Research in Molecular Modeling (CERMM), Concordia University, Montréal, Québec, Canada.', 'Canada|France|Canada|Canada', 17, '-', 'Journal Article', '10.1093/bioinformatics/bty494', '29931128', 'The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data.'),
(81829, 2018, 'bio', 'Prediction of kinetics of protein folding with non-redundant contact information', 'Censoni, Luciano|Martínez, Leandro', 'Institute of Chemistry and Center for Computational Engineering and Science, University of Campinas, Campinas, SP, Brazil.|Institute of Chemistry and Center for Computational Engineering and Science, University of Campinas, Campinas, SP, Brazil.', 'Brazil|Brazil', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty478', '29931141', 'The majority of the inter-residue distances in a protein structure are correlated given a fixed topology. Here, we investigate whether we are able to predict a structures folding rate, which is known to depend on the complexity of its fold, while considering only a small, uncorrelated fraction of its contacts.'),
(81830, 2018, 'bio', 'SpliceRover: interpretable convolutional neural networks for improved splice site prediction', 'Zuallaert, Jasper|Godin, Fréderic|Kim, Mijung|Soete, Arne|Saeys, Yvan|De Neve, Wesley', 'Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.|IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium.|Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.|Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.|Data Mining and Modeling for Biomedicine, VIB Inflammation Research Center, Ghent, Belgium.|Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.', '-|Belgium|-|Belgium|Belgium|-', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty497', '29931149', 'During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81831, 2018, 'bio', 'iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC', 'Su, Zhen-Dong|Huang, Yan|Zhang, Zhao-Yue|Zhao, Ya-Wei|Wang, Dong|Chen, Wei|Chou, Kuo-Chen|Lin, Hao', '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, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 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, 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, 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, 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, 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, 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, China.', 'China|China|China|China|China|China|China|China', 108, '-', 'Journal Article', '10.1093/bioinformatics/bty508', '29931187', 'Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations.'),
(81832, 2019, 'bio', '3DPatch: fast 3D structure visualization with residue conservation', 'Jakubec, David|Vondrášek, Jirí|Finn, Robert D', 'Department of Bioinformatics, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague 6, 166 10, Czech Republic.|Department of Bioinformatics, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague 6, 166 10, Czech Republic.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty464', '29931189', 'Amino acid residues showing above background levels of conservation are often indicative of functionally significant regions within a protein. Understanding how the sequence conservation profile relates in space requires projection onto a protein structure, a potentially time-consuming process. 3DPatch is a web application that streamlines this task by automatically generating multiple sequence alignments (where appropriate) and finding structural homologs, presenting the user with a choice of structures matching their query, annotated with residue conservation scores in a matter of seconds.'),
(81833, 2018, 'bio', 'Grimon: graphical interface to visualize multi-omics networks', 'Kanai, Masahiro|Maeda, Yuichi|Okada, Yukinori', 'Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.|Laboratory of Immune Regulation Graduate School of Medicine, Department of Microbiology and Immunology, WPI Immunology Frontier Research Center, Osaka University, Suita, Japan.|Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.', 'Japan|Japan|Japan', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty488', '29931190', 'Rapid advances in high-throughput sequencing technologies have enabled more efficient acquisition of massive amount of multi-omics data. However, interpretation of the underlying relationships across multi-omics networks has not been fully succeeded, partly due to the lack of effective methods in visualization. To aid interpretation of the results from such multi-omics data, we here present Grimon (Graphical interface to visualize multi-omics networks), an R package that visualizes high-dimensional multi-layered data sets in three-dimensional parallel coordinates. Grimon enables users to intuitively and interactively explore their analyzed data, helping their understanding of multiple inter-layer connections embedded in high-dimensional complex data.'),
(81834, 2018, 'bio', 'SONiCS: PCR stutter noise correction in genome-scale microsatellites', 'Kedzierska, Katarzyna Z|Gerber, Livia|Cagnazzi, Daniele|Krützen, Michael|Ratan, Aakrosh|Kistler, Logan', 'Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.|Evolutionary Genetics Group, Department of Anthropology, University of Zurich, CH-8057 Zurich, Switzerland.|Marine Ecology Research Centre, School of Environment, Science, and Engineering, Southern Cross University, Lismore, Australia.|Evolutionary Genetics Group, Department of Anthropology, University of Zurich, CH-8057 Zurich, Switzerland.|Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.|Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.', 'United States|Switzerland|Australia|Switzerland|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty485', '29931218', 'Massively parallel capture of short tandem repeats (STRs, or microsatellites) provides a strategy for population genomic and demographic analyses at high resolution with or without a reference genome. However, the high Polymerase Chain Reaction (PCR) cycle numbers needed for target capture experiments create genotyping noise through polymerase slippage known as PCR stutter.'),
(81835, 2018, 'bio', 'Estimating pseudocounts and fold changes for digital expression measurements', 'Erhard, Florian', 'Institut fxc3xbcr Virologie und Immunbiologie, Julius-Maximilians-Universitxc3xa4t Wxc3xbcrzburg, 97078 Wxc3xbcrzburg, Germany.', 'Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty471', '29931226', 'Fold changes from count based high-throughput experiments such as RNA-seq suffer from a zero-frequency problem. To circumvent division by zero, so-called pseudocounts are added to make all observed counts strictly positive. The magnitude of pseudocounts for digital expression measurements and on which stage of the analysis they are introduced remained an arbitrary choice. Moreover, in the strict sense, fold changes are not quantities that can be computed. Instead, due to the stochasticity involved in the experiments, they must be estimated by statistical inference.'),
(81836, 2018, 'bio', '13Check_RNA: a tool to evaluate 13C chemical shift assignments of RNA', 'Icazatti, A A|Martin, O A|Villegas, M|Szleifer, I|Vila, J A', 'Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.|Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.|Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.|Department of Biomedical Engineering.|Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.', 'Argentina|Argentina|Argentina|-|Argentina', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty470', '29931233', 'Chemical shifts (CS) are an important source of structural information of macromolecules such as RNA. In addition to the scarce availability of CS for RNA, the observed values are prone to errors due to a wrong re-calibration or miss assignments. Different groups have dedicated their efforts to correct CS systematic errors on RNA. Despite this, there are not automated and freely available algorithms for evaluating the referencing of RNA 13xe2x80x89C CS before their deposition to the BMRB or re-reference already deposited CS with systematic errors.'),
(81837, 2018, 'bio', 'GIFT: Guided and Interpretable Factorization for Tensors with an application to large-scale multi-platform cancer analysis', 'Lee, Jungwoo|Oh, Sejoon|Sael, Lee', 'Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.|Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.|Instituto de Matemxc3xa1tica Aplicada San Luis, Universidad Nacional de San Luis, CONICET, San Luis, Argentina.', 'Argentina|Argentina|Argentina', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty490', '29931238', 'Given multi-platform genome data with prior knowledge of functional gene sets, how can we extract interpretable latent relationships between patients and genes? More specifically, how can we devise a tensor factorization method which produces an interpretable gene factor matrix based on functional gene set information while maintaining the decomposition quality and speed?'),
(81838, 2018, 'bio', 'MolArt: a molecular structure annotation and visualization tool', 'Hoksza, David|Gawron, Piotr|Ostaszewski, Marek|Schneider, Reinhard', 'Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty489', '29931246', 'MolArt fills the gap between sequence and structure visualization by providing a light-weight, interactive environment enabling exploration of sequence annotations in the context of available experimental or predicted protein structures. Provided a UniProt ID, MolArt downloads and displays sequence annotations, sequence-structure mapping and relevant structures. The sequence and structure views are interlinked, enabling sequence annotations being color overlaid over the mapped structures, thus providing an enhanced understanding and interpretation of the available molecular data.'),
(81839, 2018, 'bio', 'ICBdocker: a Docker image for proteome annotation and visualization', 'Bordin, Nicola|Devos, Damien P', 'Laboratory of Evolutionary Innovations, Centro Andaluz de Biologia del Desarollo, CSIC, Universidad Pablo de Olavide, Carretera de Utrera, Seville, Spain.|Laboratory of Evolutionary Innovations, Centro Andaluz de Biologia del Desarollo, CSIC, Universidad Pablo de Olavide, Carretera de Utrera, Seville, Spain.', 'Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty493', '29931249', 'We introduce ICBdocker, a Docker environment that allows the annotation of functional and structural features of proteomes through a Python/Perl pipeline. DataTables pages make it easy to set up a web-resource for research groups with a focus on the same organisms or datasets. The results are available as tab-separated values files and HTML, allowing data analysis and browsing. The pipeline focuses on modularity and scalability, with capability of integrating with multi-processing and high-performance computing clusters.'),
(81840, 2018, 'bio', 'BioStructMap: a Python tool for integration of protein structure and sequence-based features', 'Guy, Andrew J|Irani, Vashti|Richards, Jack S|Ramsland, Paul A', 'Life Sciences, Burnet Institute, Melbourne, VIC, Australia.|Life Sciences, Burnet Institute, Melbourne, VIC, Australia.|Life Sciences, Burnet Institute, Melbourne, VIC, Australia.|Life Sciences, Burnet Institute, Melbourne, VIC, Australia.', 'Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty474', '29931276', 'A sliding window analysis over a protein or genomic sequence is commonly performed, and we present a Python tool, BioStructMap, that extends this concept to three-dimensional (3D) space, allowing the application of a 3D sliding window analysis over a protein structure. BioStructMap is easily extensible, allowing the user to apply custom functions to spatially aggregated data. BioStructMap also allows mapping of underlying genomic sequences to protein structures, allowing the user to perform genetic-based analysis over spatially linked codons-this has applications when selection pressures arise at the level of protein structure.'),
(81841, 2018, 'bio', 'Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks', 'Hanson, Jack|Paliwal, Kuldip|Litfin, Thomas|Yang, Yuedong|Zhou, Yaoqi', 'Signal Processing Laboratory, Griffith University, Brisbane, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia.', 'Australia|Australia|Australia|Australia|Australia', 39, '-', 'Journal Article', '10.1093/bioinformatics/bty481', '29931279', 'Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here, we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information.'),
(81842, 2018, 'bio', 'Meffil: efficient normalization and analysis of very large DNA methylation datasets', 'Min, J L|Hemani, G|Davey Smith, G|Relton, C|Suderman, M', 'MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, '-', 'Journal Article', '10.1093/bioinformatics/bty476', '29931280', 'DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data.'),
(81843, 2018, 'bio', 'PASTA for proteins', 'Collins, Kodi|Warnow, Tandy', 'Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of Illinois, Urbana, IL, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty495', '29931282', 'PASTA is a multiple sequence method that uses divide-and-conquer plus iteration to enable base alignment methods to scale with high accuracy to large sequence datasets. By default, PASTA included MAFFT L-INS-i, our new extension of PASTA enables the use of MAFFT G-INS-i, MAFFT Homologs, CONTRAlign and ProbCons. We analyzed the performance of each base method and PASTA using these base methods on 224 datasets from BAliBASE 4 with at least 50 sequences. We show that PASTA enables the most accurate base methods to scale to larger datasets at reduced computational effort, and generally improves alignment and tree accuracy on the largest BAliBASE datasets.'),
(81844, 2018, 'bio', 'snpAD: an ancient DNA genotype caller', 'Prüfer, Kay', 'Department for Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.', 'Germany', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty507', '29931305', 'The study of ancient genomes can elucidate the evolutionary past. However, analyses are complicated by base-modifications in ancient DNA molecules that result in errors in DNA sequences. These errors are particularly common near the ends of sequences and pose a challenge for genotype calling.'),
(81845, 2019, 'bio', 'Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting', 'Becht, Etienne|Simoni, Yannick|Coustan-Smith, Elaine|Evrard, Maximilien|Cheng, Yang|Ng, Lai Guan|Campana, Dario|Newell, Evan W', 'Singapore Immunology Network, Agency for Science Technology and Research, Singapore.|Singapore Immunology Network, Agency for Science Technology and Research, Singapore.|Department of Paediatrics, National University of Singapore, Singapore.|Singapore Immunology Network, Agency for Science Technology and Research, Singapore.|Singapore Immunology Network, Agency for Science Technology and Research, Singapore.|Singapore Immunology Network, Agency for Science Technology and Research, Singapore.|Department of Paediatrics, National University of Singapore, Singapore.|Singapore Immunology Network, Agency for Science Technology and Research, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty491', '29931307', 'Recent flow and mass cytometers generate datasets of dimensions 20 to 40 and a million single cells. From these, many tools facilitate the discovery of new cell populations associated with diseases or physiology. These new cell populations require the identification of new gating strategies, but gating strategies become exponentially more difficult to optimize when dimensionality increases. To facilitate this step, we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.'),
(81846, 2018, 'bio', 'SCIP: a single-cell image processor toolbox', 'Martins, Leonardo|Neeli-Venkata, Ramakanth|Oliveira, Samuel M D|Häkkinen, Antti|Ribeiro, Andre S|Fonseca, José M', 'Faculty of Biomedical Sciences and Engineering and Laboratory of Biosystem Dynamics of the BioMediTech Institute, Tampere University of Technology, Tampere, Finland.|Faculty of Biomedical Sciences and Engineering and Laboratory of Biosystem Dynamics of the BioMediTech Institute, Tampere University of Technology, Tampere, Finland.|Faculty of Biomedical Sciences and Engineering and Laboratory of Biosystem Dynamics of the BioMediTech Institute, Tampere University of Technology, Tampere, Finland.|Faculty of Biomedical Sciences and Engineering and Laboratory of Biosystem Dynamics of the BioMediTech Institute, Tampere University of Technology, Tampere, Finland.|Faculty of Biomedical Sciences and Engineering and Laboratory of Biosystem Dynamics of the BioMediTech Institute, Tampere University of Technology, Tampere, Finland.|Faculty of Sciences and Technology, Department of Electrical Engineering and CA3 Group of UNINOVA/CTS, NOVA University of Lisbon, Caparica, Portugal.', 'Finland|Finland|Finland|Finland|Finland|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty505', '29931314', 'Each cell is a phenotypically unique individual that is influenced by internal and external processes, operating in parallel. To characterize the dynamics of cellular processes one needs to observe many individual cells from multiple points of view and over time, so as to identify commonalities and variability. With this aim, we engineered a software, SCIP, to analyze multi-modal, multi-process, time-lapse microscopy morphological and functional images. SCIP is capable of automatic and/or manually corrected segmentation of cells and lineages, automatic alignment of different microscopy channels, as well as detect, count and characterize fluorescent spots (such as RNA tagged by MS2-GFP), nucleoids, Z rings, Min system, inclusion bodies, undefined structures, etc. The results can be exported into *mat files and all results can be jointly analyzed, to allow studying not only each feature and process individually, but also find potential relationships. While we exemplify its use on Escherichia coli, many of its functionalities are expected to be of use in analyzing other prokaryotes and eukaryotic cells as well. We expect SCIP to facilitate the finding of relationships between cellular processes, from small-scale (e.g. gene expression) to large-scale (e.g. cell division), in single cells and cell lineages.'),
(81847, 2018, 'bio', 'Learned protein embeddings for machine learning', 'Yang, Kevin K|Wu, Zachary|Bedbrook, Claire N|Arnold, Frances H', 'Faculty of Sciences and Technology, Department of Electrical Engineering and CA3 Group of UNINOVA/CTS, NOVA University of Lisbon, Caparica, Portugal.|Faculty of Sciences and Technology, Department of Electrical Engineering and CA3 Group of UNINOVA/CTS, NOVA University of Lisbon, Caparica, Portugal.|Faculty of Sciences and Technology, Department of Electrical Engineering and CA3 Group of UNINOVA/CTS, NOVA University of Lisbon, Caparica, Portugal.|Faculty of Sciences and Technology, Department of Electrical Engineering and CA3 Group of UNINOVA/CTS, NOVA University of Lisbon, Caparica, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty455', '29933431', '-'),
(81848, 2018, 'bio', 'MetaPOAP: presence or absence of metabolic pathways in metagenome-assembled genomes', 'Ward, Lewis M|Shih, Patrick M|Fischer, Woodward W', 'Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.|Environmental Genomics and Systems Biology Division, E O Lawrence Berkeley National Laboratory, Berkeley, CA.|Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA.', 'United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty510', '29939199', 'Genome-resolved metagenomics allows the construction of draft microbial genomes from short-read shotgun metagenomics (Metagenome-Assembled Genomes, or MAGs), however, even high-quality MAGs are typically somewhat incomplete and contain a small amount of contaminant sequence, making accurate prediction of metabolic potential challenging. Here, we describe MetaPOAP, an algorithm for probabalistic assessment of the statistical likelihoods for the presence or absence of metabolic pathways in MAGs.'),
(81849, 2019, 'bio', 'A descriptive marker gene approach to single-cell pseudotime inference', 'Campbell, Kieran R|Yau, Christopher', 'Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty498', '29939207', 'Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasize an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour.'),
(81850, 2018, 'bio', 'SOPanG: online text searching over a pan-genome', 'Cislak, Aleksander|Grabowski, Szymon|Holub, Jan', 'Institute of Applied Computer Science, Lodz University of Technology, Lódz, Poland.|Institute of Applied Computer Science, Lodz University of Technology, Lódz, Poland.|Faculty of Information Technology, Czech Technical University in Prague, Czechia.', 'Poland|Poland|Czechia', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty506', '29939210', 'The many thousands of high-quality genomes available now-a-days imply a shift from single genome to pan-genomic analyses. A basic algorithmic building brick for such a scenario is online search over a collection of similar texts, a problem with surprisingly few solutions presented so far.'),
(81851, 2018, 'bio', 'runibic: a Bioconductor package for parallel row-based biclustering of gene expression data', 'Orzechowski, Patryk|Panszczyk, Artur|Huang, Xiuzhen|Moore, Jason H', 'Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland.|Department of Computer Science, Arkansas State University, Jonesboro, AR, USA.|Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|Poland|United States|United States', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty512', '29939213', 'Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far.'),
(81852, 2018, 'bio', 'Practical dynamic de Bruijn graphs', 'Crawford, Victoria G|Kuhnle, Alan|Boucher, Christina|Chikhi, Rayan|Gagie, Travis', 'Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|CNRS, CRIStAL, University of Lille, Lille, France.|CeBiB and School of Computer Science and Engineering, Diego Portales University, Santiago, Chile.', 'United States|United States|United States|France|Chile', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty500', '29939217', 'The de Bruijn graph is fundamental to the analysis of next generation sequencing data and so, as datasets of DNA reads grow rapidly, it becomes more important to represent de Bruijn graphs compactly while still supporting fast assembly. Previous implementations of compact de Bruijn graphs have not supported node or edge deletion, however, which is important for pruning spurious elements from the graph.'),
(81853, 2018, 'bio', 'Robust clustering of noisy high-dimensional gene expression data for patients subtyping', 'Coretto, Pietro|Serra, Angela|Tagliaferri, Roberto', 'Department of Economics and Statistics, STATLAB, University of Salerno, Fisciano, SA, Italy.|Department of Management and Innovation Systems, NeuRoNeLab, University of Salerno, Fisciano, SA, Italy.|Department of Management and Innovation Systems, NeuRoNeLab, University of Salerno, Fisciano, SA, Italy.', 'Italy|Italy|Italy', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty502', '29939219', 'One of the most important research areas in personalized medicine is the discovery of disease sub-types with relevance in clinical applications. This is usually accomplished by exploring gene expression data with unsupervised clustering methodologies. Then, with the advent of multiple omics technologies, data integration methodologies have been further developed to obtain better performances in patient separability. However, these methods do not guarantee the survival separability of the patients in different clusters.'),
(81854, 2019, 'bio', 'Tightly integrated genomic and epigenomic data mining using tensor decomposition', 'Fang, Jianwen', 'Computational & Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr., Rockville, MD, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty513', '29939222', 'Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities.'),
(81855, 2018, 'bio', 'Predicting miRNA-disease association based on inductive matrix completion', 'Chen, Xing|Wang, Lei|Qu, Jia|Guan, Na-Na|Li, Jian-Qiang', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.', 'China|China|China|China|China', 147, '-', 'Journal Article', '10.1093/bioinformatics/bty503', '29939227', 'It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement.'),
(81856, 2018, 'bio', 'Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network', 'Ahmed, Ibrahim|Witbooi, Peter|Christoffels, Alan', 'South African National Bioinformatics Institute, South African MRC Bioinformatics Unit.|Department of Mathematics and Applied Mathematics, University of the Western Cape, Bellville, South Africa.|South African National Bioinformatics Institute, South African MRC Bioinformatics Unit.', 'South Africa|South Africa|South Africa', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty504', '29945178', 'Triplet amino acids have successfully been included in feature selection to predict human-HPV protein-protein interactions (PPI). The utility of supervised learning methods is curtailed due to experimental data not being available in sufficient quantities. Improvements in machine learning techniques and features selection will enhance the study of PPI between host and pathogen.'),
(81857, 2019, 'bio', 'ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation', 'Juul, Malene|Madsen, Tobias|Guo, Qianyun|Bertl, Johanna|Hobolth, Asger|Kellis, Manolis|Pedersen, Jakob Skou', 'Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.|Bioinformatics Research Centre, Aarhus University, C.F. Mollers Alle 8, DK-8000 Aarhus C, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.|Bioinformatics Research Centre, Aarhus University, C.F. Mollers Alle 8, DK-8000 Aarhus C, Denmark.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|United States|Denmark', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty511', '29945188', 'Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources.'),
(81858, 2018, 'bio', 'appreci8: a pipeline for precise variant calling integrating 8 tools', 'Sandmann, Sarah|Karimi, Mohsen|de Graaf, Aniek O|Rohde, Christian|Göllner, Stefanie|Varghese, Julian|Ernsting, Jan|Walldin, Gunilla|van der Reijden, Bert A|Müller-Tidow, Carsten|Malcovati, Luca|Hellström-Lindberg, Eva|Jansen, Joop H|Dugas, Martin', 'Institute of Medical Informatics, University of Münster, Münster, Germany.|Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.|Laboratory Hematology, RadboudUMC, Nijmegen GA, The Netherlands.|Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.|Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.|Institute of Medical Informatics, University of Münster, Münster, Germany.|Institute of Medical Informatics, University of Münster, Münster, Germany.|Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.|Laboratory Hematology, RadboudUMC, Nijmegen GA, The Netherlands.|Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.|Departments of Hematology Oncology & Molecular Medicine, Fondazione IRCCS Policlinico San Matteo & University of Pavia, Pavia, Italy.|Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.|Laboratory Hematology, RadboudUMC, Nijmegen GA, The Netherlands.|Institute of Medical Informatics, University of Münster, Münster, Germany.', 'Germany|Sweden|Netherlands|Germany|Germany|Germany|Germany|Sweden|Netherlands|Germany|Italy|Sweden|N', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty518', '29945233', 'The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.'),
(81859, 2018, 'bio', 'GeneCT: a generalizable cancerous status and tissue origin classifier for pan-cancer biopsies', 'Sun, Kun|Wang, Jiguang|Wang, Huating|Sun, Hao', 'Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China.|Divison of Life Science and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China.', 'China|China|China|China', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty524', '29947737', 'Tissue biopsy is commonly used in cancer diagnosis and molecular studies. However, advanced skills are required for determining cancerous status of biopsies and tissue origin of tumor for cancerous ones. Correct classification is essential for downstream experiment design and result interpretation, especially in molecular cancer studies. Methods for accurate classification of cancerous status and tissue origin for pan-cancer biopsies are thus urgently needed.'),
(81860, 2019, 'bio', 'The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis', 'Gil, Nelson|Fiser, Andras', 'Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.|Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty523', '29947739', 'The analysis of sequence conservation patterns has been widely utilized to identify functionally important (catalytic and ligand-binding) protein residues for over a half-century. Despite decades of development, on average state-of-the-art non-template-based functional residue prediction methods must predict xe2x88xbc25% of a proteins total residues to correctly identify half of the proteins functional site residues. The overwhelming proportion of false positives results in reported F-Scores of xe2x88xbc0.3. We investigated the limits of current approaches, focusing on the so-far neglected impact of the specific choice of homologs included in multiple sequence alignments (MSAs).'),
(81861, 2018, 'bio', 'BMC3C: binning metagenomic contigs using codon usage, sequence composition and read coverage', 'Yu, Guoxian|Jiang, Yuan|Wang, Jun|Zhang, Hao|Luo, Haiwei', 'College of Computer and Information Science, Southwest University, Chongqing, China.|College of Computer and Information Science, Southwest University, Chongqing, China.|College of Computer and Information Science, Southwest University, Chongqing, China.|School of Life Sciences and Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.|School of Life Sciences and Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.', 'China|China|China|China|China', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty519', '29947757', 'Metagenomics investigates the DNA sequences directly recovered from environmental samples. It often starts with reads assembly, which leads to contigs rather than more complete genomes. Therefore, contig binning methods are subsequently used to bin contigs into genome bins. While some clustering-based binning methods have been developed, they generally suffer from problems related to stability and robustness.'),
(81862, 2018, 'bio', 'Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome', 'Li, Fuyi|Li, Chen|Marquez-Lago, Tatiana T|Leier, André|Akutsu, Tatsuya|Purcell, Anthony W|Ian Smith, A|Lithgow, Trevor|Daly, Roger J|Song, Jiangning|Chou, Kuo-Chen', 'Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Gordon Life Science Institute, Boston, MA, USA.', 'Australia|Australia|United States|United States|Japan|Australia|Australia|Australia|Australia|Australia|United States', 65, '-', 'Journal Article', '10.1093/bioinformatics/bty522', '29947803', 'Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis.'),
(81863, 2018, 'bio', 'PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts', 'Swainston, Neil|Dunstan, Mark|Jervis, Adrian J|Robinson, Christopher J|Carbonell, Pablo|Williams, Alan R|Faulon, Jean-Loup|Scrutton, Nigel S|Kell, Douglas B', 'Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.|Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Algorithms|DNA|Software|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/bty105', '29949952', 'Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. PartsGenie is introduced to facilitate the computational design of such synthetic biology parts, bridging the gap between optimization tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language, and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a number of DNA optimization algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimization and dissemination of reusable synthetic biology parts through an integrated application.'),
(81864, 2018, 'bio', 'MARSI: metabolite analogues for rational strain improvement', 'Cardoso, João G R|Zeidan, Ahmad A|Jensen, Kristian|Sonnenschein, Nikolaus|Neves, Ana Rute|Herrgård, Markus J', 'The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.|Discovery, Chr. Hansen A/S, Hørsholm, Denmark.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.|Discovery, Chr. Hansen A/S, Hørsholm, Denmark.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 3, 'Databases, Factual|Genome|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/bty108', '29949953', 'Metabolite analogues (MAs) mimic the structure of native metabolites, can competitively inhibit their utilization in enzymatic reactions, and are commonly used as selection tools for isolating desirable mutants of industrial microorganisms. Genome-scale metabolic models representing all biochemical reactions in an organism can be used to predict effects of MAs on cellular phenotypes. Here, we present the metabolite analogues for rational strain improvement (MARSI) framework. MARSI provides a rational approach to strain improvement by searching for metabolites as targets instead of genes or reactions. The designs found by MARSI can be implemented by supplying MAs in the culture media, enabling metabolic rewiring without the use of recombinant DNA technologies that cannot always be used due to regulations. To facilitate experimental implementation, MARSI provides tools to identify candidate MAs to a target metabolite from a database of known drugs and analogues.'),
(81865, 2018, 'bio', 'nVenn: generalized, quasi-proportional Venn and Euler diagrams', 'Pxc3xa9rez-Silva, Josxc3xa9 G|Araujo-Voces, Miguel|Quesada, Vxc3xadctor', 'Departamento de Bioquxc3xadmica y Biologxc3xada Molecular, Universidad de Oviedo, Oviedo, Spain.|Departamento de Bioquxc3xadmica y Biologxc3xada Molecular, Universidad de Oviedo, Oviedo, Spain.|Departamento de Bioquxc3xadmica y Biologxc3xada Molecular, Universidad de Oviedo, Oviedo, Spain.', 'Spain|Spain|Spain', 14, 'Algorithms|Software', 'Journal Article', '10.1093/bioinformatics/bty109', '29949954', 'Venn and Euler diagrams are extensively used for the visualization of relationships between experiments and datasets. However, representing more than three datasets while keeping the proportions of each region is still not feasible with existing tools.'),
(81866, 2018, 'bio', 'BEL2ABM: agent-based simulation of static models in Biological Expression Language', 'Gündel, Michaela|Hoyt, Charles Tapley|Hofmann-Apitius, Martin', 'Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.', 'Germany|Germany|Germany', 0, 'Documentation|Humans|Language|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/bty107', '29949955', 'While cause-and-effect knowledge assembly models encoded in Biological Expression Language are able to support generation of mechanistic hypotheses, they are static and limited in their ability to encode temporality. Here, we present BEL2ABM, a software for producing continuous, dynamic, executable agent-based models from BEL templates.'),
(81867, 2018, 'bio', 'AmpUMI: design and analysis of unique molecular identifiers for deep amplicon sequencing', 'Clement, Kendell|Farouni, Rick|Bauer, Daniel E|Pinello, Luca', 'Molecular Pathology Unit and Cancer Center, Massachusetts General Hospital, Boston, MA, USA.|Molecular Pathology Unit and Cancer Center, Massachusetts General Hospital, Boston, MA, USA.|Division of Hematology/Oncology, Boston Childrens Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.|Molecular Pathology Unit and Cancer Center, Massachusetts General Hospital, Boston, MA, USA.', 'United States|United States|United States|United States', 5, 'Gene Frequency|Genomics|High-Throughput Nucleotide Sequencing|Models, Genetic|Sequence Analysis, DNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty264', '29949956', 'Unique molecular identifiers (UMIs) are added to DNA fragments before PCR amplification to discriminate between alleles arising from the same genomic locus and sequencing reads produced by PCR amplification. While computational methods have been developed to take into account UMI information in genome-wide and single-cell sequencing studies, they are not designed for modern amplicon-based sequencing experiments, especially in cases of high allelic diversity. Importantly, no guidelines are provided for the design of optimal UMI length for amplicon-based sequencing experiments.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81868, 2018, 'bio', 'A novel methodology on distributed representations of proteins using their interacting ligands', 'Öztürk, Hakime|Ozkirimli, Elif|Özgür, Arzucan', 'Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.|Department of Computer Engineering, Bogazici University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty287', '29949957', 'The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be utilized in protein representation. In this study, we propose SMILESVec, a Simplified molecular input line entry system (SMILES)-based method to represent ligands and a novel method to compute similarity of proteins by describing them based on their ligands. The proteins are defined utilizing the word-embeddings of the SMILES strings of their ligands. The performance of the proposed protein description method is evaluated in protein clustering task using TransClust and MCL algorithms. Two other protein representation methods that utilize protein sequence, Basic local alignment tool and ProtVec, and two compound fingerprint-based protein representation methods are compared.'),
(81869, 2018, 'bio', 'A unifying framework for joint trait analysis under a non-infinitesimal model', 'Johnson, Ruth|Shi, Huwenbo|Pasaniuc, Bogdan|Sankararaman, Sriram', 'Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.', 'United States|United States|United States|United States', 2, 'Bayes Theorem|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty254', '29949958', 'A large proportion of risk regions identified by genome-wide association studies (GWAS) are shared across multiple diseases and traits. Understanding whether this clustering is due to sharing of causal variants or chance colocalization can provide insights into shared etiology of complex traits and diseases.'),
(81870, 2018, 'bio', 'COSSMO: predicting competitive alternative splice site selection using deep learning', 'Bretschneider, Hannes|Gandhi, Shreshth|Deshwar, Amit G|Zuberi, Khalid|Frey, Brendan J', 'Deep Genomics Inc, Toronto, Canada.|Deep Genomics Inc, Toronto, Canada.|Deep Genomics Inc, Toronto, Canada.|Deep Genomics Inc, Toronto, Canada.|Deep Genomics Inc, Toronto, Canada.', 'Canada|Canada|Canada|Canada|Canada', 11, 'Alternative Splicing|Computational Biology|Deep Learning|Humans|Models, Genetic|Probability|RNA Splice Sites|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty244', '29949959', 'Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends on the strength of neighboring sites. Here, we present a new model named the competitive splice site model (COSSMO), which explicitly accounts for these competitive effects and predicts the percent selected index (PSI) distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3 acceptor site conditional on a fixed upstream 5 donor site or the choice of a 5 donor site conditional on a fixed 3 acceptor site. We build four different architectures that use convolutional layers, communication layers, long short-term memory and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model.'),
(81871, 2018, 'bio', 'aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences', 'Glouzon, Jean-Pierre Séhi|Ouangraoua, Aïda', 'Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada.|Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada.', 'Canada|Canada', 2, 'Algorithms|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty234', '29949960', 'Predicting the conserved secondary structure of homologous ribonucleic acid (RNA) sequences is crucial for understanding RNA functions. However, fast and accurate RNA structure prediction is challenging, especially when the number and the divergence of homologous RNA increases. To address this challenge, we propose aliFreeFold, based on a novel alignment-free approach which computes a representative structure from a set of homologous RNA sequences using sub-optimal secondary structures generated for each sequence. It is based on a vector representation of sub-optimal structures capturing structure conservation signals by weighting structural motifs according to their conservation across the sub-optimal structures.'),
(81872, 2018, 'bio', 'DisruPPI: structure-based computational redesign algorithm for protein binding disruption', 'Choi, Yoonjoo|Furlon, Jacob M|Amos, Ryan B|Griswold, Karl E|Bailey-Kellogg, Chris', 'Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.|Thayer School of Engineering, Dartmouth, Hanover, NH, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.|Thayer School of Engineering, Dartmouth, Hanover, NH, USA.|Department of Computer Science, Dartmouth, Hanover, NH, USA.', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Antibodies|Computational Biology|Green Fluorescent Proteins|Mutation|Protein Binding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty274', '29949961', 'Disruption of protein-protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein-Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.'),
(81873, 2018, 'bio', 'PrimAlign: PageRank-inspired Markovian alignment for large biological networks', 'Kalecky, Karel|Cho, Young-Rae', 'Institute of Biomedical Studies, Baylor University, Waco, TX, USA.|Department of Computer Science, Baylor University, Waco, TX, USA.', 'United States|United States', 7, 'Algorithms|Animals|Computational Biology|Drosophila|Humans|Markov Chains|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Software', 'Comparative Study', '10.1093/bioinformatics/bty288', '29949962', 'Cross-species analysis of large-scale protein-protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved interactions and functions of proteins. These approaches are based on the notion that orthologous proteins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge.'),
(81874, 2018, 'bio', 'Quantifying the similarity of topological domains across normal and cancer human cell types', 'Sauerwald, Natalie|Kingsford, Carl', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States', 7, 'Cell Line|Cell Line, Tumor|Chromatin|Chromosomes, Human|Genomics|Humans|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty265', '29949963', 'Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.'),
(81875, 2018, 'bio', 'Novo&Stitch: accurate reconciliation of genome assemblies via optical maps', 'Pan, Weihua|Wanamaker, Steve I|Ah-Fong, Audrey M V|Judelson, Howard S|Lonardi, Stefano', 'Department of Computer Science and Engineering, UC Riverside, CA, USA.|Department of Botany and Plant Sciences, UC Riverside, CA, USA.|Department of Plant Pathology and Microbiology, UC Riverside, CA, USA.|Department of Plant Pathology and Microbiology, UC Riverside, CA, USA.|Department of Computer Science and Engineering, UC Riverside, CA, USA.', 'United States|United States|United States|United States|United States', 8, 'Contig Mapping|Eukaryota|Genome|Phytophthora infestans|Sequence Analysis, DNA|Software|Vigna', 'Journal Article', '10.1093/bioinformatics/bty255', '29949964', 'De novo genome assembly is a challenging computational problem due to the high repetitive content of eukaryotic genomes and the imperfections of sequencing technologies (i.e. sequencing errors, uneven sequencing coverage and chimeric reads). Several assembly tools are currently available, each of which has strengths and weaknesses in dealing with the trade-off between maximizing contiguity and minimizing assembly errors (e.g. mis-joins). To obtain the best possible assembly, it is common practice to generate multiple assemblies from several assemblers and/or parameter settings and try to identify the highest quality assembly. Unfortunately, often there is no assembly that both maximizes contiguity and minimizes assembly errors, so one has to compromise one for the other.'),
(81876, 2018, 'bio', 'Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints', 'Ludwig, Marcus|Dührkop, Kai|Böcker, Sebastian', 'Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.', 'Germany|Germany|Germany', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty245', '29949965', 'Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Recently, we presented CSI:FingerID for searching in molecular structure databases using tandem mass spectrometry data. CSI:FingerID predicts a molecular fingerprint that encodes the structure of the query compound, then uses this to search a molecular structure database such as PubChem. Scoring of the predicted query fingerprint and deterministic target fingerprints is carried out assuming independence between the molecular properties constituting the fingerprint.'),
(81877, 2018, 'bio', 'DeepFam: deep learning based alignment-free method for protein family modeling and prediction', 'Seo, Seokjun|Oh, Minsik|Park, Youngjune|Kim, Sun', 'Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.', '-|-|-|-', 0, 'Deep Learning|Proteins|Receptors, G-Protein-Coupled|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty275', '29949966', 'A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and k-mer based methods. Nevertheless, existing methods have some limitations, k-mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed.'),
(81878, 2018, 'bio', 'Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information', 'Zakeri, Pooya|Simm, Jaak|Arany, Adam|ElShal, Sarah|Moreau, Yves', 'Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 4, 'Algorithms|Bayes Theorem|Computational Biology|Gene Ontology|Genetic Predisposition to Disease|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/bty289', '29949967', 'Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.'),
(81879, 2018, 'bio', 'A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data', 'Nouri, Nima|Kleinstein, Steven H', 'Department of Pathology, Yale School of Medicine, New Haven, CT, USA.|Department of Pathology, Yale School of Medicine, New Haven, CT, USA.', 'United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty235', '29949968', 'B cells derive their antigen-specificity through the expression of Immunoglobulin (Ig) receptors on their surface. These receptors are initially generated stochastically by somatic re-arrangement of the DNA and further diversified following antigen-activation by a process of somatic hypermutation, which introduces mainly point substitutions into the receptor DNA at a high rate. Recent advances in next-generation sequencing have enabled large-scale profiling of the B cell Ig repertoire from blood and tissue samples. A key computational challenge in the analysis of these data is partitioning the sequences to identify descendants of a common B cell (i.e. a clone). Current methods group sequences using a fixed distance threshold, or a likelihood calculation that is computationally-intensive. Here, we propose a new method based on spectral clustering with an adaptive threshold to determine the local sequence neighborhood. Validation using simulated and experimental datasets demonstrates that this method has high sensitivity and specificity compared to a fixed threshold that is optimized for these measures. In addition, this method works on datasets where choosing an optimal fixed threshold is difficult and is more computationally efficient in all cases. The ability to quickly and accurately identify members of a clone from repertoire sequencing data will greatly improve downstream analyses. Clonally-related sequences cannot be treated independently in statistical models, and clonal partitions are used as the basis for the calculation of diversity metrics, lineage reconstruction and selection analysis. Thus, the spectral clustering-based method here represents an important contribution to repertoire analysis.'),
(81880, 2018, 'bio', 'Versatile genome assembly evaluation with QUAST-LG', 'Mikheenko, Alla|Prjibelski, Andrey|Saveliev, Vladislav|Antipov, Dmitry|Gurevich, Alexey', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.', '-|-|-|-|-', 0, 'Animals|Genomics|High-Throughput Nucleotide Sequencing|Humans|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty266', '29949969', 'The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.'),
(81881, 2018, 'bio', 'A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains', 'Her, Hsuan-Lin|Wu, Yu-Wei', 'School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.|Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.', '-|-', 0, 'Anti-Bacterial Agents|Drug Resistance, Bacterial|Escherichia coli|Genome, Bacterial|Machine Learning|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty276', '29949970', 'Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens. Whole-genome sequences have been collected for different microbial strains in order to identify crucial characteristics that allow certain strains to become resistant to antibiotics, however, a global inspection of the gene content responsible for AMR activities remains to be done.'),
(81882, 2018, 'bio', 'Strand-seq enables reliable separation of long reads by chromosome via expectation maximization', 'Ghareghani, Maryam|Porubskxe1xbbxb3, David|Sanders, Ashley D|Meiers, Sascha|Eichler, Evan E|Korbel, Jan O|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrxc3xbccken, 66123, Germany.|Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrxc3xbccken, 66123, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.|Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrxc3xbccken, 66123, Germany.', 'Germany|Germany|Germany|Germany|United States|Germany|Germany', 0, 'Algorithms|Chromosomes, Human|Computer Simulation|Female|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty290', '29949971', 'Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately.'),
(81883, 2018, 'bio', 'Association mapping in biomedical time series via statistically significant shapelet mining', 'Bock, Christian|Gumbsch, Thomas|Moor, Michael|Rieck, Bastian|Roqueiro, Damian|Borgwardt, Karsten', '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.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Biomarkers|Data Mining|Genetic Association Studies|Humans|Software', 'Journal Article', '10.1093/bioinformatics/bty246', '29949972', 'Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component-their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.'),
(81884, 2018, 'bio', 'An optimization framework for network annotation', 'Patkar, Sushant|Sharan, Roded', 'Computer Science, University of Maryland, College Park, MD, USA.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'United States|Israel', 1, 'Models, Biological|Protein Interaction Maps|Saccharomyces cerevisiae|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bty236', '29949973', 'A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well-studied biological pathways.'),
(81885, 2018, 'bio', 'Dissecting newly transcribed and old RNA using GRAND-SLAM', 'Jürges, Christopher|Dölken, Lars|Erhard, Florian', 'Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.|Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.|Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.', 'Germany|Germany|Germany', 0, 'Bayes Theorem|Gene Expression Profiling|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty256', '29949974', 'Global quantification of total RNA is used to investigate steady state levels of gene expression. However, being able to differentiate pre-existing RNA (that has been synthesized prior to a defined point in time) and newly transcribed RNA can provide invaluable information e.g. to estimate RNA half-lives or identify fast and complex regulatory processes. Recently, new techniques based on metabolic labeling and RNA-seq have emerged that allow to quantify new and old RNA: Nucleoside analogs are incorporated into newly transcribed RNA and are made detectable as point mutations in mapped reads. However, relatively infrequent incorporation events and significant sequencing error rates make the differentiation between old and new RNA a highly challenging task. We developed a statistical approach termed GRAND-SLAM that, for the first time, allows to estimate the proportion of old and new RNA in such an experiment. Uncertainty in the estimates is quantified in a Bayesian framework. Simulation experiments show our approach to be unbiased and highly accurate. Furthermore, we analyze how uncertainty in the proportion translates into uncertainty in estimating RNA half-lives and give guidelines for planning experiments. Finally, we demonstrate that our estimates of RNA half-lives compare favorably to other experimental approaches and that biological processes affecting RNA half-lives can be investigated with greater power than offered by any other method. GRAND-SLAM is freely available for non-commercial use at http://software.erhard-lab.de, R scripts to generate all figures are available at zenodo (doi: 10.5281/zenodo.1162340).'),
(81886, 2018, 'bio', 'Learning with multiple pairwise kernels for drug bioactivity prediction', 'Cichonska, Anna|Pahikkala, Tapio|Szedmak, Sandor|Julkunen, Heli|Airola, Antti|Heinonen, Markus|Aittokallio, Tero|Rousu, Juho', 'Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Information Technology, University of Turku, Turku, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Information Technology, University of Turku, Turku, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Antineoplastic Agents|Cell Line, Tumor|Computational Biology|Drug Discovery|Humans|Neoplasms|Protein Kinases|Signal Transduction|Software|Support Vector Machine|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/bty277', '29949975', 'Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.'),
(81887, 2018, 'bio', 'Viral quasispecies reconstruction via tensor factorization with successive read removal', 'Ahn, Soyeon|Ke, Ziqi|Vikalo, Haris', 'Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.|Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.|Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.', 'United States|United States|United States', 3, 'Algorithms|Cluster Analysis|Genome, Viral|HIV-1|High-Throughput Nucleotide Sequencing|Quasispecies|RNA Viruses|Sequence Analysis, RNA|Software|Zika Virus', 'Journal Article', '10.1093/bioinformatics/bty291', '29949976', 'As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains--a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small.'),
(81888, 2018, 'bio', 'An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification', 'Zhao, Yixiu|Zeng, Xiangrui|Guo, Qiang|Xu, Min', 'Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.|Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|Germany|United States', 0, 'Algorithms|Electron Microscope Tomography|Image Interpretation, Computer-Assisted|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Likelihood Functions|Macromolecular Substances|Models, Molecular|Molecular Conformation|Proteasome Endopeptidase Complex|Ribosomes|Signal-To-Noise Ratio|Software', 'Journal Article', '10.1093/bioinformatics/bty267', '29949977', 'Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation.'),
(81889, 2018, 'bio', 'Convolutional neural networks for classification of alignments of non-coding RNA sequences', 'Aoki, Genta|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, Yokohama, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama, Japan.', 'Japan|Japan', 13, 'Adenocarcinoma|Binding Sites|Cluster Analysis|Computational Biology|Humans|Male|MicroRNAs|Neural Networks (Computer)|Nucleic Acid Conformation|Prostatic Neoplasms|Protein Binding|RNA, Small Nucleolar|RNA, Transfer|RNA, Untranslated|Software', 'Journal Article', '10.1093/bioinformatics/bty228', '29949978', 'The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites.'),
(81890, 2018, 'bio', 'Classifying tumors by supervised network propagation', 'Zhang, Wei|Ma, Jianzhu|Ideker, Trey', 'Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|Department of Medicine, University of California, San Diego, La Jolla, CA, USA.', 'United States|United States|United States', 12, 'Breast Neoplasms|Computational Biology|Female|Glioblastoma|Humans|Mutation|Neoplasms|Protein Interaction Maps|Signal Transduction|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/bty247', '29949979', 'Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.'),
(81891, 2018, 'bio', 'Protein threading using residue co-variation and deep learning', 'Zhu, Jianwei|Wang, Sheng|Bu, Dongbo|Xu, Jinbo', 'Toyota Technological Institute, Chicago, IL, USA.|Toyota Technological Institute, Chicago, IL, USA.|Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|Toyota Technological Institute, Chicago, IL, USA.', 'United States|United States|China|United States', 0, 'Deep Learning|Models, Molecular|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty278', '29949980', 'Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging.'),
(81892, 2018, 'bio', 'Covariate-dependent negative binomial factor analysis of RNA sequencing data', 'Zamani Dadaneh, Siamak|Zhou, Mingyuan|Qian, Xiaoning', 'Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX, USA.|Department of Information, Risk, and Operations Management, The University of Texas at Austin, Austin, TX, USA.|Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States', 1, 'Autistic Disorder|Bayes Theorem|Factor Analysis, Statistical|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty237', '29949981', 'High-throughput sequencing technologies, in particular RNA sequencing (RNA-seq), have become the basic practice for genomic studies in biomedical research. In addition to studying genes individually, for example, through differential expression analysis, investigating co-ordinated expression variations of genes may help reveal the underlying cellular mechanisms to derive better understanding and more effective prognosis and intervention strategies. Although there exists a variety of co-expression network based methods to analyze microarray data for this purpose, instead of blindly extending these methods for microarray data that may introduce unnecessary bias, it is crucial to develop methods well adapted to RNA-seq data to identify the functional modules of genes with similar expression patterns.'),
(81893, 2018, 'bio', 'A space and time-efficient index for the compacted colored de Bruijn graph', 'Almodaresi, Fatemeh|Sarkar, Hirak|Srivastava, Avi|Patro, Rob', 'Department of Computer Science, Stony Brook University, Stonybrook, NY, USA.|Department of Computer Science, Stony Brook University, Stonybrook, NY, USA.|Department of Computer Science, Stony Brook University, Stonybrook, NY, USA.|Department of Computer Science, Stony Brook University, Stonybrook, NY, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Data Visualization|Gene Expression Profiling|Genome, Bacterial|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty292', '29949982', 'Indexing reference sequences for search-both individual genomes and collections of genomes-is an important building block for many sequence analysis tasks. Much work has been dedicated to developing full-text indices for genomic sequences, based on data structures such as the suffix array, the BWT and the FM-index. However, the de Bruijn graph, commonly used for sequence assembly, has recently been gaining attention as an indexing data structure, due to its natural ability to represent multiple references using a graphical structure, and to collapse highly-repetitive sequence regions. Yet, much less attention has been given as to how to best index such a structure, such that queries can be performed efficiently and memory usage remains practical as the size and number of reference sequences being indexed grows large.'),
(81894, 2018, 'bio', 'Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering', 'Ballnus, Benjamin|Schaper, Steffen|Theis, Fabian J|Hasenauer, Jan', 'Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.|Bayer AG, Engineering and Technologies, Applied Mathematics, Leverkusen, Germany.|Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Bayes Theorem|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/bty229', '29949983', 'Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.'),
(81895, 2018, 'bio', 'Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge', 'Sundin, Iiris|Peltola, Tomi|Micallef, Luana|Afrabandpey, Homayun|Soare, Marta|Mamun Majumder, Muntasir|Daee, Pedram|He, Chen|Serim, Baris|Havulinna, Aki|Heckman, Caroline|Jacucci, Giulio|Marttinen, Pekka|Kaski, Samuel', 'Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finl', 4, 'Bayes Theorem|Genomics|Humans|Precision Medicine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty257', '29949984', 'Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large.'),
(81896, 2018, 'bio', 'Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies', 'Peng, Yisu|Jiang, Yuxiang|Radivojac, Predrag', 'Department of Computer Science, Indiana University, Bloomington, USA.|Department of Computer Science, Indiana University, Bloomington, USA.|Department of Computer Science, Indiana University, Bloomington, USA.', 'India|India|India', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty268', '29949985', 'Modern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontology can be seen as directed acyclic graphs (DAGs), where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent sub-graph, that is, a sub-graph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent sub-graphs, however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology.'),
(81897, 2018, 'bio', 'Predicting CTCF-mediated chromatin loops using CTCF-MP', 'Zhang, Ruochi|Wang, Yuchuan|Yang, Yang|Zhang, Yang|Ma, Jian', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology 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|United States|United States|United States|United States', 11, 'CCCTC-Binding Factor|Chromatin|Chromatin Immunoprecipitation|Chromosomes, Human|Genomics|HeLa Cells|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty248', '29949986', 'The three dimensional organization of chromosomes within the cell nucleus is highly regulated. It is known that CCCTC-binding factor (CTCF) is an important architectural protein to mediate long-range chromatin loops. Recent studies have shown that the majority of CTCF binding motif pairs at chromatin loop anchor regions are in convergent orientation. However, it remains unknown whether the genomic context at the sequence level can determine if a convergent CTCF motif pair is able to form a chromatin loop.'),
(81898, 2018, 'bio', 'mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion', 'Jokinen, Emmi|Heinonen, Markus|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty238', '29949987', 'Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.'),
(81899, 2018, 'bio', 'Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge', 'Mukherjee, Sumit|Zhang, Yue|Fan, Joshua|Seelig, Georg|Kannan, Sreeram', 'Department of Electrical Engineering, University of Washington, Seattle, WA, USA.|Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.|Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.|Department of Electrical Engineering, University of Washington, Seattle, WA, USA.|Department of Electrical Engineering, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty293', '29949988', 'Single cell RNA-seq (scRNA-seq) data contains a wealth of information which has to be inferred computationally from the observed sequencing reads. As the ability to sequence more cells improves rapidly, existing computational tools suffer from three problems. (i) The decreased reads-per-cell implies a highly sparse sample of the true cellular transcriptome. (ii) Many tools simply cannot handle the size of the resulting datasets. (iii) Prior biological knowledge such as bulk RNA-seq information of certain cell types or qualitative marker information is not taken into account. Here we present UNCURL, a preprocessing framework based on non-negative matrix factorization for scRNA-seq data, that is able to handle varying sampling distributions, scales to very large cell numbers and can incorporate prior knowledge.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81900, 2018, 'bio', 'A graph-based approach to diploid genome assembly', 'Garg, Shilpa|Rautiainen, Mikko|Novak, Adam M|Garrison, Erik|Durbin, Richard|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, Germany.|UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.|Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, Germany.', 'Germany|Germany|United Kingdom|United Kingdom|United Kingdom|Germany', 0, 'Data Visualization|Diploidy|Genome, Fungal|Haplotypes|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Yeasts', 'Journal Article', '10.1093/bioinformatics/bty279', '29949989', 'Constructing high-quality haplotype-resolved de novo assemblies of diploid genomes is important for revealing the full extent of structural variation and its role in health and disease. Current assembly approaches often collapse the two sequences into one haploid consensus sequence and, therefore, fail to capture the diploid nature of the organism under study. Thus, building an assembler capable of producing accurate and complete diploid assemblies, while being resource-efficient with respect to sequencing costs, is a key challenge to be addressed by the bioinformatics community.'),
(81901, 2018, 'bio', 'Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis', 'Stapor, Paul|Fröhlich, Fabian|Hasenauer, Jan', 'Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.|Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.|Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.', 'Germany|Germany|Germany', 4, 'Algorithms|Computational Biology|Models, Biological|Probability|Software', 'Journal Article', '10.1093/bioinformatics/bty230', '29949990', 'Parameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems.'),
(81902, 2018, 'bio', 'Finding associated variants in genome-wide association studies on multiple traits', 'Gai, Lisa|Eskin, Eleazar', 'Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, CA, USA.', 'United States|United States', 0, 'Genome-Wide Association Study|Meta-Analysis as Topic|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/bty249', '29949991', 'Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time.'),
(81903, 2018, 'bio', 'AnoniMME: bringing anonymity to the Matchmaker Exchange platform for rare disease gene discovery', 'Oprisanu, Bristena|De Cristofaro, Emiliano', 'Department of Computer Science, University College London, London, UK.|Department of Computer Science, University College London, London, UK.', 'United Kingdom|United Kingdom', 2, 'Competitive Behavior|Databases, Factual|Genetic Association Studies|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genomics|Humans|Information Dissemination|Information Storage and Retrieval|Privacy|Rare Diseases|Research Personnel|Software', 'Journal Article', '10.1093/bioinformatics/bty269', '29949992', 'Advances in genome sequencing and genomics research are bringing us closer to a new era of personalized medicine, where healthcare can be tailored to the individuals genetic makeup and to more effective diagnosis and treatment of rare genetic diseases. Much of this progress depends on collaborations and access to data, thus, a number of initiatives have been introduced to support seamless data sharing. Among these, the Global Alliance for Genomics and Health has developed and operates a platform, called Matchmaker Exchange (MME), which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases. Queries include gene variations which are linked to rare diseases, and the ability to find other researchers that have seen or have interest in those variations is extremely valuable. Nonetheless, in some cases, researchers may be reluctant to use the platform since the queries they make (thus, what they are working on) are revealed to other researchers, and this creates concerns with respect to privacy and competitive advantage. In this paper, we present AnoniMME, a framework geared to enable anonymous queries within the MME platform. The framework, building on a cryptographic primitive called Reverse Private Information Retrieval, let researchers anonymously query the federated platform, in a multi-server setting-specifically, they write their query, along with a public encryption key, anonymously in a public database. Responses are also supported, so that other researchers can respond to queries by providing their encrypted contact details.'),
(81904, 2018, 'bio', 'Discriminating early- and late-stage cancers using multiple kernel learning on gene sets', 'Rahimi, Arezou|Gxc3xb6nen, Mehmet', 'Graduate School of Sciences and Engineering, Koxc3xa7 University, Istanbul, Turkey.|Department of Industrial Engineering, College of Engineering, Koxc3xa7 University, xc4xb0stanbul, Turkey.', 'Turkey|Turkey', 12, 'Female|Gene Expression Profiling|Genome, Human|Humans|Machine Learning|Male|Metabolic Networks and Pathways|Neoplasm Staging|Neoplasms|Sequence Analysis, RNA|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bty239', '29949993', 'Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets.'),
(81905, 2018, 'bio', 'A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity', 'Petralia, Francesca|Wang, Li|Peng, Jie|Yan, Arthur|Zhu, Jun|Wang, Pei', 'Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Statistics, University of California, Davis, Davis, CA, USA.|Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Humans|Neoplasms|Ovarian Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty280', '29949994', 'Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.'),
(81906, 2018, 'bio', 'Asymptotically optimal minimizers schemes', 'Marçais, Guillaume|DeBlasio, Dan|Kingsford, Carl', 'Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 4, 'Algorithms|Computational Biology', 'Journal Article', '10.1093/bioinformatics/bty258', '29949995', 'The minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density and thereby making existing and future bioinformatics tools even more efficient.'),
(81907, 2018, 'bio', 'Modeling polypharmacy side effects with graph convolutional networks', 'Zitnik, Marinka|Agrawal, Monica|Leskovec, Jure', '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', 99, 'Computational Biology|Data Visualization|Drug Interactions|Drug-Related Side Effects and Adverse Reactions|Female|Humans|Models, Biological|Neural Networks (Computer)|Polypharmacy|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/bty294', '29949996', 'The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.'),
(81908, 2018, 'bio', 'Personalized regression enables sample-specific pan-cancer analysis', 'Lengerich, Benjamin J|Aragam, Bryon|Xing, Eric P', 'Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 4, 'Female|Genetic Predisposition to Disease|Genomics|Humans|Male|Models, Genetic|Models, Statistical|Mutation|Neoplasms|Polymorphism, Single Nucleotide|Precision Medicine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty250', '29949997', 'In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of the entire cohort. Unfortunately, many traditional methods for genomic analysis seek to estimate a single model which is shared by all samples in a population, ignoring this inter-sample heterogeneity entirely. In order to better understand patient heterogeneity, it is necessary to develop practical, personalized statistical models.'),
(81909, 2018, 'bio', 'NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks', 'Abdellah, Marwan|Hernando, Juan|Eilemann, Stefan|Lapere, Samuel|Antille, Nicolas|Markram, Henry|Schxc3xbcrmann, Felix', 'Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 9, 'Animals|Computer Simulation|Humans|Image Interpretation, Computer-Assisted|Microscopy|Neurons|Software', 'Journal Article', '10.1093/bioinformatics/bty231', '29949998', 'From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled.'),
(81910, 2018, 'bio', 'Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations', 'Smaili, Fatima Zohra|Gao, Xin|Hoehndorf, Robert', 'Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 16, 'Computational Biology|Gene Ontology|Humans|Machine Learning|Protein Interaction Maps|Saccharomyces cerevisiae|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/bty259', '29949999', 'Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms, notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.'),
(81911, 2018, 'bio', 'Improved pathway reconstruction from RNA interference screens by exploiting off-target effects', 'Srivatsa, Sumana|Kuipers, Jack|Schmich, Fabian|Eicher, Simone|Emmenlauer, Mario|Dehio, Christoph|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.|Biozentrum, University of Basel, Basel, Switzerland.|Biozentrum, University of Basel, Basel, Switzerland.|Biozentrum, University of Basel, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 2, 'Algorithms|Computational Biology|Gene Knockdown Techniques|Humans|Models, Statistical|RNA Interference|RNA, Small Interfering|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bty240', '29950000', 'Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs.'),
(81912, 2018, 'bio', 'Deconvolution and phylogeny inference of structural variations in tumor genomic samples', 'Eaton, Jesse|Wang, Jingyi|Schwartz, Russell', 'Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty270', '29950001', 'Phylogenetic reconstruction of tumor evolution has emerged as a crucial tool for making sense of the complexity of emerging cancer genomic datasets. Despite the growing use of phylogenetics in cancer studies, though, the field has only slowly adapted to many ways that tumor evolution differs from classic species evolution. One crucial question in that regard is how to handle inference of structural variations (SVs), which are a major mechanism of evolution in cancers but have been largely neglected in tumor phylogenetics to date, in part due to the challenges of reliably detecting and typing SVs and interpreting them phylogenetically.'),
(81913, 2018, 'bio', 'SigMat: a classification scheme for gene signature matching', 'Xiao, Jinfeng|Blatti, Charles|Sinha, Saurabh', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.', 'United States|United States|United States', 3, 'Algorithms|Cell Line|Computational Biology|Gene Expression Profiling|Humans|Pharmacogenetics|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty251', '29950002', 'Several large-scale efforts have been made to collect gene expression signatures from a variety of biological conditions, such as response of cell lines to treatment with drugs, or tumor samples with different characteristics. These gene signature collections are utilized through bioinformatics tools for signature matching, whereby a researcher studying an expression profile can identify previously cataloged biological conditions most related to their profile. Signature matching tools typically retrieve from the collection the signature that has highest similarity to the user-provided profile. Alternatively, classification models may be applied where each biological condition in the signature collection is a class label, however, such models are trained on the collection of available signatures and may not generalize to the novel cellular context or cell line of the researchers expression profile.'),
(81914, 2018, 'bio', 'Driver gene mutations based clustering of tumors: methods and applications', 'Zhang, Wensheng|Flemington, Erik K|Zhang, Kun', 'Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, USA.|Department of Pathology, Tulane School of Medicine, Tulane Cancer Center, Tulane University, New Orleans, LA, USA.|Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, USA.', 'United States|United States|United States', 0, 'Cluster Analysis|DNA Mutational Analysis|Genes, Neoplasm|Genomics|Humans|Models, Genetic|Mutation|Neoplasms|Prognosis|Software', 'Journal Article', '10.1093/bioinformatics/bty232', '29950003', 'Somatic mutations in proto-oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity-based sub-types within a cancer type and relate the pinpointed sub-types to the clinical outcomes and pathologic characteristics of patients.'),
(81915, 2018, 'bio', 'Inference of species phylogenies from bi-allelic markers using pseudo-likelihood', 'Zhu, Jiafan|Nakhleh, Luay', 'Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.', 'United States|United States', 6, 'Alleles|Computational Biology|Evolution, Molecular|Models, Genetic|Phylogeny|Probability|Software', 'Journal Article', '10.1093/bioinformatics/bty295', '29950004', 'Phylogenetic networks represent reticulate evolutionary histories. Statistical methods for their inference under the multispecies coalescent have recently been developed. A particularly powerful approach uses data that consist of bi-allelic markers (e.g. single nucleotide polymorphism data) and allows for exact likelihood computations of phylogenetic networks while numerically integrating over all possible gene trees per marker. While the approach has good accuracy in terms of estimating the network and its parameters, likelihood computations remain a major computational bottleneck and limit the methods applicability.'),
(81916, 2018, 'bio', 'Unsupervised embedding of single-cell Hi-C data', 'Liu, Jie|Lin, Dejun|Yardimci, Galip Gürkan|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States', 9, 'Cell Nucleus|Chromatin|DNA|Eukaryota|High-Throughput Nucleotide Sequencing|Imaging, Three-Dimensional|Nucleic Acid Conformation|Sequence Analysis, DNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/bty285', '29950005', 'Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. We apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding.'),
(81917, 2018, 'bio', 'Random forest based similarity learning for single cell RNA sequencing data', 'Pouyan, Maziyar Baran|Kostka, Dennis', 'Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States', 10, 'Cluster Analysis|Gene Expression Profiling|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty260', '29950006', 'Genome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore, obtaining accurate cell-cell similarities from scRNA-seq data is a critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal.'),
(81918, 2018, 'bio', 'An evolutionary model motivated by physicochemical properties of amino acids reveals variation among proteins', 'Braun, Edward L', 'Department of Biology and Genetics Institute, University of Florida, Gainesville, FL, USA.', 'United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty261', '29950007', 'The relative rates of amino acid interchanges over evolutionary time are likely to vary among proteins. Variation in those rates has the potential to reveal information about constraints on proteins. However, the most straightforward model that could be used to estimate relative rates of amino acid substitution is parameter-rich and it is therefore impractical to use for this purpose.'),
(81919, 2018, 'bio', 'MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples', 'Asgari, Ehsaneddin|Garakani, Kiavash|McHardy, Alice C|Mofrad, Mohammad R K', 'Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.|Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.|Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany.|Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.', 'United States|United States|Germany|United States', 13, 'Databases, Nucleic Acid|Genes, rRNA|Humans|Microbiota|Phenotype|RNA, Ribosomal, 16S|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty296', '29950008', 'Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes.'),
(81920, 2018, 'bio', 'SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra', 'Nguyen, Dai Hai|Nguyen, Canh Hao|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.', 'Japan|Japan|Japan', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty252', '29950009', 'Recent success in metabolite identification from tandem mass spectra has been led by machine learning, which has two stages: mapping mass spectra to molecular fingerprint vectors and then retrieving candidate molecules from the database. In the first stage, i.e. fingerprint prediction, spectrum peaks are features and considering their interactions would be reasonable for more accurate identification of unknown metabolites. Existing approaches of fingerprint prediction are based on only individual peaks in the spectra, without explicitly considering the peak interactions. Also the current cutting-edge method is based on kernels, which are computationally heavy and difficult to interpret.'),
(81921, 2018, 'bio', 'GSEA-InContext: identifying novel and common patterns in expression experiments', 'Powers, Rani K|Goodspeed, Andrew|Pielke-Lombardo, Harrison|Tan, Aik-Choon|Costello, James C', 'Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Department of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.', 'United States|United States|United States|United States|United States', 9, 'Algorithms|Gene Expression Profiling|Humans|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/bty271', '29950010', 'Gene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate pathway-level changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.'),
(81922, 2018, 'bio', 'Training for translation between disciplines: a philosophy for life and data sciences curricula', 'Anton Feenstra, K|Abeln, Sanne|Westerhuis, Johan A|Brancos Dos Santos, Filipe|Molenaar, Douwe|Teusink, Bas|Hoefsloot, Huub C J|Heringa, Jaap', 'Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands.|Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands.|Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, GE Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, GE Amsterdam, The Netherlands.|AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands.|AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, GE Amsterdam, The Netherlands.|Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, 'Computational Biology|Curriculum|Data Science|Humans', 'Journal Article', '10.1093/bioinformatics/bty233', '29950011', 'Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other.'),
(81923, 2018, 'bio', 'DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy', 'Li, Yu|Xu, Fan|Zhang, Fa|Xu, Pingyong|Zhang, Mingshu|Fan, Ming|Li, Lihua|Gao, Xin|Han, Renmin', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.|Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.', 'Saudi Arabia|China|China|China|China|China|China|Saudi Arabia|Saudi Arabia', 17, '-', 'Journal Article', '10.1093/bioinformatics/bty241', '29950012', 'Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.'),
(81924, 2018, 'bio', 'HFSP: high speed homology-driven function annotation of proteins', 'Mahlich, Yannick|Steinegger, Martin|Rost, Burkhard|Bromberg, Yana', 'Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.|Computational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.|Computational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.|Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.', 'United States|Germany|Germany|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty262', '29950013', 'The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.'),
(81925, 2018, 'bio', 'Haplotype phasing in single-cell DNA-sequencing data', 'Satas, Gryte|Raphael, Benjamin J', 'Department of Computer Science, Princeton University, Princeton, NJ, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.', 'United States|United States', 0, 'Algorithms|Breast Neoplasms|Diploidy|Female|Gene Frequency|Genome, Human|Genomics|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Male|Neurons|Polymorphism, Single Nucleotide|Single-Cell Analysis|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty286', '29950014', 'Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.'),
(81926, 2018, 'bio', 'ISMB 2018 PROCEEDINGS PAPERS COMMITTEE', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty359', '29950015', '-'),
(81927, 2018, 'bio', 'The Kappa platform for rule-based modeling', 'Boutillier, Pierre|Maasha, Mutaamba|Li, Xing|Medina-Abarca, Héctor F|Krivine, Jean|Feret, Jérôme|Cristescu, Ioana|Forbes, Angus G|Fontana, Walter', 'Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|IRIF, Université Paris-Diderot - Paris Paris, France.|Département dinformatique de lENS (INRIA/ENS/CNRS), PSL Research University, Paris, France.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Computational Media, UC Santa Cruz, Santa Cruz, CA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.', 'United States|United States|United States|United States|France|France|United States|United States|United States', 20, 'Computational Biology|Data Visualization|Epidermal Growth Factor|Models, Biological|Signal Transduction|Software|Wnt Signaling Pathway', 'Journal Article', '10.1093/bioinformatics/bty272', '29950016', 'We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction.'),
(81928, 2018, 'bio', 'A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach', 'Xing, Wenhui|Qi, Junsheng|Yuan, Xiaohui|Li, Lin|Zhang, Xiaoyu|Fu, Yuhua|Xiong, Shengwu|Hu, Lun|Peng, Jing', 'School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|Department of Plant Science, College of Biological Science, China Agricultural University, Beijing, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Data Mining|Databases, Bibliographic|Genetic Association Studies|Genotype|Machine Learning|Phenotype|Plants|Software', 'Journal Article', '10.1093/bioinformatics/bty263', '29950017', 'The fundamental challenge of modern genetic analysis is to establish gene-phenotype correlations that are often found in the large-scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study- or author-specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants.'),
(81929, 2018, 'bio', 'Accurate prediction of orthologs in the presence of divergence after duplication', 'Lafond, Manuel|Meghdari Miardan, Mona|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.', 'Canada|Canada|Canada', 8, 'Computational Biology|Eukaryota|Evolution, Molecular|Gene Duplication|Mutation Rate|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty242', '29950018', 'When gene duplication occurs, one of the copies may become free of selective pressure and evolve at an accelerated pace. This has important consequences on the prediction of orthology relationships, since two orthologous genes separated by divergence after duplication may differ in both sequence and function. In this work, we make the distinction between the primary orthologs, which have not been affected by accelerated mutation rates on their evolutionary path, and the secondary orthologs, which have. Similarity-based prediction methods will tend to miss secondary orthologs, whereas phylogeny-based methods cannot separate primary and secondary orthologs. However, both types of orthology have applications in important areas such as gene function prediction and phylogenetic reconstruction, motivating the need for methods that can distinguish the two types.'),
(81930, 2018, 'bio', 'A scalable estimator of SNP heritability for biobank-scale data', 'Wu, Yue|Sankararaman, Sriram', 'Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, CA, USA.', 'United States|United States', 0, 'Female|Genome-Wide Association Study|Humans|Likelihood Functions|Linear Models|Male|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty253', '29950019', 'Heritability, the proportion of variation in a trait that can be explained by genetic variation, is an important parameter in efforts to understand the genetic architecture of complex phenotypes as well as in the design and interpretation of genome-wide association studies. Attempts to understand the heritability of complex phenotypes attributable to genome-wide single nucleotide polymorphism (SNP) variation data has motivated the analysis of large datasets as well as the development of sophisticated tools to estimate heritability in these datasets. Linear mixed models (LMMs) have emerged as a key tool for heritability estimation where the parameters of the LMMs, i.e. the variance components, are related to the heritability attributable to the SNPs analyzed. Likelihood-based inference in LMMs, however, poses serious computational burdens.'),
(81931, 2018, 'bio', 'Deep neural networks and distant supervision for geographic location mention extraction', 'Magge, Arjun|Weissenbacher, Davy|Sarker, Abeed|Scotch, Matthew|Gonzalez-Hernandez, Graciela', 'Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.|Department of Biostatistics, Epidemiology, and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, Epidemiology, and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.|Department of Biostatistics, Epidemiology, and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States|United States|United States|United States', 4, 'Databases, Nucleic Acid|Deep Learning|Humans|Information Storage and Retrieval|Phylogeography|Viruses', 'Journal Article', '10.1093/bioinformatics/bty273', '29950020', 'Virus phylogeographers rely on DNA sequences of viruses and the locations of the infected hosts found in public sequence databases like GenBank for modeling virus spread. However, the locations in GenBank records are often only at the country or state level, and may require phylogeographers to scan the journal articles associated with the records to identify more localized geographic areas. To automate this process, we present a named entity recognizer (NER) for detecting locations in biomedical literature. We built the NER using a deep feedforward neural network to determine whether a given token is a toponym or not. To overcome the limited human annotated data available for training, we use distant supervision techniques to generate additional samples to train our NER.'),
(81932, 2018, 'bio', 'LONGO: an R package for interactive gene length dependent analysis for neuronal identity', 'McCoy, Matthew J|Paul, Alexander J|Victor, Matheus B|Richner, Michelle|Gabel, Harrison W|Gong, Haijun|Yoo, Andrew S|Ahn, Tae-Hyuk', 'Department of Developmental Biology, Washington University School of Medicine, Saint Louis, MO, USA.|Program in Bioinformatics & Computational Biology, Saint Louis University, Saint Louis, MO, USA.|Department of Developmental Biology, Washington University School of Medicine, Saint Louis, MO, USA.|Department of Developmental Biology, Washington University School of Medicine, Saint Louis, MO, USA.|Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA.|Department of Mathematics & Statistics, Saint Louis University, Saint Louis, MO, USA.|Department of Developmental Biology, Washington University School of Medicine, Saint Louis, MO, USA.|Program in Bioinformatics & Computational Biology, Saint Louis University, Saint Louis, MO, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Aged|Cellular Reprogramming|Female|Gene Expression Profiling|Humans|Male|Middle Aged|Neurons|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty243', '29950021', 'Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (>100xe2x80x89kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes.'),
(81933, 2018, 'bio', 'ISMB 2018 proceedings', 'Bromberg, Yana|Radivojac, Predrag', 'Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.|Department of Computer Science, Indiana University, Bloomington, IN, USA.', 'United States|India', 0, '-', 'Introductory Journal Article', '10.1093/bioinformatics/bty413', '29950022', '-');
