INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79742, 2016, 'bio', 'MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information', 'Lee, S H|van der Werf, J H J', 'School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia and Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.|School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia and.', 'Australia|Australia', 0, 'Algorithms|Animals|Genome|Genome-Wide Association Study|Genomics|Humans|Linear Models|Mice', 'Journal Article', '10.1093/bioinformatics/btw012', '26755623', 'We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.'),
(79743, 2016, 'bio', 'Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer', 'Young, Jonathan H|Peyton, Michael|Seok Kim, Hyun|McMillan, Elizabeth|Minna, John D|White, Michael A|Marcotte, Edward M', 'Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA, Center for Systems and Synthetic Biology and Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea, and.|Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA, Center for Systems and Synthetic Biology and Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Carcinoma, Non-Small-Cell Lung|Cluster Analysis|DNA, Neoplasm|Gene Expression Regulation, Neoplastic|Humans|Lung Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw010', '26755624', 'Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets.'),
(79744, 2016, 'bio', 'Collaborative analysis of multi-gigapixel imaging data using Cytomine', 'Marée, Raphaël|Rollus, Loïc|Stévens, Benjamin|Hoyoux, Renaud|Louppe, Gilles|Vandaele, Rémy|Begon, Jean-Michel|Kainz, Philipp|Geurts, Pierre|Wehenkel, Louis', 'Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium Bioimage Analysis Unit, Institut Pasteur, Paris, France.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Institute of Biophysics, Medical University of Graz, Graz, Austria.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.|Systems and Modeling, Department of Electrical Engineering and Computer Science and GIGA-Research, University of Liège, Liège, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Austria|Belgium|Belgium', 47, 'Image Interpretation, Computer-Assisted|Internet|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw013', '26755625', 'Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries.'),
(79745, 2016, 'bio', 'Integration of string and de Bruijn graphs for genome assembly', 'Huang, Yao-Ting|Liao, Chen-Fu', 'Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan.|Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan.', '-|-', 0, 'Algorithms|Animals|Genome|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw011', '26755626', 'String and de Bruijn graphs are two graph models used by most genome assemblers. At present, none of the existing assemblers clearly outperforms the others across all datasets. We found that although a string graph can make use of entire reads for resolving repeats, de Bruijn graphs can naturally assemble through regions that are error-prone due to sequencing bias.'),
(79746, 2016, 'bio', 'Realizing privacy preserving genome-wide association studies', 'Simmons, Sean|Berger, Bonnie', 'Department of Mathematics and CSAIL, MIT, Cambridge, MA, USA.|Department of Mathematics and CSAIL, MIT, Cambridge, MA, USA.', 'United States|United States', 30, 'Algorithms|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Privacy', 'Journal Article', '10.1093/bioinformatics/btw009', '26769317', 'As genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency.'),
(79747, 2016, 'bio', 'FINEMAP: efficient variable selection using summary data from genome-wide association studies', 'Benner, Christian|Spencer, Chris C A|Havulinna, Aki S|Salomaa, Veikko|Ripatti, Samuli|Pirinen, Matti', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Department of Public Health, University of Helsinki, Helsinki, Finland.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|National Institute for Health and Welfare (THL), Helsinki, Finland and.|National Institute for Health and Welfare (THL), Helsinki, Finland and.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Department of Public Health, University of Helsinki, Helsinki, Finland, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.', 'Finland|United Kingdom|Finland|Finland|Finland|Finland', 113, 'Algorithms|Genome|Genome-Wide Association Study|Genomics|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw018', '26773131', 'The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.'),
(79748, 2016, 'bio', 'SIRAH tools: mapping, backmapping and visualization of coarse-grained models', 'Machado, Matías R|Pantano, Sergio', 'Biomolecular Simulations, Institut Pasteur De Montevideo, Montevideo 11400, Uruguay.|Biomolecular Simulations, Institut Pasteur De Montevideo, Montevideo 11400, Uruguay.', 'Uruguay|Uruguay', 38, 'Molecular Dynamics Simulation', 'Journal Article', '10.1093/bioinformatics/btw020', '26773132', 'Coarse-grained (CG) models reduce the cost of molecular dynamics simulations keeping the essence of molecular interactions. Still, the diversity of CG representations (sizes, connectivity, naming, etc.) hampers the handling and visualization of such models. SIRAH Tools comprises a set of utilities to convert all-atoms coordinates to arbitrary residue-based CG schemes, write GROMACS topological information at any resolution into PSF format and a VMD plugin to visualize, analyze and retrieve pseudo-atomistic information from CG trajectories performed with the SIRAH force field. These tools facilitate the use of intricate CG force fields outside the small developers community.'),
(79749, 2016, 'bio', 'Multi-omics enrichment analysis using the GeneTrail2 web service', 'Stöckel, Daniel|Kehl, Tim|Trampert, Patrick|Schneider, Lara|Backes, Christina|Ludwig, Nicole|Gerasch, Andreas|Kaufmann, Michael|Gessler, Manfred|Graf, Norbert|Meese, Eckart|Keller, Andreas|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Department of Human Genetics, Medical School, Saarland University, Homburg D-66421.|Center for Bioinformatics, Eberhard-Karls-University, Tübingen, D-72076.|Center for Bioinformatics, Eberhard-Karls-University, Tübingen, D-72076.|Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Würzburg University, Würzburg D-97074 and.|Department of Pediatric Oncology and Hematology, Medical School, Saarland University, Homburg, D-66421, Germany.|Department of Human Genetics, Medical School, Saarland University, Homburg D-66421.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.|Center for Bioinformatics, Saarland University, Saarbrücken D-66041.', '-|-|-|-|-|-|-|-|-|Germany|-|-|-', 64, 'Genome|Genomics|Humans|Neoplasms|Proteomics|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv770', '26787660', 'Gene set analysis has revolutionized the interpretation of high-throughput transcriptomic data. Nowadays, with comprehensive studies that measure multiple -omics from the same sample, powerful tools for the integrative analysis of multi-omics datasets are required.'),
(79750, 2016, 'bio', 'MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data', 'Murillo, Gabriel H|You, Na|Su, Xiaoquan|Cui, Wei|Reilly, Muredach P|Li, Mingyao|Ning, Kang|Cui, Xinping', 'Department of Statistics, University of California, Riverside, CA 92521, USA.|Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China.|Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China.|Department of Statistics, University of California, Riverside, CA 92521, USA.|Cardiovascular Institute.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China and.|Department of Statistics, University of California, Riverside, CA 92521, USA, Center for Plant Cell Biology, Institute for Integrative Genome Biology, University of California, Riverside, CA 92521, USA.', 'United States|China|China|United States|United States|United States|China|United States', 2, 'Genotype|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv753', '26787661', 'Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors. It also addresses the multiple testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC) techniques.'),
(79751, 2016, 'bio', 'EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope-paratope interactions', 'Viart, B|Dias-Lopes, C|Kozlova, E|Oliveira, C F B|Nguyen, C|Neshich, G|Chávez-Olórtegui, C|Molina, F|Felicori, L F', 'Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Sys2Diag, FRE3690-CNRS/ALCEDIAG, Montpellier, France and.|Computational Biology Research Group, Embrapa Informática Agropecuária, Campinas, SP, Brazil.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Sys2Diag, FRE3690-CNRS/ALCEDIAG, Montpellier, France and.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.', 'Brazil|Brazil|Brazil|Brazil|France|Brazil|Brazil|France|Brazil', 5, 'Binding Sites, Antibody|Epitopes|Ligands|Peptide Library|Peptides', 'Journal Article', '10.1093/bioinformatics/btw014', '26787662', 'Antibodies are an important class of biological drugs, but with limitations, such as inadequate pharmacokinetics, adverse immunogenicity and high production costs. Synthetic peptides for the desired target represent an important alternative to antibodies. However, no computational tool exists to guide the design of these peptides.'),
(79752, 2016, 'bio', 'lnCaNet: pan-cancer co-expression network for human lncRNA and cancer genes', 'Liu, Yining|Zhao, Min', 'Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia.|Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia.', 'Australia|Australia', 33, 'Databases, Nucleic Acid|Genes, Neoplasm|Humans|Neoplasms|RNA, Long Noncoding', 'Journal Article', '10.1093/bioinformatics/btw017', '26787663', 'Thousands of human long non-coding RNAs (lncRNAs) have been identified in cancers and played important roles in a wide range of tumorigenesis. However, the functions of vast majority of human lncRNAs are still elusive. Emerging studies revealed that the expression level of majority lncRNAs shows discordant expression pattern with their protein-coding gene neighbors in various model organisms. Therefore, it may be useful to infer lncRNAs potential biological function in cancer development by more comprehensive functional views of co-expressed cancer genes beyond mere physical proximity of genes. To this aim, we performed thorough searches and analyses of the interactions between lncRNA and non-neighboring cancer genes and provide a comprehensive co-expression data resource, LnCaNet. In current version, LnCaNet contains the pre-computed 8 494 907 significant co-expression pairs of 9641 lncRNAs and 2544 well-classified cancer genes in 2922 matched TCGA samples. In detail, we integrated 10 cancer gene lists from public database and calculate the co-expression with all the lncRNAs in 11 TCGA cancer types separately. Based on the resulted 110 co-expression networks, we identified 17 common regulatory pairs related to extracellular space shared in 11 cancers. We expect LnCaNet will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction.'),
(79753, 2016, 'bio', 'Piecewise parameter estimation for stochastic models in COPASI', 'Bergmann, Frank T|Sahle, Sven|Zimmer, Christoph', 'COS Heidelberg/BioQuant, Heidelberg 69120, Germany.|COS Heidelberg/BioQuant, Heidelberg 69120, Germany.|BIOMS, University of Heidelberg, Heidelberg, Germany.', 'Germany|Germany|Germany', 2, 'Algorithms|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv759', '26787664', 'Computational modeling is widely used for deepening the understanding of biological processes. Parameterizing models to experimental data needs computationally efficient techniques for parameter estimation. Challenges for parameter estimation include in general the high dimensionality of the parameter space with local minima and in specific for stochastic modeling the intrinsic stochasticity.'),
(79754, 2016, 'bio', 'Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data', 'Burkett, Kelly M|McNeney, Brad|Graham, Jinko', 'Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and Department of Mathematics and Statistics, University of Ottawa, Ottawa K1N 6N5, Canada.|Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and.|Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and.', 'Canada|Canada|Canada', 2, 'Genealogy and Heraldry|Genotype|Markov Chains|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv763', '26787665', ': The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run.'),
(79755, 2016, 'bio', 'GeneValidator: identify problems with protein-coding gene predictions', 'Drxc4x83gan, Monica-Andreea|Moghul, Ismail|Priyam, Anurag|Bustos, Claudio|Wurm, Yannick', 'Department of Computer Science, ETH Zxc3xbcrich, Zxc3xbcrich, Switzerland.|School of Biological and Chemical Sciences, Queen Mary University of London, London, UK and.|School of Biological and Chemical Sciences, Queen Mary University of London, London, UK and.|Departamento de Psiquiatrxc3xada y Salud Mental, University of Concepcixc3xb3n, Concepcixc3xb3n, Chile.|School of Biological and Chemical Sciences, Queen Mary University of London, London, UK and.', 'Switzerland|-|-|Chile|-', 8, 'Databases, Genetic|Genes|Predictive Value of Tests|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw015', '26787666', ': Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions and to aid manual curation. For each gene, GV performs multiple analyses based on comparisons to gene sequences from large databases. The resulting report identifies problematic gene predictions and includes extensive statistics and graphs for each prediction to guide manual curation efforts. GV thus accelerates and enhances the work of biocurators and researchers who need accurate gene predictions from newly sequenced genomes.'),
(79756, 2016, 'bio', 'Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins', 'Stražar, Martin|Žitnik, Marinka|Zupan, Blaž|Ule, Jernej|Curk, Tomaž', 'University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, SI 1000, Slovenia.|University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, SI 1000, Slovenia.|University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, SI 1000, Slovenia Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.|University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, SI 1000, Slovenia.', 'Slovenia|Slovenia|Slovenia|United Kingdom|Slovenia', 40, 'Binding Sites|Data Collection|Datasets as Topic|Models, Molecular|RNA|RNA-Binding Proteins', 'Journal Article', '10.1093/bioinformatics/btw003', '26787667', 'RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein-RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein-RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed.'),
(79757, 2016, 'bio', 'Protein backbone ensemble generation explores the local structural space of unseen natural homologs', 'Schenkelberg, Christian D|Bystroff, Christopher', 'Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.|Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.', 'United States|United States', 3, 'Algorithms|Amino Acid Sequence|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw001', '26787668', 'Mutations in homologous proteins affect changes in the backbone conformation that involve a complex interplay of forces which are difficult to predict. Protein design algorithms need to anticipate these backbone changes in order to accurately calculate the energy of the structure given an amino acid sequence, without knowledge of the final, designed sequence. This is related to the problem of predicting small changes in the backbone between highly similar sequences.'),
(79758, 2016, 'bio', 'TFBSTools: an R/bioconductor package for transcription factor binding site analysis', 'Tan, Ge|Lenhard, Boris', 'Computational Regulatory Genomics, MRC Clinical Sciences Centre, Imperial College London, London W12 0NN, UK.|Computational Regulatory Genomics, MRC Clinical Sciences Centre, Imperial College London, London W12 0NN, UK.', 'United Kingdom|United Kingdom', 63, 'Animals|Binding Sites|Genome-Wide Association Study|Humans|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw024', '26794315', ': The ability to efficiently investigate transcription factor binding sites (TFBSs) genome-wide is central to computational studies of gene regulation. TFBSTools is an R/Bioconductor package for the analysis and manipulation of TFBSs and their associated transcription factor profile matrices. TFBStools provides a toolkit for handling TFBS profile matrices, scanning sequences and alignments including whole genomes, and querying the JASPAR database. The functionality of the package can be easily extended to include advanced statistical analysis, data visualization and data integration.'),
(79759, 2016, 'bio', 'Inclusion of dyad-repeat pattern improves topology prediction of transmembrane xcexb2-barrel proteins', 'Hayat, Sikander|Peters, Christoph|Shu, Nanjiang|Tsirigos, Konstantinos D|Elofsson, Arne', 'Memorial Sloan Kettering Cancer Center, New York City, NY, USA.|Stockholm Bioinformatics Center, SciLifeLab, Swedish E-Science Research Center, Stockholm University, Stockholm, SE, 10691, Sweden and.|Stockholm Bioinformatics Center, SciLifeLab, Swedish E-Science Research Center, Stockholm University, Stockholm, SE, 10691, Sweden and Sweden Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm University, Sweden.|Stockholm Bioinformatics Center, SciLifeLab, Swedish E-Science Research Center, Stockholm University, Stockholm, SE, 10691, Sweden and.|Stockholm Bioinformatics Center, SciLifeLab, Swedish E-Science Research Center, Stockholm University, Stockholm, SE, 10691, Sweden and.', 'United States|Sweden|Sweden|Sweden|Sweden', 0, 'Computational Biology|Membrane Proteins|Models, Molecular|Programming Languages|Protein Structure, Secondary', 'Journal Article', '10.1093/bioinformatics/btw025', '26794316', ': Accurate topology prediction of transmembrane xcexb2-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane xcexb2-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane xcexb2-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane xcexb2-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane xcexb2-barrel proteins.'),
(79760, 2016, 'bio', 'The TraDIS toolkit: sequencing and analysis for dense transposon mutant libraries', 'Barquist, Lars|Mayho, Matthew|Cummins, Carla|Cain, Amy K|Boinett, Christine J|Page, Andrew J|Langridge, Gemma C|Quail, Michael A|Keane, Jacqueline A|Parkhill, Julian', 'Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and Institute for Molecular Infection Biology, University of Würzburg, Würzburg D-97080, Germany.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and.', 'Germany|-|-|-|-|-|-|-|-|-', 42, 'DNA Transposable Elements|Gene Library|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw022', '26794317', 'Transposon insertion sequencing is a high-throughput technique for assaying large libraries of otherwise isogenic transposon mutants providing insight into gene essentiality, gene function and genetic interactions. We previously developed the Transposon Directed Insertion Sequencing (TraDIS) protocol for this purpose, which utilizes shearing of genomic DNA followed by specific PCR amplification of transposon-containing fragments and Illumina sequencing. Here we describe an optimized high-yield library preparation and sequencing protocol for TraDIS experiments and a novel software pipeline for analysis of the resulting data. The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools. This article can serve as a general reference for the application of the TraDIS methodology.'),
(79761, 2016, 'bio', 'Vasohibins: new transglutaminase-like cysteine proteases possessing a non-canonical Cys-His-Ser catalytic triad', 'Sanchez-Pulido, Luis|Ponting, Chris P', 'MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK.|MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK.', 'United Kingdom|United Kingdom', 19, 'Amino Acid Sequence|Biocatalysis|Cysteine|Cysteine Proteases|Transglutaminases', 'Journal Article', '10.1093/bioinformatics/btv761', '26794318', 'Vasohibin-1 and Vasohibin-2 regulate angiogenesis, tumour growth and metastasis. Their molecular functions, however, were previously unknown, in large part owing to their perceived lack of homology to proteins of known structure and function. To identify their functional amino acids and domains, their molecular activity and their evolutionary history, we undertook an in-depth analysis of Vasohibin sequences. We find that Vasohibin proteins are previously undetected members of the transglutaminase-like cysteine protease superfamily, and all possess a non-canonical Cys-His-Ser catalytic triad. We further propose a calcium-dependent activation mechanism for Vasohibin proteins. These findings can now be used to design constructs for protein structure determination and to develop enzyme inhibitors as angiogenic regulators to treat metastasis and tumour growth.'),
(79762, 2016, 'bio', 'ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts', 'Cavalcante, Raymond G|Patil, Snehal|Weymouth, Terry E|Bendinskas, Kestutis G|Karnovsky, Alla|Sartor, Maureen A', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Chemistry, State University of New York at Oswego, Oswego, NY 13126, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Datasets as Topic|Humans|Metabolic Networks and Pathways|Metabolomics|Software|Statistics as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btw016', '26794319', 'Capabilities in the field of metabolomics have grown tremendously in recent years. Many existing resources contain the chemical properties and classifications of commonly identified metabolites. However, the annotation of small molecules (both endogenous and synthetic) to meaningful biological pathways and concepts still lags behind the analytical capabilities and the chemistry-based annotations. Furthermore, no tools are available to visually explore relationships and networks among functionally related groups of metabolites (biomedical concepts). Such a tool would provide the ability to establish testable hypotheses regarding links among metabolic pathways, cellular processes, phenotypes and diseases.'),
(79763, 2016, 'bio', 'ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity', 'Witvliet, Daniel K|Strokach, Alexey|Giraldo-Forero, Andrés Felipe|Teyra, Joan|Colak, Recep|Kim, Philip M', 'Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Department of Molecular Genetics, University of Toronto.|Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Department of Computer Science, University of Toronto, Toronto, ON, Canada and.|Department of Electrical and Electronic Engineering, Universidad Nacional De Colombia, Manizales, Colombia.|Donnelly Center for Cellular and Biomolecular Research, University of Toronto.|Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Department of Computer Science, University of Toronto, Toronto, ON, Canada and.|Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Department of Molecular Genetics, University of Toronto, Department of Computer Science, University of Toronto, Toronto, ON, Canada and.', '-|Canada|Colombia|-|Canada|Canada', 0, 'Humans|Mutation|Protein Binding|Protein Folding|Protein Stability|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/btw031', '26801957', 'ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein-protein interactions. Here, we present the ELASPIC webserver, which makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain-domain interactions for all known proteins, as well as homology models of domains and domain-domain interactions for the human proteome. Homology models for proteins of other organisms are calculated on the fly, and mutations are evaluated within minutes once the homology model is available.'),
(79764, 2016, 'bio', 'MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing', 'Awan, Muaaz Gul|Saeed, Fahad', 'Department of Electrical and Computer Engineering and.|Department of Electrical and Computer Engineering and Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USA.', 'United States|United States', 11, 'Algorithms|Data Compression|Mass Spectrometry|Peptides|Proteomics', 'Journal Article', '10.1093/bioinformatics/btw023', '26801958', 'Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks.'),
(79765, 2016, 'bio', 'Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions', 'Niu, Zhen|Chasman, Deborah|Eisfeld, Amie J|Kawaoka, Yoshihiro|Roy, Sushmita', 'Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA.|Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA.|Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53711, USA.|Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53711, USA Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, Japan.|Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.', 'United States|United States|United States|Japan|United States', 4, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Consensus|Gene Expression Profiling|Gene Regulatory Networks|Genome|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw007', '26801959', 'Identifying the shared and pathogen-specific components of host transcriptional regulatory programs is important for understanding the principles of regulation of immune response. Recent efforts in systems biology studies of infectious diseases have resulted in a large collection of datasets measuring host transcriptional response to various pathogens. Computational methods to identify and compare gene expression modules across different infections offer a powerful way to identify strain-specific and shared components of the regulatory program. An important challenge is to identify statistically robust gene expression modules as well as to reliably detect genes that change their module memberships between infections.'),
(79766, 2016, 'bio', 'Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method', 'Du, Lei|Huang, Heng|Yan, Jingwen|Kim, Sungeun|Risacher, Shannon L|Inlow, Mark|Moore, Jason H|Saykin, Andrew J|Shen, Li|Alzheimerxe2x80x99s Disease Neuroimaging Initiative', 'Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Computer Science & Engineering, The University of Texas at Arlington, Arlington, TX, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA and.|Institute for Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.|Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.', 'India|United States|India|India|India|United States|United States|India|India|India', 0, 'Algorithms|Brain|Humans|Neuroimaging', 'Journal Article', '10.1093/bioinformatics/btw033', '26801960', 'Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated.'),
(79767, 2016, 'bio', 'MuCor: mutation aggregation and correlation', 'Kroll, Karl W|Eisfeld, Ann-Katherin|Lozanski, Gerard|Bloomfield, Clara D|Byrd, John C|Blachly, James S', 'Division of Hematology, Department of Internal Medicine.|Department of Human Cancer Genetics and Molecular Virology.|Department of Pathology, The Ohio State University, Columbus, OH 43210 and.|Division of Hematology, Department of Internal Medicine, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43210, USA.|Division of Hematology, Department of Internal Medicine, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43210, USA.|Division of Hematology, Department of Internal Medicine, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43210, USA.', 'United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Animals|Computational Biology|Humans|Mutation|Programming Languages|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btw028', '26803155', 'There are many tools for variant calling and effect prediction, but little to tie together large sample groups. Aggregating, sorting and summarizing variants and effects across a cohort is often done with ad hoc scripts that must be re-written for every new project. In response, we have written MuCor, a tool to gather variants from a variety of input formats (including multiple files per sample), perform database lookups and frequency calculations, and write many types of reports. In addition to use in large studies with numerous samples, MuCor can also be employed to directly compare variant calls from the same sample across two or more platforms, parameters or pipelines. A companion utility, DepthGauge, measures coverage at regions of interest to increase confidence in calls.'),
(79768, 2016, 'bio', 'Quantitative trait association study for mean telomere length in the South Asian genomes', 'Hakobyan, Anna|Nersisyan, Lilit|Arakelyan, Arsen', 'Bioinformatics Group, Institute of Molecular Biology NAS RA, Yerevan 0014, Armenia, College of Science and Engineering, American University of Armenia, Yerevan 0019, Armenia and.|Bioinformatics Group, Institute of Molecular Biology NAS RA, Yerevan 0014, Armenia, Synopsys Inc., Yerevan 0026, Armenia.|Bioinformatics Group, Institute of Molecular Biology NAS RA, Yerevan 0014, Armenia, Synopsys Inc., Yerevan 0026, Armenia.', 'Armenia|Armenia|Armenia', 5, 'Asian Continental Ancestry Group|Genome, Human|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide|Telomere', 'Journal Article', '10.1093/bioinformatics/btw027', '26803156', 'Mean telomere length (MTL) is associated with cancers and age-related diseases, which necessitates identification of genomic and environmental factors that impact telomere length dynamics. Here, we present a pilot genome wide association (GWA) study for MTL in South Asian population using publicly available next generation whole genome sequences (WGS), both for MTL and genotype calculations.'),
(79769, 2016, 'bio', 'lodGWAS: a software package for genome-wide association analysis of biomarkers with a limit of detection', 'Vaez, Ahmad|van der Most, Peter J|Prins, Bram P|Snieder, Harold|van den Heuvel, Edwin|Alizadeh, Behrooz Z|Nolte, Ilja M', 'Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran and.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Biomarkers|Genome-Wide Association Study|Humans|Limit of Detection|Software', 'Journal Article', '10.1093/bioinformatics/btw021', '26803157', 'Genome-wide association study (GWAS) of a biomarker is complicated when the assay procedure of the biomarker is restricted by a Limit of Detection (LOD). Those observations falling outside the LOD cannot be simply discarded, but should be included into the analysis by applying an appropriate statistical method. However, the problem of LOD in GWAS analysis of such biomarkers is usually overlooked. lodGWAS is a flexible, easy-to-use R package that provides a simple and elegant way for GWAS analysis of such biomarkers while simultaneously accommodating the problem of LOD by applying a parametric survival analysis method.'),
(79770, 2016, 'bio', 'FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees', 'Rainer, Johannes|Taliun, Daniel|DElia, Yuri|Pattaro, Cristian|Domingues, Francisco S|Weichenberger, Christian X', 'Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Bolzano 39100, Italy and.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA.|Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Bolzano 39100, Italy and.|Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Bolzano 39100, Italy and.|Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Bolzano 39100, Italy and.|Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Bolzano 39100, Italy and.', 'Germany|United States|Germany|Germany|Germany|Germany', 5, 'Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btw019', '26803158', 'Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families.'),
(79771, 2016, 'bio', 'ParDRe: faster parallel duplicated reads removal tool for sequencing studies', 'Gonzxc3xa1lez-Domxc3xadnguez, Jorge|Schmidt, Bertil', 'Grupo de Arquitectura de Computadores, Universidade da Coruxc3xb1a, Campus De Elvixc3xb1a, 15071, A Coruxc3xb1a, Spain and.|Parallel and Distributed Architectures Group, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.', 'Spain|Germany', 0, 'Algorithms|Cluster Analysis|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw038', '26803159', 'Current next generation sequencing technologies often generate duplicated or near-duplicated reads that (depending on the application scenario) do not provide any interesting biological information but can increase memory requirements and computational time of downstream analysis. In this work we present ParDRe, a de novo parallel tool to remove duplicated and near-duplicated reads through the clustering of Single-End or Paired-End sequences from fasta or fastq files. It uses a novel bitwise approach to compare the suffixes of DNA strings and employs hybrid MPI/multithreading to reduce runtime on multicore systems. We show that ParDRe is up to 27.29 times faster than Fulcrum (a representative state-of-the-art tool) on a platform with two 8-core Sandy-Bridge processors.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79772, 2016, 'bio', 'Alloscore: a method for predicting allosteric ligand-protein interactions', 'Li, Shuai|Shen, Qiancheng|Su, Minyi|Liu, Xinyi|Lu, Shaoyong|Chen, Zhongjie|Wang, Renxiao|Zhang, Jian', 'Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.|Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.|Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China and.', 'China|China|China|China|China|China|China|China', 18, 'Allosteric Site|Drug Discovery|Ligands|Proteins', 'Journal Article', '10.1093/bioinformatics/btw036', '26803160', 'Allosteric ligands have increasingly gained attention as potential therapeutic agents due to their higher target selectivity and lower toxicity compared with classic orthosteric ligands. Despite the great interest in the development of allosteric drugs as a new tactic in drug discovery, the understanding of the ligand-protein interactions underlying allosteric binding represents a key challenge. Herein, we introduce Alloscore, a web server that predicts the binding affinities of allosteric ligand-protein interactions. This method exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists.'),
(79773, 2016, 'bio', 'PAA: an R/bioconductor package for biomarker discovery with protein microarrays', 'Turewicz, Michael|Ahrens, Maike|May, Caroline|Marcus, Katrin|Eisenacher, Martin', 'Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany.|Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany.|Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany.|Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany.|Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Biomarkers|Protein Array Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw037', '26803161', 'The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAAs multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels.'),
(79774, 2016, 'bio', 'DesignSignatures: a tool for designing primers that yields amplicons with distinct signatures', 'Wright, Erik S|Vetsigian, Kalin H', 'Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53715, USA.|Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53715, USA.', 'United States|United States', 4, 'Animals|DNA Primers|DNA Restriction Enzymes|Humans|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btw047', '26803162', 'For numerous experimental applications, PCR primers must be designed to efficiently amplify a set of homologous DNA sequences while giving rise to amplicons with maximally diverse signatures. We developed DesignSignatures to automate the process of designing primers for high-resolution melting (HRM), fragment length polymorphism (FLP) and sequencing experiments. The program also finds the best restriction enzyme to further diversify HRM or FLP signatures. This enables efficient comparison across many experimental designs in order to maximize signature diversity.'),
(79775, 2016, 'bio', 'BISQUE: locus- and variant-specific conversion of genomic, transcriptomic and proteomic database identifiers', 'Meyer, Michael J|Geske, Philip|Yu, Haiyuan', 'Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA and Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA.|Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA and.|Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA and.', 'United States|United States|United States', 0, 'Databases, Factual|Genome|Genomics|Humans|Proteomics|Software|Transcriptome|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw043', '26803163', 'Biological sequence databases are integral to efforts to characterize and understand biological molecules and share biological data. However, when analyzing these data, scientists are often left holding disparate biological currency-molecular identifiers from different databases. For downstream applications that require converting the identifiers themselves, there are many resources available, but analyzing associated loci and variants can be cumbersome if data is not given in a form amenable to particular analyses. Here we present BISQUE, a web server and customizable command-line tool for converting molecular identifiers and their contained loci and variants between different database conventions. BISQUE uses a graph traversal algorithm to generalize the conversion process for residues in the human genome, genes, transcripts and proteins, allowing for conversion across classes of molecules and in all directions through an intuitive web interface and a URL-based web service.'),
(79776, 2016, 'bio', 'Computational approaches to define a human milk metaglycome', 'Agravat, Sanjay B|Song, Xuezheng|Rojsajjakul, Teerapat|Cummings, Richard D|Smith, David F', 'Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA and.|Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA.|Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA and.|Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA and.|Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA.', 'Israel|United States|Israel|Israel|United States', 0, 'Carbohydrate Sequence|Humans|Mass Spectrometry|Microarray Analysis|Milk, Human|Polysaccharides', 'Journal Article', '10.1093/bioinformatics/btw048', '26803164', 'The goal of deciphering the human glycome has been hindered by the lack of high-throughput sequencing methods for glycans. Although mass spectrometry (MS) is a key technology in glycan sequencing, MS alone provides limited information about the identification of monosaccharide constituents, their anomericity and their linkages. These features of individual, purified glycans can be partly identified using well-defined glycan-binding proteins, such as lectins and antibodies that recognize specific determinants within glycan structures.'),
(79777, 2016, 'bio', 'FILTUS: a desktop GUI for fast and efficient detection of disease-causing variants, including a novel autozygosity detector', 'Vigeland, Magnus D|Gjøtterud, Kristina S|Selmer, Kaja K', 'Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo N-0424.|Department of Research, Cancer Registry of Norway, Oslo N-0304, Norway.|Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo N-0424.', '-|Norway|-', 0, 'Exome|Humans|Programming Languages|Software|Zygote', 'Journal Article', '10.1093/bioinformatics/btw046', '26819469', 'FILTUS is a stand-alone tool for working with annotated variant files, e.g. when searching for variants causing Mendelian disease. Very flexible in terms of input file formats, FILTUS offers efficient filtering and a range of downstream utilities, including statistical analysis of gene sharing patterns, detection of de novo mutations in trios, quality control plots and autozygosity mapping. The autozygosity mapping is based on a hidden Markov model and enables accurate detection of autozygous regions directly from exome-scale variant files.'),
(79778, 2016, 'bio', 'Differential methylation analysis for BS-seq data under general experimental design', 'Park, Yongseok|Wu, Hao', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA and.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.', 'United States|United States', 68, 'Algorithms|DNA Methylation|Epigenesis, Genetic|Genome|Research Design', 'Journal Article', '10.1093/bioinformatics/btw026', '26819470', 'DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods.'),
(79779, 2016, 'bio', 'On the discovery of hospital admission patterns-a clarification', 'Arandjelovixc4x87, Ognjen', 'School of Computer Science, University of St Andrews, St Andrews, UK.', 'United Kingdom', 0, '-', 'Letter', '10.1093/bioinformatics/btw049', '26819471', 'ognjen.arandjelvoic@gmail.com.'),
(79780, 2016, 'bio', 'Unipept web services for metaproteomics analysis', 'Mesuere, Bart|Willems, Toon|Van der Jeugt, Felix|Devreese, Bart|Vandamme, Peter|Dawyndt, Peter', 'Department of Applied Mathematics, Computer Science and Statistics.|Department of Applied Mathematics, Computer Science and Statistics.|Department of Applied Mathematics, Computer Science and Statistics.|Laboratory for Protein Biochemistry and Biomolecular Engineering, Faculty of Sciences and.|Laboratory for Microbiology Faculty of Sciences, Ghent University, B-9000, Ghent, Belgium.|Department of Applied Mathematics, Computer Science and Statistics.', '-|-|-|-|Belgium|-', 25, 'Computational Biology|Databases, Genetic|Information Storage and Retrieval|Internet|Knowledge Bases|Metabolomics|Peptides|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btw039', '26819472', 'Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines.'),
(79781, 2016, 'bio', 'BioCircosjs: an interactive Circos JavaScript library for biological data visualization on web applications', 'Cui, Ya|Chen, Xiaowei|Luo, Huaxia|Fan, Zhen|Luo, Jianjun|He, Shunmin|Yue, Haiyan|Zhang, Peng|Chen, Runsheng', 'Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China, University of Chinese Academy of Sciences, Beijing 100049, China.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China, Core Facility for Protein Research, Institute of Biophysics.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China, Core Facility for Protein Research, Institute of Biophysics.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China and.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China, University of Chinese Academy of Sciences, Beijing 100049, China.|University of Chinese Academy of Sciences, Beijing 100049, China.|Key Laboratory of RNA Biology, Beijing Key Laboratory of Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China, Research Network of Computational Biology, RNCB, Beijing 100101, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Computer Graphics|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btw041', '26819473', 'We here present BioCircos.js, an interactive and lightweight JavaScript library especially for biological data interactive visualization. BioCircos.js facilitates the development of web-based applications for circular visualization of various biological data, such as genomic features, genetic variations, gene expression and biomolecular interactions.'),
(79782, 2016, 'bio', 'Galaxy Portal: interacting with the galaxy platform through mobile devices', 'Bxc3xb8rnich, Claus|Grytten, Ivar|Hovig, Eivind|Paulsen, Jonas|xc4x8cech, Martin|Sandve, Geir Kjetil', 'Biomedicial Informatics, Department of Informatics, University of Oslo, Oslo, Norway.|Biomedicial Informatics, Department of Informatics, University of Oslo, Oslo, Norway.|Biomedicial Informatics, Department of Informatics, University of Oslo, Oslo, Norway, Department of Tumor Biology, Institute of Cancer Research, Oslo, Norway, Department of Cancer Genetics and Informatics, Radium Hospital, Part of Oslo University Hospital, Oslo, Norway and.|Biomedicial Informatics, Department of Informatics, University of Oslo, Oslo, Norway.|Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania 16802, USA.|Biomedicial Informatics, Department of Informatics, University of Oslo, Oslo, Norway.', 'Norway|Norway|Norway|Norway|United States|Norway', 4, 'Mobile Applications|Software', 'Journal Article', '10.1093/bioinformatics/btw042', '26819474', ': We present Galaxy Portal app, an open source interface to the Galaxy system through smart phones and tablets. The Galaxy Portal provides convenient and efficient monitoring of job completion, as well as opportunities for inspection of results and execution history. In addition to being useful to the Galaxy community, we believe that the app also exemplifies a useful way of exploiting mobile interfaces for research/high-performance computing resources in general.'),
(79783, 2016, 'bio', 'ASDB: a resource for probing protein functions with small molecules', 'Liu, Zhihong|Ding, Peng|Yan, Xin|Zheng, Minghao|Zhou, Huihao|Xu, Yuehua|Du, Yunfei|Gu, Qiong|Xu, Jun', 'Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|Department of Pharmacy, Cancer Hospital of Shantou University Medical College, Shantou 515031, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|National University of Defense Technology, National Supercomupter Center in Guangzhou, Sun Yat-sen University, Guangzhou 510006, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.|Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Databases, Factual|Drug Delivery Systems|Internet|Proteins', 'Journal Article', '10.1093/bioinformatics/btw055', '26822210', ': Identifying chemical probes or seeking scaffolds for a specific biological target is important for protein function studies. Therefore, we create the Annotated Scaffold Database (ASDB), a computer-readable and systematic target-annotated scaffold database, to serve such needs. The scaffolds in ASDB were derived from public databases including ChEMBL, DrugBank and TCMSP, with a scaffold-based classification approach. Each scaffold was assigned with an InChIKey as its unique identifier, energy-minimized 3D conformations, and other calculated properties. A scaffold is also associated with drugs, natural products, drug targets and medical indications. The database can be retrieved through text or structure query tools. ASDB collects 333 601 scaffolds, which are associated with 4368 targets. The scaffolds consist of 3032 scaffolds derived from drugs and 5163 scaffolds derived from natural products. For given scaffolds, scaffold-target networks can be generated from the database to demonstrate the relations of scaffolds and targets.'),
(79784, 2016, 'bio', 'FastLSU: a more practical approach for the Benjamini-Hochberg FDR controlling procedure for huge-scale testing problems', 'Madar, Vered|Batista, Sandra', 'Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC 27709, USA and.|Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.', 'United States|United States', 11, 'Algorithms|Software', 'Journal Article', '10.1093/bioinformatics/btw029', '26826716', 'We address a common problem in large-scale data analysis, and especially the field of genetics, the huge-scale testing problem, where millions to billions of hypotheses are tested together creating a computational challenge to control the inflation of the false discovery rate. As a solution we propose an alternative algorithm for the famous Linear Step Up procedure of Benjamini and Hochberg.'),
(79785, 2016, 'bio', 'Library of binding protein scaffolds (LibBP): a computational platform for selection of binding protein scaffolds', 'Hong, Seungpyo|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, Daejeon 305-338, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 305-338, South Korea.', '-|-', 0, 'Amino Acid Sequence|Gene Library|Humans|Protein Binding|Protein Engineering|Proteins', 'Journal Article', '10.1093/bioinformatics/btw032', '26826717', 'Developments in biotechnology have enabled the in vitro evolution of binding proteins. The emerging limitations of antibodies in binding protein engineering have led to suggestions for other proteins as alternative binding protein scaffolds. Most of these proteins were selected based on human intuition rather than systematic analysis of the available data. To improve this strategy, we developed a computational framework for finding desirable binding protein scaffolds by utilizing protein structure and sequence information.'),
(79786, 2016, 'bio', 'BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data', 'Narasimhan, Vagheesh|Danecek, Petr|Scally, Aylwyn|Xue, Yali|Tyler-Smith, Chris|Durbin, Richard', 'Wellcome Trust Sanger Institute, Hinxton and.|Wellcome Trust Sanger Institute, Hinxton and.|Department of Genetics, University of Cambridge, Cambridge, UK.|Wellcome Trust Sanger Institute, Hinxton and.|Wellcome Trust Sanger Institute, Hinxton and.|Wellcome Trust Sanger Institute, Hinxton and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Exome|Genomics|Genotype|High-Throughput Nucleotide Sequencing|Homozygote|Software', 'Journal Article', '10.1093/bioinformatics/btw044', '26826718', 'Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity.'),
(79787, 2016, 'bio', 'The geospatial data quality REST API for primary biodiversity data', 'Otegui, Javier|Guralnick, Robert P', 'Florida Museum of Natural History, University of Florida, Gainesville, FL, USA.|Florida Museum of Natural History, University of Florida, Gainesville, FL, USA.', 'United States|United States', 6, 'Biodiversity|Data Accuracy|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw057', '26833340', 'We present a REST web service to assess the geospatial quality of primary biodiversity data. It enables access to basic and advanced functions to detect completeness and consistency issues as well as general errors in the provided record or set of records. The API uses JSON for data interchange and efficient parallelization techniques for fast assessments of large datasets.'),
(79788, 2016, 'bio', 'Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data', 'Chen, Jinyu|Zhang, Shihua', 'National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.', 'China|China', 31, 'Algorithms|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Humans', 'Journal Article', '10.1093/bioinformatics/btw059', '26833341', 'The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development. However, how to identify the underlying combinatorial patterns among pharmacogenomics data are still a challenging issue.'),
(79789, 2016, 'bio', 'Sprites: detection of deletions from sequencing data by re-aligning split reads', 'Zhang, Zhen|Wang, Jianxin|Luo, Junwei|Ding, Xiaojun|Zhong, Jiancheng|Wang, Jun|Wu, Fang-Xiang|Pan, Yi', 'School of Information Science and Engineering, Central South University, Changsha, 410083, China, College of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|Department of Molecular Physiology & Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada and.|Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA.', 'China|China|China|China|China|United States|Canada|Georgia', 0, 'Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btw053', '26833342', 'Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, deletions with microhomologies and deletions with microsinsertions. The last two types are very common in the human genome, but they pose difficulty for the detection. Furthermore, finding deletions from sequencing data remains challenging. It is highly appealing to develop sensitive and accurate methods to detect deletions from sequencing data, especially deletions with microhomology and deletions with microinsertion.'),
(79790, 2016, 'bio', 'Model-based media selection to minimize the cost of metabolic cooperation in microbial ecosystems', 'Zampieri, Mattia|Sauer, Uwe', 'Department of Biology, Institute for Molecular Systems Biology, Zurich 8093, Switzerland.|Department of Biology, Institute for Molecular Systems Biology, Zurich 8093, Switzerland.', 'Switzerland|Switzerland', 0, 'Algorithms|Ecosystem|Models, Biological|Models, Theoretical|Programming, Linear', 'Journal Article', '10.1093/bioinformatics/btw062', '26833343', 'Simple forms of mutualism between microorganisms are widespread in nature. Nevertheless, the role played by the environmental nutrient composition in mediating cross-feeding in microbial ecosystems is still poorly understood.'),
(79791, 2016, 'bio', 'A zero-inflated Poisson model for insertion tolerance analysis of genes based on Tn-seq data', 'Liu, Fangfang|Wang, Chong|Wu, Zuowei|Zhang, Qijing|Liu, Peng', 'Department of Statistics, Iowa State University.|Department of Statistics, Iowa State University, Department of Veterinary Diagnostic and Production Animal Medicine and.|Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50010, USA.|Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50010, USA.|Department of Statistics, Iowa State University.', 'United States|United States|United States|United States|United States', 7, 'Algorithms|DNA Transposable Elements|High-Throughput Nucleotide Sequencing|Likelihood Functions', 'Journal Article', '10.1093/bioinformatics/btw061', '26833344', 'Transposon insertion sequencing (Tn-seq) is an emerging technology that combines transposon mutagenesis with next-generation sequencing technologies for the identification of genes related to bacterial survival. The resulting data from Tn-seq experiments consist of sequence reads mapped to millions of potential transposon insertion sites and a large portion of insertion sites have zero mapped reads. Novel statistical method for Tn-seq data analysis is needed to infer functions of genes on bacterial growth.'),
(79792, 2016, 'bio', 'GiANT: gene set uncertainty in enrichment analysis', 'Schmid, Florian|Schmid, Matthias|Müssel, Christoph|Sträng, J Eric|Buske, Christian|Bullinger, Lars|Kraus, Johann M|Kestler, Hans A', 'Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany.|Institut für Medizinische Biometrie, Informatik und Epidemiologie, Universität Bonn, Bonn 53127, Germany.|Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany.|Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany.|Institute of Experimental Cancer Research.|Department of Internal Medicine III, Ulm University, Ulm 89069, Germany and.|Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany.|Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany, Leibniz Institute on Ageing - Fritz Lipmann Institute and FSU Jena, Jena 07745, Germany.', 'Germany|Germany|Germany|Germany|-|Germany|Germany|Germany', 3, 'Algorithms|Animals|Computer Simulation|Gene Regulatory Networks|Genes, Retinoblastoma|Humans|Mice|Neoplasms|Software|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btw030', '26833345', 'Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization.'),
(79793, 2016, 'bio', 'Optimization of alignment-based methods for taxonomic binning of metagenomics reads', 'Jaillard, Magali|Tournoud, Maud|Meynier, Faustine|Veyrieras, Jean-Baptiste', 'Bioinformatics Research Department, bioMerieux, Marcy Letoile 69280, France, LBBE, UMR CNRS 5558 University Lyon 1, Villeurbanne F-69622, France.|Bioinformatics Research Department, bioMerieux, Marcy Letoile 69280, France.|Biomathematics System Development, bioMerieux, Marcy Letoile 69280, France.|Bioinformatics Research Department, bioMerieux, Marcy Letoile 69280, France.', 'France|France|France|France', 0, 'Algorithms|Humans|Metagenome|Metagenomics|Microbiota|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw040', '26833346', 'Alignment-based taxonomic binning for metagenome characterization proceeds in two steps: reads mapping against a reference database (RDB) and taxonomic assignment according to the best hits. Beyond the sequencing technology and the completeness of the RDB, selecting the optimal configuration of the workflow, in particular the mapper parameters and the best hit selection threshold, to get the highest binning performance remains quite empirical.'),
(79794, 2016, 'bio', 'MOCCASIN: converting MATLAB ODE models to SBML', 'Gómez, Harold F|Hucka, Michael|Keating, Sarah M|Nudelman, German|Iber, Dagmar|Sealfon, Stuart C', 'Department of Biosystems Science and Engineering, ETH Zürich, Basel CH 4058, Switzerland.|Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge CB10 1SD, UK and.|Department of Neurology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center and School of Medicine, New York, NY 10029, USA.|Department of Biosystems Science and Engineering, ETH Zürich, Basel CH 4058, Switzerland.|Department of Neurology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center and School of Medicine, New York, NY 10029, USA.', 'Switzerland|United States|United States|United States|Switzerland|United States', 6, 'Computational Biology|Computer Simulation|Models, Biological|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw056', '26861819', 'MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging-especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format.'),
(79795, 2016, 'bio', 'Characterization of clinical signs in the human interactome', 'Chagoyen, Monica|Pazos, Florencio', 'Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Darwin 3, Madrid 28049, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Darwin 3, Madrid 28049, Spain.', 'Spain|Spain', 6, 'Humans|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btw054', '26861820', 'Many diseases are related by shared associated molecules and pathways, exhibiting comorbidities and common phenotypes, an indication of the continuous nature of the human pathological landscape. Although it is continuous, this landscape is always partitioned into discrete diseases when studied at the molecular level. Clinical signs are also important phenotypic descriptors that can reveal the molecular mechanisms that underlie pathological states, but have seldom been the subject of systemic research. Here, we quantify the modular nature of the clinical signs associated with genetic diseases in the human interactome.'),
(79796, 2016, 'bio', 'TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data', 'De Sano, Luca|Caravagna, Giulio|Ramazzotti, Daniele|Graudenzi, Alex|Mauri, Giancarlo|Mishra, Bud|Antoniotti, Marco', 'Department of Informatics, Systems and Communication, University of Milano-Bicocca.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, School of Informatics, University of Edinburgh, Edinburgh, UK.|Department of Informatics, Systems and Communication, University of Milano-Bicocca.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan Center for Neuroscience, University of Milan-Bicocca, Milan, Italy.', 'United Kingdom|United Kingdom|United Kingdom|Italy|United Kingdom|United Kingdom|Italy', 0, 'Algorithms|Disease Progression|Epigenesis, Genetic|Genomics|Humans|Models, Theoretical|Neoplasms|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw035', '26861821', 'We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy.'),
(79797, 2016, 'bio', 'Measuring the spatial correlations of protein binding sites', 'Wei, Yingying|Wu, Hao', 'Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong and.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.', 'Hong Kong|United States', 1, 'Binding Sites|DNA-Binding Proteins|Gene Knockout Techniques|Genome|Genomics|High-Throughput Screening Assays|Linear Models|Protein Binding', 'Journal Article', '10.1093/bioinformatics/btw058', '26861822', 'Understanding the interactions of different DNA binding proteins is a crucial first step toward deciphering gene regulatory mechanism. With advances of high-throughput sequencing technology such as ChIP-seq, the genome-wide binding sites of many proteins have been profiled under different biological contexts. It is of great interest to quantify the spatial correlations of the binding sites, such as their overlaps, to provide information for the interactions of proteins. Analyses of the overlapping patterns of binding sites have been widely performed, mostly based on ad hoc methods. Due to the heterogeneity and the tremendous size of the genome, such methods often lead to biased even erroneous results.'),
(79798, 2016, 'bio', 'Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast', 'Jain, Shobhit|Bader, Gary D', 'Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada', 1, 'Amino Acid Sequence|Bayes Theorem|Binding Sites|Peptides|Protein Binding|Saccharomyces cerevisiae|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btw045', '26861823', 'Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein-protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.'),
(79799, 2016, 'bio', 'An efficient gene-gene interaction test for genome-wide association studies in trio families', 'Sung, Pei-Yuan|Wang, Yi-Ting|Yu, Ya-Wen|Chung, Ren-Hua', 'Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and.|Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.', '-|-|-|-', 0, 'Data Interpretation, Statistical|Epistasis, Genetic|Genome-Wide Association Study|Humans|Parents|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw077', '26873927', 'Several efficient gene-gene interaction tests have been developed for unrelated case-control samples in genome-wide association studies (GWAS), making it possible to test tens of billions of interaction pairs of single-nucleotide polymorphisms (SNPs) in a reasonable timeframe. However, current family-based gene-gene interaction tests are computationally expensive and are not applicable to genome-wide interaction analysis.'),
(79800, 2016, 'bio', 'SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data', 'Kahles, André|Ong, Cheng Soon|Zhong, Yi|Rätsch, Gunnar', 'Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA and.|Canberra Research Laboratory, NICTA, Canberra, ACT 2601, Australia.|Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA and.|Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA and.', '-|Australia|-|-', 33, 'Alternative Splicing|Gene Expression Profiling|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw076', '26873928', 'Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.'),
(79801, 2016, 'bio', 'Gene expression inference with deep learning', 'Chen, Yifei|Li, Yi|Narayan, Rajiv|Subramanian, Aravind|Xie, Xiaohui', 'Department of Computer Science, University of California, Irvine, CA 92697, USA Baidu Research-Big Data Lab, Beijing, 100085, China.|Department of Computer Science, University of California, Irvine, CA 92697, USA.|Broad Institute of MIT And Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT And Harvard, Cambridge, MA 02142, USA.|Department of Computer Science, University of California, Irvine, CA 92697, USA Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.', 'China|United States|United States|United States|United States', 138, 'Gene Expression|Gene Expression Profiling|Linear Models|Machine Learning|RNA', 'Journal Article', '10.1093/bioinformatics/btw074', '26873929', 'Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only xe2x88xbc1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes.'),
(79802, 2016, 'bio', 'Trinculo: Bayesian and frequentist multinomial logistic regression for genome-wide association studies of multi-category phenotypes', 'Jostins, Luke|McVean, Gilean', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, UK Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, UK.', 'United Kingdom|United Kingdom', 0, 'Bayes Theorem|Genome-Wide Association Study|Humans|Logistic Models|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btw075', '26873930', 'For many classes of disease the same genetic risk variants underly many related phenotypes or disease subtypes. Multinomial logistic regression provides an attractive framework to analyze multi-category phenotypes, and explore the genetic relationships between these phenotype categories. We introduce Trinculo, a program that implements a wide range of multinomial analyses in a single fast package that is designed to be easy to use by users of standard genome-wide association study software.'),
(79803, 2016, 'bio', 'Privacy-preserving microbiome analysis using secure computation', 'Wagner, Justin|Paulson, Joseph N|Wang, Xiao|Bhattacharjee, Bobby|Corrada Bravo, Hxc3xa9ctor', 'Center for Bioinformatics and Computational Biologyand.|Center for Bioinformatics and Computational Biologyand.|Maryland Cybersecurity Center, Department of Computer Science, University of Maryland, College Park, MD USA.|Maryland Cybersecurity Center, Department of Computer Science, University of Maryland, College Park, MD USA.|Center for Bioinformatics and Computational Biologyand.', 'United States|United States|United States|United States|United States', 7, 'DNA|Humans|Metagenomics|Microbiota|Privacy|Software', 'Journal Article', '10.1093/bioinformatics/btw073', '26873931', 'Developing targeted therapeutics and identifying biomarkers relies on large amounts of research participant data. Beyond human DNA, scientists now investigate the DNA of micro-organisms inhabiting the human body. Recent work shows that an individuals collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving analysis tools does not meet the requirements for microbiome sequencing studies.'),
(79804, 2016, 'bio', 'An empirical Bayes change-point model for identifying 3 and 5 alternative splicing by next-generation RNA sequencing', 'Zhang, Jie|Wei, Zhi', 'Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.', 'Jersey|Jersey', 7, 'Alternative Splicing|Bayes Theorem|High-Throughput Nucleotide Sequencing|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw060', '26873932', 'Next-generation RNA sequencing (RNA-seq) has been widely used to investigate alternative isoform regulations. Among them, alternative 3 : splice site (SS) and 5 : SS account for more than 30% of all alternative splicing (AS) events in higher eukaryotes. Recent studies have revealed that they play important roles in building complex organisms and have a critical impact on biological functions which could cause disease. Quite a few analytical methods have been developed to facilitate alternative 3 : SS and 5 : SS studies using RNA-seq data. However, these methods have various limitations and their performances may be further improved.'),
(79805, 2016, 'bio', 'Selecting a classification function for class prediction with gene expression data', 'Jong, Victor L|Novianti, Putri W|Roes, Kit C B|Eijkemans, Marinus J C', 'Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA, Utrecht, The Netherlands, Viroscience Lab, Erasmus Medical Center Rotterdam, Rotterdam, CE 3015, The Netherlands and.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA, Utrecht, The Netherlands, Epidemiology & Biostatistics Department, Vrije University Medical Center Amsterdam, HV Amsterdam 1081, The Netherlands.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA, Utrecht, The Netherlands.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 8, 'Computational Biology|Computer Simulation|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Theoretical|Neoplasms|Regression Analysis|Sample Size', 'Journal Article', '10.1093/bioinformatics/btw034', '26873933', 'Class predicting with gene expression is widely used to generate diagnostic and/or prognostic models. The literature reveals that classification functions perform differently across gene expression datasets. The question, which classification function should be used for a given dataset remains to be answered. In this study, a predictive model for choosing an optimal function for class prediction on a given dataset was devised.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79806, 2016, 'bio', 'Beyond accuracy: creating interoperable and scalable text-mining web services', 'Wei, Chih-Hsuan|Leaman, Robert|Lu, Zhiyong', 'National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA.', 'United States|United States|United States', 0, 'Data Mining|Humans|Internet|PubMed|Software', 'Journal Article', '10.1093/bioinformatics/btv760', '26883486', 'The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text.'),
(79807, 2016, 'bio', 'Computationally expanding infinium HumanMethylation450 BeadChip array data to reveal distinct DNA methylation patterns of rheumatoid arthritis', 'Fan, Shicai|Li, Chengzhe|Ai, Rizi|Wang, Mengchi|Firestein, Gary S|Wang, Wei', 'School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China Department of Chemistry and Biochemistry.|School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.|Department of Chemistry and Biochemistry.|Department of Chemistry and Biochemistry.|Division of Rheumatology, Allergy and Immunology, University of California San Diego, La Jolla, CA, USA.|Department of Chemistry and Biochemistry.', 'China|China|United States|United States|United States|United States', 8, 'Arthritis, Rheumatoid|CpG Islands|DNA Methylation|Fibroblasts|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw089', '26883487', 'DNA methylation signatures in rheumatoid arthritis (RA) have been identified in fibroblast-like synoviocytes (FLS) with Illumina HumanMethylation450 array. Since <2% of CpG sites are covered by the Illumina 450K array and whole genome bisulfite sequencing is still too expensive for many samples, computationally predicting DNA methylation levels based on 450K data would be valuable to discover more RA-related genes.'),
(79808, 2016, 'bio', 'RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data', 'Zhan, Xiaowei|Hu, Youna|Li, Bingshan|Abecasis, Goncalo R|Liu, Dajiang J', 'Department of Clinical Science, Quantitative Biomedical Research Center, Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|A9.Com Inc, Palo Alto, CA 94301, USA.|Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37240, USA.|Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.|Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA and Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA 17033, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Genetic Variation|Genotype|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw079', '27153000', 'Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.'),
(79809, 2016, 'bio', 'ProtAnnot: an App for Integrated Genome Browser to display how alternative splicing and transcription affect proteins', 'Mall, Tarun|Eckstein, John|Norris, David|Vora, Hiral|Freese, Nowlan H|Loraine, Ann E', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Alternative Splicing|Genome|Genomics|Humans|Molecular Sequence Annotation|Proteins|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw068', '27153567', 'One gene can produce multiple transcript variants encoding proteins with different functions. To facilitate visual analysis of transcript variants, we developed ProtAnnot, which shows protein annotations in the context of genomic sequence. ProtAnnot searches InterPro and displays profile matches (protein annotations) alongside gene models, exposing how alternative promoters, splicing and 3 end processing add, remove, or remodel functional motifs. To draw attention to these effects, ProtAnnot color-codes exons by frame and displays a cityscape graphic summarizing exonic sequence at each position. These techniques make visual analysis of alternative transcripts faster and more convenient for biologists.'),
(79810, 2016, 'bio', 'Integrated genome browser: visual analytics platform for genomics', 'Freese, Nowlan H|Norris, David C|Loraine, Ann E', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA.', 'United States|United States|United States', 92, 'Animals|Chromatin Immunoprecipitation|Computational Biology|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw069', '27153568', 'Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser.'),
(79811, 2016, 'bio', 'A quantitative model for oxygen uptake and release in a family of hemeproteins', 'Bustamante, Juan P|Szretter, María E|Sued, Mariela|Martí, Marcelo A|Estrin, Darío A|Boechi, Leonardo', 'Departamento de Química Inorgánica, Analítica Y Química Física, INQUIMAE-CONICET, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires.|Instituto De Cálculo, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires Departamento De Matemática, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires.|Instituto De Cálculo, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires.|Departamento De Química Biológica E Instituto De Química Biológica De La Facultad De Ciencias Exactas Y Naturales (IQUIBICEN), Universidad De Buenos Aires, Buenos Aires, Argentina.|Departamento de Química Inorgánica, Analítica Y Química Física, INQUIMAE-CONICET, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires.|Instituto De Cálculo, Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires.', '-|-|-|Argentina|-|-', 0, 'Hemeproteins|Kinetics|Ligands|Oxygen|Truncated Hemoglobins', 'Journal Article', '10.1093/bioinformatics/btw083', '27153569', 'Hemeproteins have many diverse functions that largely depend on the rate at which they uptake or release small ligands, like oxygen. These proteins have been extensively studied using either simulations or experiments, albeit only qualitatively and one or two proteins at a time.'),
(79812, 2016, 'bio', 'Alpha-CENTAURI: assessing novel centromeric repeat sequence variation with long read sequencing', 'Sevim, Volkan|Bashir, Ali|Chin, Chen-Shan|Miga, Karen H', 'Pacific Biosciences, Inc., Menlo Park, CA 94025, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA.|Pacific Biosciences, Inc., Menlo Park, CA 94025, USA.|Center for Biomolecular Science and Engineering, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States', 18, 'Algorithms|Centromere|Computational Biology|Consensus Sequence|Female|Genomics|Humans|Hydatidiform Mole|Pregnancy|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btw101', '27153570', 'Long arrays of near-identical tandem repeats are a common feature of centromeric and subtelomeric regions in complex genomes. These sequences present a source of repeat structure diversity that is commonly ignored by standard genomic tools. Unlike reads shorter than the underlying repeat structure that rely on indirect inference methods, e.g. assembly, long reads allow direct inference of satellite higher order repeat structure. To automate characterization of local centromeric tandem repeat sequence variation we have designed Alpha-CENTAURI (ALPHA satellite CENTromeric AUtomated Repeat Identification), that takes advantage of Pacific Bioscience long-reads from whole-genome sequencing datasets. By operating on reads prior to assembly, our approach provides a more comprehensive set of repeat-structure variants and is not impacted by rearrangements or sequence underrepresentation due to misassembly.'),
(79813, 2016, 'bio', 'An algorithm for designing minimal microbial communities with desired metabolic capacities', 'Eng, Alexander|Borenstein, Elhanan', 'Department of Genome Sciences.|Department of Genome Sciences Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA Santa Fe Institute, Santa Fe, NM, USA.', 'United States|United States', 21, 'Algorithms|Metabolic Networks and Pathways|Microbiota|Programming, Linear', 'Journal Article', '10.1093/bioinformatics/btw107', '27153571', 'Recent efforts to manipulate various microbial communities, such as fecal microbiota transplant and bioreactor systems optimization, suggest a promising route for microbial community engineering with numerous medical, environmental and industrial applications. However, such applications are currently restricted in scale and often rely on mimicking or enhancing natural communities, calling for the development of tools for designing synthetic communities with specific, tailored, desired metabolic capacities.'),
(79814, 2016, 'bio', 'Visual Omics Explorer (VOE): a cross-platform portal for interactive data visualization', 'Kim, Baekdoo|Ali, Thahmina|Hosmer, Samuel|Krampis, Konstantinos', 'Department of Biological Sciences Center for Translational and Basic Research and Belfer Research Building, Hunter College of the City University of New York, New York, NY, USA.|Department of Biological Sciences Center for Translational and Basic Research and Belfer Research Building, Hunter College of the City University of New York, New York, NY, USA.|The Graduate Center, City University of New York, New York, NY, USA.|Department of Biological Sciences Center for Translational and Basic Research and Belfer Research Building, Hunter College of the City University of New York, New York, NY, USA The Graduate Center, City University of New York, New York, NY, USA Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weil Cornell Medical College, Cornell University, New York, NY, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Epigenomics|Genomics|Humans|Internet|Metagenomics|Software|Transcriptome|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw119', '27153572', 'Given the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices.'),
(79815, 2016, 'bio', 'The spatiotemporal order of signaling events unveils the logic of development signaling', 'Zhu, Hao|Owen, Markus R|Mao, Yanlan', 'Bioinformatics Section, Southern Medical University, Guangzhou 510515, China.|School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK.|MRC Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK.', 'China|United Kingdom|United Kingdom', 1, 'Animals|Drosophila|Drosophila Proteins|Eye|Gene Expression Regulation, Developmental|Logic|Receptors, Notch|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw121', '27153573', 'Animals from worms and insects to birds and mammals show distinct body plans, however, the embryonic development of diverse body plans with tissues and organs within is controlled by a surprisingly few signaling pathways. It is well recognized that combinatorial use of and dynamic interactions among signaling pathways follow specific logic to control complex and accurate developmental signaling and patterning, but it remains elusive what such logic is, or even, what it looks like.'),
(79816, 2016, 'bio', 'SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data', 'Arnold, Christian|Bhat, Pooja|Zaugg, Judith B', 'European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany.|European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany.|European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany.', 'Germany|Germany|Germany', 2, 'Epigenomics|Genome|Humans|Polymorphism, Single Nucleotide|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btw127', '27153574', 'The vast majority of the many thousands of disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding part of the genome. They are likely to affect regulatory elements, such as enhancers and promoters, rather than the function of a protein. To understand the molecular mechanisms underlying genetic diseases, it is therefore increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin or transcription factor binding.'),
(79817, 2016, 'bio', 'mDCC_tools: characterizing multi-modal atomic motions in molecular dynamics trajectories', 'Kasahara, Kota|Mohan, Neetha|Fukuda, Ikuo|Nakamura, Haruki', 'Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.', 'Japan|Japan|Japan|Japan', 2, 'Bayes Theorem|Molecular Dynamics Simulation|Motion|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btw129', '27153575', 'We previously reported the multi-modal Dynamic Cross Correlation (mDCC) method for analyzing molecular dynamics trajectories. This method quantifies the correlation coefficients of atomic motions with complex multi-modal behaviors by using a Bayesian-based pattern recognition technique that can effectively capture transiently formed, unstable interactions. Here, we present an open source toolkit for performing the mDCC analysis, including pattern recognitions, complex network analyses and visualizations. We include a tutorial document that thoroughly explains how to apply this toolkit for an analysis, using the example trajectory of the 100xe2x80x89ns simulation of an engineered endothelin-1 peptide dimer.'),
(79818, 2016, 'bio', 'Pathway analysis by randomization incorporating structure-PARIS: an update', 'Butkiewicz, Mariusz|Cooke Bailey, Jessica N|Frase, Alex|Dudek, Scott|Yaspan, Brian L|Ritchie, Marylyn D|Pendergrass, Sarah A|Haines, Jonathan L', 'Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA.|Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA.|Department of Human Genetics, Genentech, Inc, South San Francisco, CA, USA.|Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA.|Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Databases, Factual|Genome-Wide Association Study|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw130', '27153576', 'We present an update to the pathway enrichment analysis tool Pathway Analysis by Randomization Incorporating Structure (PARIS) that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches.'),
(79819, 2016, 'bio', 'Analysis of CFSE time-series data using division-, age- and label-structured population models', 'Hross, Sabrina|Hasenauer, Jan', 'Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany Department of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Garching 85748, Germany.|Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany Department of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Garching 85748, Germany.', 'Germany|Germany', 0, 'Bayes Theorem|Cell Proliferation|Models, Theoretical|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btw131', '27153577', 'In vitro and in vivo cell proliferation is often studied using the dye carboxyfluorescein succinimidyl ester (CFSE). The CFSE time-series data provide information about the proliferation history of populations of cells. While the experimental procedures are well established and widely used, the analysis of CFSE time-series data is still challenging. Many available analysis tools do not account for cell age and employ optimization methods that are inefficient (or even unreliable).'),
(79820, 2016, 'bio', 'PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm', 'Zaidman, Daniel|Wolfson, Haim J', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel', 10, 'Algorithms|Computational Biology|Peptides|Proteins', 'Journal Article', '10.1093/bioinformatics/btw133', '27153578', 'Design of protein-protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5-15 amino acid long), are natural candidates for inhibition of protein-protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations.'),
(79821, 2016, 'bio', 'ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database', 'Yang, Ying|Jiang, Xiaotao|Chai, Benli|Ma, Liping|Li, Bing|Zhang, Anni|Cole, James R|Tiedje, James M|Zhang, Tong', 'Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China School of Marine Sciences, Sun Yat-Sen University, Guangzhou, China.|Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.', 'China|China|United States|China|China|China|United States|United States|China', 80, 'Anti-Bacterial Agents|Databases, Genetic|Drug Resistance, Microbial|Genes, Bacterial|Humans|Metagenomics|Molecular Sequence Annotation', 'Journal Article', '10.1093/bioinformatics/btw136', '27153579', 'Environmental dissemination of antibiotic resistance genes (ARGs) has become an increasing concern for public health. Metagenomics approaches can effectively detect broad profiles of ARGs in environmental samples, however, the detection and subsequent classification of ARG-like sequences are time consuming and have been severe obstacles in employing metagenomic methods. We sought to accelerate quantification of ARGs in metagenomic data from environmental samples.'),
(79822, 2016, 'bio', 'chromPlot: visualization of genomic data in chromosomal context', 'Oróstica, Karen Y|Verdugo, Ricardo A', 'Programa de Genética Humana ICBM, Facultad de Medicina, Universidad de Chile, Independencia 1027, Santiago, Chile.|Programa de Genética Humana ICBM, Facultad de Medicina, Universidad de Chile, Independencia 1027, Santiago, Chile.', 'Chile|Chile', 0, 'Animals|Chromosomes|Computer Graphics|Genetic Variation|Genome|Genomics|Humans|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btw137', '27153580', ': Visualizing genomic data in chromosomal context can help detecting errors in data processing and may suggest new hypotheses to be tested. Here, we report a new tool for displaying large and diverse genomic data along chromosomes. The software is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiple tracks of data. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, connected lines, colored segments, or histograms for representing data. chromPlot takes data from tables in data.frame in GRanges formats. The information necessary to draw chromosomes for mouse and human is included with the package. For other organisms, chromPlot can read Gap and cytoBandIdeo tables from the UCSC Genome Browser. We present common use cases here, and a full tutorial is included as the packages vignette.'),
(79823, 2016, 'bio', 'D3M: detection of differential distributions of methylation levels', 'Matsui, Yusuke|Mizuta, Masahiro|Ito, Satoshi|Miyano, Satoru|Shimamura, Teppei', 'Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan.|Information Initiative Center, Hokkaido University, Sapporo 060-0811, Japan.|Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.|Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.|Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan.', 'Japan|Japan|Japan|Japan|Japan', 1, 'Algorithms|DNA Methylation|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw138', '27153581', 'DNA methylation is an important epigenetic modification related to a variety of diseases including cancers. We focus on the methylation data from Illuminas Infinium HumanMethylation450 BeadChip. One of the key issues of methylation analysis is to detect the differential methylation sites between case and control groups. Previous approaches describe data with simple summary statistics or kernel function, and then use statistical tests to determine the difference. However, a summary statistics-based approach cannot capture complicated underlying structure, and a kernel function-based approach lacks interpretability of results.'),
(79824, 2016, 'bio', 'On genomic repeats and reproducibility', 'Firtina, Can|Alkan, Can', 'Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey.|Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey.', 'Turkey|Turkey', 14, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw139', '27153582', 'Here, we present a comprehensive analysis on the reproducibility of computational characterization of genomic variants using high throughput sequencing data. We reanalyzed the same datasets twice, using the same tools with the same parameters, where we only altered the order of reads in the input (i.e. FASTQ file). Reshuffling caused the reads from repetitive regions being mapped to different locations in the second alignment, and we observed similar results when we only applied a scatter/gather approach for read mapping-without prior shuffling. Our results show that, some of the most common variation discovery algorithms do not handle the ambiguous read mappings accurately when random locations are selected. In addition, we also observed that even when the exact same alignment is used, the GATK HaplotypeCaller generates slightly different call sets, which we pinpoint to the variant filtration step. We conclude that, algorithms at each step of genomic variation discovery and characterization need to treat ambiguous mappings in a deterministic fashion to ensure full replication of results.'),
(79825, 2016, 'bio', 'FRODOCK 20: fast protein-protein docking server', 'Ramírez-Aportela, Erney|López-Blanco, José Ramón|Chacón, Pablo', 'Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C, Madrid 28006, Spain Centro de Investigaciones Biológicas, CSIC, Madrid E-28040, Spain.|Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C, Madrid 28006, Spain.|Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C, Madrid 28006, Spain.', 'Spain|Spain|Spain', 33, 'Computers|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw141', '27153583', 'The prediction of protein-protein complexes from the structures of unbound components is a challenging and powerful strategy to decipher the mechanism of many essential biological processes. We present a user-friendly protein-protein docking server based on an improved version of FRODOCK that includes a complementary knowledge-based potential. The web interface provides a very effective tool to explore and select protein-protein models and interactively screen them against experimental distance constraints. The competitive success rates and efficiency achieved allow the retrieval of reliable potential protein-protein binding conformations that can be further refined with more computationally demanding strategies.'),
(79826, 2016, 'bio', 'LS-GKM: a new gkm-SVM for large-scale datasets', 'Lee, Dongwon', 'McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.', 'United States', 0, 'Chromatin Immunoprecipitation|DNA|Gene Regulatory Networks|Humans|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw142', '27153584', 'gkm-SVM is a sequence-based method for predicting and detecting the regulatory vocabulary encoded in functional DNA elements, and is a commonly used tool for studying gene regulatory mechanisms. Here we introduce new software, LS-GKM, which removes several limitations of our previous releases, enabling training on much larger scale (LS) datasets. LS-GKM also provides additional advanced gapped k-mer based kernel functions. With these improvements, LS-GKM achieves considerably higher accuracy than the original gkm-SVM.'),
(79827, 2016, 'bio', 'EBglmnet: a comprehensive R package for sparse generalized linear regression models', 'Huang, Anhui|Liu, Dianting', 'Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA.|Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw143', '27153585', 'EBglmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic net (EBEN) priors for generalized linear models. In our previous studies, both EBlasso and EBEN outperformed other state-of-the-art methods such as lasso and elastic net in inferring sparse genotype and phenotype associations, in which the number of covariates is typically much larger than the sample size. While high density genetic markers can be easily obtained nowadays in genetics and population analysis thanks to the advancements in molecular high throughput technologies, EBglmnet will be a very useful tool for statistical modeling in this area.'),
(79828, 2016, 'bio', 'MeCorS: Metagenome-enabled error correction of single cell sequencing reads', 'Bremges, Andreas|Singer, Esther|Woyke, Tanja|Sczyrba, Alexander', 'Center for Biotechnology and Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.|U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.|U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.|Center for Biotechnology and Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.', 'Germany|United States|United States|Germany', 6, 'Algorithms|Escherichia coli|Metagenome|Sequence Analysis, DNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw144', '27153586', 'We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies.'),
(79829, 2016, 'bio', 'jSplice: a high-performance method for accurate prediction of alternative splicing events and its application to large-scale renal cancer transcriptome data', 'Christinat, Yann|Pawxc5x82owski, Rafaxc5x82|Krek, Wilhelm', 'Institute of Molecular Health Sciences, ETH Zurich, Zurich 8093, Switzerland.|Institute of Molecular Health Sciences, ETH Zurich, Zurich 8093, Switzerland.|Institute of Molecular Health Sciences, ETH Zurich, Zurich 8093, Switzerland.', 'Switzerland|Switzerland|Switzerland', 5, 'Algorithms|Alternative Splicing|Humans|Kidney Neoplasms|Reproducibility of Results|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw145', '27153587', 'Alternative splicing represents a prime mechanism of post-transcriptional gene regulation whose misregulation is associated with a broad range of human diseases. Despite the vast availability of transcriptome data from different cell types and diseases, bioinformatics-based surveys of alternative splicing patterns remain a major challenge due to limited availability of analytical tools that combine high accuracy and rapidity.'),
(79830, 2016, 'bio', 'Ferret: a user-friendly Java tool to extract data from the 1000 Genomes Project', 'Limou, Sophie|Taverner, Andrew M|Winkler, Cheryl A', 'Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, NCI, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, MD 21702, USA.|Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, NCI, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, MD 21702, USA Quantitative and Computational Biology program, Princeton University, Princeton, NJ 08544, USA.|Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, NCI, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, MD 21702, USA.', 'United States|United States|United States', 5, 'Computational Biology|Exome|Gene Frequency|Genetics, Population|Genome, Human|Genomics|Haplotypes|Humans|INDEL Mutation|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw147', '27153588', 'The 1000 Genomes (1KG) Project provides a near-comprehensive resource on human genetic variation in worldwide reference populations. 1KG variants can be accessed through a browser and through the raw and annotated data that are regularly released on an ftp server. We developed Ferret, a user-friendly Java tool, to easily extract genetic variation information from these large and complex data files. From a locus, gene(s) or SNP(s) of interest, Ferret retrieves genotype data for 1KG SNPs and indels, and computes allelic frequencies for 1KG populations and optionally, for the Exome Sequencing Project populations. By converting the 1KG data into files that can be imported into popular pre-existing tools (e.g. PLINK and HaploView), Ferret offers a straightforward way, even for non-bioinformatics specialists, to manipulate, explore and merge 1KG data with the users dataset, as well as visualize linkage disequilibrium pattern, infer haplotypes and design tagSNPs.'),
(79831, 2016, 'bio', 'Inferring gene targets of drugs and chemical compounds from gene expression profiles', 'Noh, Heeju|Gunawan, Rudiyanto', 'Institute for Chemical and Bioengineering, Zurich, ETH Zurich, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Institute for Chemical and Bioengineering, Zurich, ETH Zurich, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|Switzerland', 5, 'Algorithms|Animals|Computational Biology|Drosophila|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Humans|Linear Models|Molecular Targeted Therapy|Saccharomyces cerevisiae|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw148', '27153589', 'Finding genes which are directly perturbed or targeted by drugs is of great interest and importance in drug discovery. Several network filtering methods have been created to predict the gene targets of drugs from gene expression data based on an ordinary differential equation model of the gene regulatory network (GRN). A critical step in these methods involves inferring the GRN from the expression data, which is a very challenging problem on its own. In addition, existing network filtering methods require computationally intensive parameter tuning or expression data from experiments with known genetic perturbations or both.'),
(79832, 2016, 'bio', 'chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts', 'Kehr, Birte|Melsted, Pxc3xa1ll', 'deCODE Genetics/Amgen, Reykjavxc3xadk, Iceland.|deCODE Genetics/Amgen, Reykjavxc3xadk, Iceland Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavxc3xadk, Iceland.', 'Iceland|Iceland', 0, 'Computational Biology|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw149', '27153590', 'Advances in sequencing capacity have led to the generation of unprecedented amounts of genomic data. The processing of this data frequently leads to I/O bottlenecks, e. g. when analyzing a small genomic region across a large number of samples. The largest I/O burden is, however, often not imposed by the amount of data needed for the analysis but rather by index files that help retrieving this data. We have developed chopBAI, a program that can chop a BAM index (BAI) file into small pieces. The program outputs a list of BAI files each indexing a specified genomic interval. The output files are much smaller in size but maintain compatibility with existing software tools. We show how preprocessing BAI files with chopBAI can lead to a reduction of I/O by more than 95% during the analysis of 10xe2x80x89kb genomic regions, eventually enabling the joint analysis of more than 10 000 individuals.'),
(79833, 2016, 'bio', 'DUDes: a top-down taxonomic profiler for metagenomics', 'Piro, Vitor C|Lindner, Martin S|Renard, Bernhard Y', 'Research Group Bioinformatics (NG4), Robert Koch Institute, Nordufer 20, Berlin 13353, Germany CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020 Brazil.|Research Group Bioinformatics (NG4), Robert Koch Institute, Nordufer 20, Berlin 13353, Germany 4-Antibody AG, Hochberger Strasse 60C, Basel 4057, Switzerland.|Research Group Bioinformatics (NG4), Robert Koch Institute, Nordufer 20, Berlin 13353, Germany.', 'Brazil|Switzerland|Germany', 19, 'Algorithms|Genome|High-Throughput Nucleotide Sequencing|Metagenomics', 'Journal Article', '10.1093/bioinformatics/btw150', '27153591', 'Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms.'),
(79834, 2016, 'bio', 'Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks', 'Blatti, Charles|Sinha, Saurabh', 'Department of Computer Science, University of Illinois, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois, Urbana, IL 61801, USA Institute of Genomic Biology, University of Illinois, Urbana, IL 61801, USA.', 'United States|United States', 18, 'Algorithms|Animals|Behavior, Animal|Computational Biology|Drosophila|Embryonic Development|Gene Regulatory Networks|Social Behavior', 'Journal Article', '10.1093/bioinformatics/btw151', '27153592', 'Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or properties such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks.'),
(79835, 2016, 'bio', 'Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences', 'Li, Heng', 'Medical Population Genetics, Broad Institute, Cambridge, MA 02142, USA.', 'United States', 264, 'Animals|Bacteria|Caenorhabditis elegans|Chromosome Mapping|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw152', '27153593', 'Single Molecule Real-Time (SMRT) sequencing technology and Oxford Nanopore technologies (ONT) produce reads over 10xe2x80x89kb in length, which have enabled high-quality genome assembly at an affordable cost. However, at present, long reads have an error rate as high as 10-15%. Complex and computationally intensive pipelines are required to assemble such reads.'),
(79836, 2016, 'bio', 'HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials', 'Mukherjee, Sumanta|Bhattacharyya, Chiranjib|Chandra, Nagasuma', 'IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India.|Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India.|Department of Biochemistry, Indian Institute of Science, Bangalore, India.', 'India|India|India', 5, 'Algorithms|Alleles|Amino Acid Sequence|Computational Biology|Epitopes, T-Lymphocyte|Humans|Peptides', 'Journal Article', '10.1093/bioinformatics/btw156', '27153594', 'T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction.'),
(79837, 2016, 'bio', 'MGEScan: a Galaxy-based system for identifying retrotransposons in genomes', 'Lee, Hyungro|Lee, Minsu|Mohammed Ismail, Wazim|Rho, Mina|Fox, Geoffrey C|Oh, Sangyoon|Tang, Haixu', 'School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|Department of Computer Science and Engineering, Hanyang University, Seoul, Korea.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|Department of Software Convergence Technology, Ajou University, Suwon, Korea.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.', 'India|United States|India|United States|India|United States|India', 10, 'Algorithms|Computational Biology|Genome|Programming Languages|Retroelements|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btw157', '27153595', ': MGEScan-long terminal repeat (LTR) and MGEScan-non-LTR are successfully used programs for identifying LTRs and non-LTR retrotransposons in eukaryotic genome sequences. However, these programs are not supported by easy-to-use interfaces nor well suited for data visualization in general data formats. Here, we present MGEScan, a user-friendly system that combines these two programs with a Galaxy workflow system accelerated with MPI and Python threading on compute clusters. MGEScan and Galaxy empower researchers to identify transposable elements in a graphical user interface with ready-to-use workflows. MGEScan also visualizes the custom annotation tracks for mobile genetic elements in public genome browsers. A maximum speed-up of 3.26× is attained for execution time using concurrent processing and MPI on four virtual cores. MGEScan provides four operational modes: as a command line tool, as a Galaxy Toolshed, on a Galaxy-based web server, and on a virtual cluster on the Amazon cloud.'),
(79838, 2016, 'bio', 'OxyBS: estimation of 5-methylcytosine and 5-hydroxymethylcytosine from tandem-treated oxidative bisulfite and bisulfite DNA', 'Houseman, E Andres|Johnson, Kevin C|Christensen, Brock C', 'School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA.|Department of Pharmacology and Toxicology Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|Department of Pharmacology and Toxicology Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.', 'United States|United States|United States', 15, '5-Methylcytosine|Cytosine|DNA|DNA Methylation|Humans|Likelihood Functions|Microarray Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw158', '27153596', 'The use of sodium bisulfite (BS) treatment followed by hybridization to an Illumina Infinium BeadChip (HumanMethylation450 and MethylationEPIC) is a common method for interrogating 5-methylcytosine (5mC) at single nucleotide resolution. However, standard treatment of DNA with BS does not allow disambiguation of 5mC from an additional cytosine modification, 5-hydroxymethylcytosine (5hmC). Recently, it has been demonstrated that paired BS and oxidative bisulfite (oxBS) treatment on the same sample followed by hybridization to an Infinium microarray permits the differentiation of 5hmC from 5mC. Nevertheless, estimation of 5hmC and 5mC from tandem-treated arrays has been shown to produce irregular estimates of cytosine modifications.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79839, 2016, 'bio', 'gEVALxe2x80x89-xe2x80x89a web-based browser for evaluating genome assemblies', 'Chow, William|Brugger, Kim|Caccamo, Mario|Sealy, Ian|Torrance, James|Howe, Kerstin', 'Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.|East Anglian Medical Genetics Centre, Cambridge University Hospitals, NHS Foundation Trust, Cambridge CB2 0QQ, UK.|National Institute of Agricultural Botany, Cambridge CB3 0LE, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Animals|Genome|Genomics|Humans|Internet|Mice|Sequence Alignment|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw159', '27153597', 'For most research approaches, genome analyses are dependent on the existence of a high quality genome reference assembly. However, the local accuracy of an assembly remains difficult to assess and improve. The gEVAL browser allows the user to interrogate an assembly in any region of the genome by comparing it to different datasets and evaluating the concordance. These analyses include: a wide variety of sequence alignments, comparative analyses of multiple genome assemblies, and consistency with optical and other physical maps. gEVAL highlights allelic variations, regions of low complexity, abnormal coverage, and potential sequence and assembly errors, and offers strategies for improvement. Although gEVAL focuses primarily on sequence integrity, it can also display arbitrary annotation including from Ensembl or TrackHub sources. We provide gEVAL web sites for many human, mouse, zebrafish and chicken assemblies to support the Genome Reference Consortium, and gEVAL is also downloadable to enable its use for any organism and assembly.'),
(79840, 2016, 'bio', 'FREGAT: an R package for region-based association analysis', 'Belonogova, Nadezhda M|Svishcheva, Gulnara R|Axenovich, Tatiana I', 'Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk Vavilov Institute of General Genetics, the Russian Academy of Sciences, Moscow, Russia.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk Novosibirsk State University, Novosibirsk, Russia.', '-|-|-', 9, 'Exome|Humans|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/btw160', '27153598', 'Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.'),
(79841, 2016, 'bio', 'HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data', 'Gu, Zuguang|Eils, Roland|Schlesner, Matthias', 'Division of Theoretical Bioinformatics Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Theoretical Bioinformatics Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany.|Division of Theoretical Bioinformatics.', 'Germany|Germany|-', 6, 'Computer Graphics|Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btw161', '27153599', ': Hilbert curves enable high-resolution visualization of genomic data on a chromosome- or genome-wide scale. Here we present the HilbertCurve package that provides an easy-to-use interface for mapping genomic data to Hilbert curves. The package transforms the curve as a virtual axis, thereby hiding the details of the curve construction from the user. HilbertCurve supports multiple-layer overlay that makes it a powerful tool to correlate the spatial distribution of multiple feature types.'),
(79842, 2016, 'bio', 'CellMiner Companion: an interactive web application to explore CellMiner NCI-60 data', 'Wang, Sufang|Gribskov, Michael|Hazbun, Tony R|Pascuzzi, Pete E', 'Department of Biological Sciences.|Department of Biological Sciences.|Department of Medicinal Chemistry and Molecular Pharmacology Purdue University Center for Cancer Research.|Purdue University Center for Cancer Research Department of Biochemistry Purdue University Libraries, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States|United States|United States', 5, 'Cell Line, Tumor|Humans|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw162', '27153600', 'The NCI-60 human tumor cell line panel is an invaluable resource for cancer researchers, providing drug sensitivity, molecular and phenotypic data for a range of cancer types. CellMiner is a web resource that provides tools for the acquisition and analysis of quality-controlled NCI-60 data. CellMiner supports queries of up to 150 drugs or genes, but the output is an Excel file for each drug or gene. This output format makes it difficult for researchers to explore the data from large queries. CellMiner Companion is a web application that facilitates the exploration and visualization of output from CellMiner, further increasing the accessibility of NCI-60 data.'),
(79843, 2016, 'bio', 'Canvas: versatile and scalable detection of copy number variants', 'Roller, Eric|Ivakhno, Sergii|Lee, Steve|Royce, Thomas|Tanner, Stephen', 'Illumina Inc, San Diego, CA 92122, USA.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Inc, San Diego, CA 92122, USA.|Ashion Analytics, Phoenix, AZ, USA.|Illumina Inc, San Diego, CA 92122, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|DNA Copy Number Variations|Exome|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw163', '27153601', 'Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples.'),
(79844, 2016, 'bio', 'Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis', 'Narushima, Yuta|Kozuka-Hata, Hiroko|Tsumoto, Kouhei|Inoue, Jun-Ichiro|Oyama, Masaaki', 'Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.|Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.|Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan.|Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan Department of Cancer Biology, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.|Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.', 'Japan|Japan|Japan|Japan|Japan', 10, 'Computational Biology|Glioblastoma|Humans|Neoplastic Stem Cells|Phosphoproteins|Phosphorylation|Phosphotransferases|Protein Interaction Mapping|Proteomics|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw164', '27153602', 'Phosphorylation-dependent cellular signaling is known to play a diverse role in regulating multiple cellular processes such as proliferation, differentiation and apoptosis. Recent technological advances in mass spectrometry-based phosphoproteomics have enabled us to measure network-wide signaling dynamics in a comprehensive and quantitative manner. As conventional protein-protein interaction (PPI) information-based network analysis is insufficient to systematically analyze phosphorylation site-dependent complex interaction dynamics, here we develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis.'),
(79845, 2016, 'bio', 'Deep models for brain EM image segmentation: novel insights and improved performance', 'Fakhry, Ahmed|Peng, Hanchuan|Ji, Shuiwang', 'Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.|Allen Institute for Brain Science, Seattle, WA 98103, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.', 'United States|United States|United States', 27, 'Algorithms|Brain|Microscopy, Electron|Models, Theoretical|Neural Networks (Computer)', 'Journal Article', '10.1093/bioinformatics/btw165', '27153603', 'Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.'),
(79846, 2016, 'bio', 'Genome-scale prediction of moonlighting proteins using diverse protein association information', 'Khan, Ishita K|Kihara, Daisuke', 'Department of Computer Science.|Department of Computer Science Department of Biological Science, Purdue University, West Lafayette, IN, USA.', 'United States|United States', 0, 'Computational Biology|Genome|Humans|Machine Learning|Phylogeny|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/btw166', '27153604', 'Moonlighting proteins (MPs) show multiple cellular functions within a single polypeptide chain. To understand the overall landscape of their functional diversity, it is important to establish a computational method that can identify MPs on a genome scale. Previously, we have systematically characterized MPs using functional and omics-scale information. In this work, we develop a computational prediction model for automatic identification of MPs using a diverse range of protein association information.'),
(79847, 2016, 'bio', 'pileupjs: a JavaScript library for interactive and in-browser visualization of genomic data', 'Vanderkam, Dan|Aksoy, B Arman|Hodes, Isaac|Perrone, Jaclyn|Hammerbacher, Jeff', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.', 'United States|United States|United States|United States|United States', 0, 'Computer Graphics|Genome|Genomics|Internet|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw167', '27153605', 'P: ileup.js is a new browser-based genome viewer. It is designed to facilitate the investigation of evidence for genomic variants within larger web applications. It takes advantage of recent developments in the JavaScript ecosystem to provide a modular, reliable and easily embedded library.'),
(79848, 2016, 'bio', 'Drug-induced adverse events prediction with the LINCS L1000 data', 'Wang, Zichen|Clark, Neil R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.', 'United States|United States|United States', 47, 'Drug-Related Side Effects and Adverse Reactions|Gene Expression|Gene Library|Humans', 'Journal Article', '10.1093/bioinformatics/btw168', '27153606', 'Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all xe2x80x89>20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections.'),
(79849, 2016, 'bio', 'GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets', 'Dozmorov, Mikhail G|Cara, Lukas R|Giles, Cory B|Wren, Jonathan D', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.|Department of Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.|Department of Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.', 'United States|United States|United States|United States', 0, 'Automation|Computers|Databases, Genetic|Genomics|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw169', '27153607', 'The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets.'),
(79850, 2016, 'bio', 'ProbOnto: ontology and knowledge base of probability distributions', 'Swat, Maciej J|Grenon, Pierre|Wimalaratne, Sarala', 'EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.|CHIME, University College London, London, UK.|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Knowledge Bases|Probability', 'Journal Article', '10.1093/bioinformatics/btw170', '27153608', 'Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access.'),
(79851, 2016, 'bio', 'Identifying cell-specific microRNA transcriptional start sites', 'Hua, Xu|Chen, Luxiao|Wang, Jin|Li, Jie|Wingender, Edgar', 'The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China Department of Bioinformatics, Medical School, George August University of Gxc3xb6ttingen, Goldschmidtstrasse 1, Gxc3xb6ttingen D-37077, Germany.|The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China.|The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China.|The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China Department of Bioinformatics, Medical School, George August University of Gxc3xb6ttingen, Goldschmidtstrasse 1, Gxc3xb6ttingen D-37077, Germany.|Department of Bioinformatics, Medical School, George August University of Gxc3xb6ttingen, Goldschmidtstrasse 1, Gxc3xb6ttingen D-37077, Germany.', 'China|China|China|China|Germany', 10, 'Gene Expression Regulation|High-Throughput Nucleotide Sequencing|MicroRNAs|Promoter Regions, Genetic|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btw171', '27153609', 'Identification of microRNA (miRNA) transcriptional start sites (TSSs) is crucial to understand the transcriptional regulation of miRNA. As miRNA expression is highly cell specific, an automatic and systematic method that could identify miRNA TSSs accurately and cell specifically is in urgent requirement.'),
(79852, 2016, 'bio', 'CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets', 'Schofield, E C|Carver, T|Achuthan, P|Freire-Pritchett, P|Spivakov, M|Todd, J A|Burren, O S', 'JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.|JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.|JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.|Nuclear Dynamics Programme, The Babraham Institute, Cambridge CB22 3AT, UK.|Nuclear Dynamics Programme, The Babraham Institute, Cambridge CB22 3AT, UK.|JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.|JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chromosomes|Computer Graphics|Genome|Genomics|Internet|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Software', 'Journal Article', '10.1093/bioinformatics/btw173', '27153610', 'Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets.'),
(79853, 2016, 'bio', 'Drseq: a quality control and analysis pipeline for droplet sequencing', 'Huo, Xiao|Hu, Shengen|Zhao, Chengchen|Zhang, Yong', 'School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China.|School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China.|School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China.|School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China.', 'China|China|China|China', 2, 'Cluster Analysis|High-Throughput Nucleotide Sequencing|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btw174', '27153611', 'Drop-seq has recently emerged as a powerful technology to analyze gene expression from thousands of individual cells simultaneously. Currently, Drop-seq technology requires refinement and quality control (QC) steps are critical for such data analysis. There is a strong need for a convenient and comprehensive approach to obtain dedicated QC and to determine the relationships between cells for ultra-high-dimensional datasets.'),
(79854, 2016, 'bio', 'ProbFold: a probabilistic method for integration of probing data in RNA secondary structure prediction', 'Sahoo, Sudhakar|xc5x9awitnicki, Michaxc5x82 P|Pedersen, Jakob Skou', 'Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus N 8200, Denmark.|Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus N 8200, Denmark.|Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus N 8200, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus C DK-8000, Denmark.', 'Denmark|Denmark|Denmark', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Models, Statistical|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1093/bioinformatics/btw175', '27153612', 'Recently, new RNA secondary structure probing techniques have been developed, including Next Generation Sequencing based methods capable of probing transcriptome-wide. These techniques hold great promise for improving structure prediction accuracy. However, each new data type comes with its own signal properties and biases, which may even be experiment specific. There is therefore a growing need for RNA structure prediction methods that can be automatically trained on new data types and readily extended to integrate and fully exploit multiple types of data.'),
(79855, 2016, 'bio', 'Quality control of single-cell RNA-seq by SinQC', 'Jiang, Peng|Thomson, James A|Stewart, Ron', 'Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA.|Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA Department of Cell and Regenerative Biology, University of Wisconsin, Madison, WI 53706, USA Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA 93106, USA.|Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA.', 'United States|United States|United States', 20, 'Gene Expression Profiling|Quality Control|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw176', '27153613', 'Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for profiling cell-to-cell variability in cell populations. However, the combination of technical noise and intrinsic biological variability makes detecting technical artifacts in scRNA-seq samples particularly challenging. Proper detection of technical artifacts is critical to prevent spurious results during downstream analysis. In this study, we present Single-cell RNA-seq Quality Control (SinQC), a method and software tool to detect technical artifacts in scRNA-seq samples by integrating both gene expression patterns and data quality information. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality.'),
(79856, 2016, 'bio', 'Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce', 'Nellore, Abhinav|Wilks, Christopher|Hansen, Kasper D|Leek, Jeffrey T|Langmead, Ben', 'Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|RNA|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btw177', '27153614', 'Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP. To analyse dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data.'),
(79857, 2016, 'bio', 'Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction', 'Arbabi, Aryan|Rampxc3xa1xc5xa1ek, Ladislav|Brudno, Michael', 'Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada and.|Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.|Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada and Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.', 'Canada|Canada|Canada', 0, 'Aneuploidy|DNA|DNA Copy Number Variations|Humans|Polymorphism, Single Nucleotide|Prenatal Diagnosis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw178', '27153615', 'Non-invasive detection of aneuploidies in a fetal genome through analysis of cell-free DNA circulating in the maternal plasma is becoming a routine clinical test. Such tests, which rely on analyzing the read coverage or the allelic ratios at single-nucleotide polymorphism (SNP) loci, are not sensitive enough for smaller sub-chromosomal abnormalities due to sequencing biases and paucity of SNPs in a genome.'),
(79858, 2016, 'bio', 'SBtab: a flexible table format for data exchange in systems biology', 'Lubitz, Timo|Hahn, Jens|Bergmann, Frank T|Noor, Elad|Klipp, Edda|Liebermeister, Wolfram', 'Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.|Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.|COS/Bioquant, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.|Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland.|Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.|Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Switzerland|Germany|Germany', 16, 'Databases, Factual|Models, Biological|Models, Theoretical|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw179', '27153616', ': SBtab is a table-based data format for Systems Biology, designed to support automated data integration and model building. It uses the structure of spreadsheets and defines conventions for table structure, controlled vocabularies and semantic annotations. The format comes with predefined table types for experimental data and SBML-compliant model structures and can easily be customized to cover new types of data.'),
(79859, 2016, 'bio', 'methyLiftover: cross-platform DNA methylation data integration', 'Titus, Alexander J|Houseman, E Andrés|Johnson, Kevin C|Christensen, Brock C', 'Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.|Department of Biostatistics, Oregon State University College of Public Health and Human Sciences, Corvallis, OR, USA.|Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.|Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.', 'Lebanon|United States|Lebanon|Lebanon', 0, 'CpG Islands|DNA Methylation|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw180', '27153617', ': The public availability of high throughput molecular data provides new opportunities for researchers to advance discovery, replication and validation efforts. One common challenge in leveraging such data is the diversity of measurement approaches and platforms and a lack of utilities enabling cross-platform comparisons among data sources for analysis. We present a method to map DNA methylation data from bisulfite sequencing approaches to CpG sites measured with the widely used Illumina methylation bead-array platforms. Correlations and median absolute deviations support the validity of using bisulfite sequencing data in combination with Illumina bead-array methylation data.'),
(79860, 2016, 'bio', 'R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter', 'Yang, Jing|Jin, Qi-Yu|Zhang, Biao|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.', 'China|China|China|China', 0, 'Algorithms|Computational Biology|Machine Learning|Mutation|Noise|Normal Distribution|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btw181', '27153618', 'Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly.'),
(79861, 2016, 'bio', 'ProtPOS: a python package for the prediction of protein preferred orientation on a surface', 'Ngai, Jimmy C F|Mak, Pui-In|Siu, Shirley W I', 'Department of Computer and Information Science.|State-Key Laboratory of Analog and Mixed-Signal VLSI, Faculty of Science and Technology, University of Macau (UM), Avenida da Universidade, Taipa, Macau, China.|Department of Computer and Information Science.', '-|China|-', 3, 'Algorithms|Molecular Dynamics Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw182', '27153619', 'Atomistic molecular dynamics simulation is a promising technique to investigate the energetics and dynamics in the protein-surface adsorption process which is of high relevance to modern biotechnological applications. To increase the chance of success in simulating the adsorption process, favorable orientations of the protein at the surface must be determined. Here, we present ProtPOS which is a lightweight and easy-to-use python package that can predict low-energy protein orientations on a surface of interest. It combines a fast conformational sampling algorithm with the energy calculation of GROMACS. The advantage of ProtPOS is it allows users to select any force fields suitable for the system at hand and provide structural output readily available for further simulation studies.'),
(79862, 2016, 'bio', 'MOCAT2: a metagenomic assembly, annotation and profiling framework', 'Kultima, Jens Roat|Coelho, Luis Pedro|Forslund, Kristoffer|Huerta-Cepas, Jaime|Li, Simone S|Driessen, Marja|Voigt, Anita Yvonne|Zeller, Georg|Sunagawa, Shinichi|Bork, Peer', 'Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany School of Biotechnology and Biomolecular Sciences, University of New South Wales, 2052 Sydney, Australia.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Australia|Germany|Germany|Germany|Germany|Germany', 66, 'Databases, Factual|Metagenome|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btw183', '27153620', 'MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes.'),
(79863, 2016, 'bio', 'Memdock: an xcexb1-helical membrane protein docking algorithm', 'Hurwitz, Naama|Schneidman-Duhovny, Dina|Wolfson, Haim J', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Department of Bioengineering and Therapeutic Sciences and Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'Israel|United States|Israel', 0, 'Algorithms|Databases, Protein|Membrane Proteins|Models, Molecular', 'Journal Article', '10.1093/bioinformatics/btw184', '27153621', 'A wide range of fundamental biological processes are mediated by membrane proteins. Despite their large number and importance, less than 1% of all 3D protein structures deposited in the Protein Data Bank are of membrane proteins. This is mainly due to the challenges of crystallizing such proteins or performing NMR spectroscopy analyses. All the more so, there is only a small number of membrane protein-protein complexes with known structure. Therefore, developing computational tools for docking membrane proteins is crucial. Numerous methods for docking globular proteins exist, however few have been developed especially for membrane proteins and designed to address docking within the lipid bilayer environment.'),
(79864, 2016, 'bio', 'PREDAC-H3: a user-friendly platform for antigenic surveillance of human influenza a(H3N2) virus based on hemagglutinin sequences', 'Peng, Yousong|Yang, Lei|Li, Honglei|Zou, Yuanqiang|Deng, Lizong|Wu, Aiping|Du, Xiangjun|Wang, Dayan|Shu, Yuelong|Jiang, Taijiao', 'College of Biology, Hunan University, Changsha, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China.|Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.|National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.|National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.|College of Biology, Hunan University, Changsha, China Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China.', 'China|China|China|China|China|China|United States|China|China|China', 0, 'Antigenic Variation|Antigens, Viral|Computational Biology|DNA, Viral|Epidemiological Monitoring|Hemagglutinin Glycoproteins, Influenza Virus|Hemagglutinins|Humans|Influenza A Virus, H3N2 Subtype|Influenza A virus|Influenza, Human|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw185', '27153622', 'Timely surveillance of the antigenic dynamics of the influenza virus is critical for accurate selection of vaccine strains, which is important for effective prevention of viral spread and infection.'),
(79865, 2016, 'bio', 'iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework', 'Liu, Bin|Long, Ren|Chou, Kuo-Chen', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China Gordon Life Science Institute, Belmont, MA 02478, USA.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|Gordon Life Science Institute, Belmont, MA 02478, USA Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.', 'China|China|Saudi Arabia', 0, 'Algorithms|DNA|Deoxyribonuclease I|Genomics|High-Throughput Nucleotide Sequencing|Humans|Nucleotides|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw186', '27153623', 'Regulatory DNA elements are associated with DNase I hypersensitive sites (DHSs). Accordingly, identification of DHSs will provide useful insights for in-depth investigation into the function of noncoding genomic regions.'),
(79866, 2016, 'bio', 'DyNet: visualization and analysis of dynamic molecular interaction networks', 'Goenawan, Ivan H|Bryan, Kenneth|Lynn, David J', 'EMBL Australia Biomedical Informatics Group, Infection and Immunity Theme, South Australian Medical and Health Research Institute, North Terrace, Adelaide, South Australia 5000, Australia.|EMBL Australia Biomedical Informatics Group, Infection and Immunity Theme, South Australian Medical and Health Research Institute, North Terrace, Adelaide, South Australia 5000, Australia.|EMBL Australia Biomedical Informatics Group, Infection and Immunity Theme, South Australian Medical and Health Research Institute, North Terrace, Adelaide, South Australia 5000, Australia School of Medicine, Flinders University, Bedford Park, South Australia 5042, Australia.', 'Australia|Australia|Australia', 0, 'Computational Biology|Gene Regulatory Networks|Genomics|Humans|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btw187', '27153624', ': The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most rewired nodes across many network states.'),
(79867, 2016, 'bio', 'Efficient identification and analysis of chemical exchange in biomolecules by R1xcfx81 relaxation dispersion with Amaterasu', 'Walinda, Erik|Morimoto, Daichi|Nishizawa, Mayu|Shirakawa, Masahiro|Sugase, Kenji', 'Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto, 615-8510, Japan.|Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto, 615-8510, Japan.|Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto, 615-8510, Japan.|Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto, 615-8510, Japan.|Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto, 615-8510, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Computer Simulation|Magnetic Resonance Spectroscopy|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw188', '27153625', 'We introduce here a novel acquisition and processing methodology for cross-polarization based 1D rotating-frame relaxation dispersion NMR experiments. This easy-to-use protocol greatly facilitates the screening, acquisition, processing and model fitting of large on- and off-resonance R1xcfx81 relaxation dispersion NMR datasets in an automated manner for the analysis of chemical exchange phenomena in biomolecules.'),
(79868, 2016, 'bio', 'Tools4miRs - one place to gather all the tools for miRNA analysis', 'Lukasik, Anna|Wójcikowski, Maciej|Zielenkiewicz, Piotr', 'Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, 02-096 Warsaw, Poland.', 'Poland|Poland|Poland', 26, 'Computational Biology|MicroRNAs|Models, Molecular|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw189', '27153626', 'MiRNAs are short, non-coding molecules that negatively regulate gene expression and thereby play several important roles in living organisms. Dozens of computational methods for miRNA-related research have been developed, which greatly differ in various aspects. The substantial availability of difficult-to-compare approaches makes it challenging for the user to select a proper tool and prompts the need for a solution that will collect and categorize all the methods. Here, we present tools4miRs, the first platform that gathers currently more than 160 methods for broadly defined miRNA analysis. The collected tools are classified into several general and more detailed categories in which the users can additionally filter the available methods according to their specific research needs, capabilities and preferences. Tools4miRs is also a web-based target prediction meta-server that incorporates user-designated target prediction methods into the analysis of user-provided data.'),
(79869, 2016, 'bio', 'MEMO: multi-experiment mixture model analysis of censored data', 'Geissen, Eva-Maria|Hasenauer, Jan|Heinrich, Stephanie|Hauf, Silke|Theis, Fabian J|Radde, Nicole E', 'Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70550, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Mathematics, Technische Universität München, Garching 85748, Germany.|Friedrich Miescher Laboratory of the Max Planck Society, Tübingen 72076, Germany.|Friedrich Miescher Laboratory of the Max Planck Society, Tübingen 72076, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Mathematics, Technische Universität München, Garching 85748, Germany.|Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70550, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 4, 'Computational Biology|Humans|Models, Statistical|Probability', 'Journal Article', '10.1093/bioinformatics/btw190', '27153627', 'The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79870, 2016, 'bio', 'flowAI: automatic and interactive anomaly discerning tools for flow cytometry data', 'Monaco, Gianni|Chen, Hao|Poidinger, Michael|Chen, Jinmiao|de Magalhães, João Pedro|Larbi, Anis', 'Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.|Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore.|Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore.|Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore.|Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.|Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore.', 'Singapore|Singapore|Singapore|Singapore|United Kingdom|Singapore', 27, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Flow Cytometry|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw191', '27153628', 'Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.'),
(79871, 2016, 'bio', 'INPS-MD: a web server to predict stability of protein variants from sequence and structure', 'Savojardo, Castrense|Fariselli, Piero|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, CIG, Interdepartmental Center xc2xabLuigi Galvanixc2xbb for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Italy.|BCA, University of Padova, Italy.|Biocomputing Group, CIG, Interdepartmental Center xc2xabLuigi Galvanixc2xbb for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Italy.|Biocomputing Group, CIG, Interdepartmental Center xc2xabLuigi Galvanixc2xbb for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Italy Interdepartmental Center xc2xabGiorgio Prodixc2xbb for Cancer Research, University of Bologna, Italy.', 'Italy|Italy|Italy|Italy', 41, 'Amino Acid Sequence|Computers|Humans|Protein Stability|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw192', '27153629', 'Protein function depends on its structural stability. The effects of single point variations on protein stability can elucidate the molecular mechanisms of human diseases and help in developing new drugs. Recently, we introduced INPS, a method suited to predict the effect of variations on protein stability from protein sequence and whose performance is competitive with the available state-of-the-art tools.'),
(79872, 2016, 'bio', 'TOPDOM: database of conservatively located domains and motifs in proteins', 'Varga, Julia|Dobson, Lxc3xa1szlxc3xb3|Tusnxc3xa1dy, Gxc3xa1bor E', 'Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, HAS, Budapest H-1518, Hungary.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, HAS, Budapest H-1518, Hungary.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, HAS, Budapest H-1518, Hungary.', 'Hungary|Hungary|Hungary', 0, 'Algorithms|Amino Acid Motifs|Conserved Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Membrane Proteins|Protein Domains|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw193', '27153630', 'The TOPDOM database-originally created as a collection of domains and motifs located consistently on the same side of the membranes in xcexb1-helical transmembrane proteins-has been updated and extended by taking into consideration consistently localized domains and motifs in globular proteins, too. By taking advantage of the recently developed CCTOP algorithm to determine the type of a protein and predict topology in case of transmembrane proteins, and by applying a thorough search for domains and motifs as well as utilizing the most up-to-date version of all source databases, we managed to reach a 6-fold increase in the size of the whole database and a 2-fold increase in the number of transmembrane proteins.'),
(79873, 2016, 'bio', 'MINTbase: a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments', 'Pliatsika, Venetia|Loher, Phillipe|Telonis, Aristeidis G|Rigoutsos, Isidore', 'Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA.', 'United States|United States|United States|United States', 28, 'Anticodon|Base Sequence|Humans|Internet|Mitochondria|RNA, Transfer|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw194', '27153631', 'It has been known that mature transfer RNAs (tRNAs) that are encoded in the nuclear genome give rise to short molecules, collectively known as tRNA fragments or tRFs. Recently, we reported that, in healthy individuals and in patients, tRFs are constitutive, arise from mitochondrial as well as from nuclear tRNAs, and have composition and abundances that depend on a persons sex, population origin and race as well as on tissue, disease and disease subtype. Our findings as well as similar work by other groups highlight the importance of tRFs and presage an increase in the communitys interest in elucidating the roles of tRFs in health and disease.'),
(79874, 2016, 'bio', 'hotspot: software to support sperm-typing for investigating recombination hotspots', 'Odenthal-Hesse, Linda|Dutheil, Julien Y|Klötzl, Fabian|Haubold, Bernhard', 'Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Alleles|Humans|Likelihood Functions|Male|Recombination, Genetic|Software|Spermatozoa', 'Journal Article', '10.1093/bioinformatics/btw195', '27153632', 'In many organisms, including humans, recombination clusters within recombination hotspots. The standard method for de novo detection of recombinants at hotspots is sperm typing. This relies on allele-specific PCR at single nucleotide polymorphisms. Designing allele-specific primers by hand is time-consuming. We have therefore written a package to support hotspot detection and analysis.'),
(79875, 2016, 'bio', 'Muxstep: an open-source Cxe2x80x89++ multiplex HMM library for making inferences on multiple data types', 'Velixc4x8dkovixc4x87, Petar|Lixc3xb2, Pietro', 'Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.|Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Machine Learning|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw196', '27153633', 'With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions, in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng,nsio with nontrivial topological features. As the first step in establishing such a trend, we present MUXSTEP: , an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described.'),
(79876, 2016, 'bio', 'Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking', 'Kuroda, Daisuke|Gray, Jeffrey J', 'Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA, Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo, 142-8555, Japan.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA, Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.', 'Japan|United States', 0, 'Algorithms|Antibodies|Computational Biology|Hydrogen|Hydrogen Bonding|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btw197', '27153634', 'Characterizing protein-protein interfaces and the hydrogen bonds is a first step to better understand proteins structures and functions toward high-resolution protein design. However, there are few large-scale surveys of hydrogen bonds of interfaces. In addition, previous work of shape complementarity of protein complexes suggested that lower shape complementarity in antibody-antigen interfaces is related to their evolutionary origin.'),
(79877, 2016, 'bio', 'TreeQTL: hierarchical error control for eQTL findings', 'Peterson, C B|Bogomolov, M|Benjamini, Y|Sabatti, C', 'Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA.|Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel.|Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 6997801, Israel.|Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, CA 94305, USA.', 'United States|Israel|Israel|United States', 13, 'Humans|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btw198', '27153635', ': Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative to a grouping of the eQTL hypotheses.'),
(79878, 2016, 'bio', 'Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins', 'Ziegler, Zachary|Schmidt, Molly|Gurry, Thomas|Burger, Virginia|Stultz, Collin M', 'Cornell University, Ithaca, NY 14850, USA Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA The Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.', 'United States|United States|United States|United States|United States', 0, 'Computers|Internet|Intrinsically Disordered Proteins|Protein Conformation', 'Journal Article', '10.1093/bioinformatics/btw200', '27153636', 'Intrinsically disordered proteins (IDPs) play central roles in many biological processes. Consequently, an accurate description of the disordered state is an important step towards a comprehensive understanding of a number of important biological functions. In this work we describe a new web server, Mollack, for the automated construction of unfolded ensembles that uses both experimental and molecular simulation data to construct models for the unfolded state. An important aspect of the method is that it calculates a quantitative estimate of the uncertainty in the constructed ensemble, thereby providing an objective measure of the quality of the final model. Overall, Mollack facilitates structure-function studies of disordered proteins.'),
(79879, 2016, 'bio', 'SCell: integrated analysis of single-cell RNA-seq data', 'Diaz, Aaron|Liu, Siyuan J|Sandoval, Carmen|Pollen, Alex|Nowakowski, Tom J|Lim, Daniel A|Kriegstein, Arnold', 'Department of Neurological Surgery, UCSF Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Department of Neurological Surgery, UCSF Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.|Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.', '-|-|-|-|-|-|-', 0, 'High-Throughput Nucleotide Sequencing|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw201', '27153637', 'Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. We present SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. Scripts and protocols for the high-throughput pre-processing of large ensembles of single-cell, RNA-seq datasets are provided as an additional resource.'),
(79880, 2016, 'bio', 'Beta-Poisson model for single-cell RNA-seq data analyses', 'Vu, Trung Nghia|Wills, Quin F|Kalari, Krishna R|Niu, Nifang|Wang, Liewei|Rantalainen, Mattias|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK.|Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.', 'Sweden|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Sweden|Sweden', 39, 'Computational Biology|Gene Expression Profiling|Models, Theoretical|RNA|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btw202', '27153638', 'Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property.'),
(79881, 2016, 'bio', 'gkmSVM: an R package for gapped-kmer SVM', 'Ghandi, Mahmoud|Mohammad-Noori, Morteza|Ghareghani, Narges|Lee, Dongwon|Garraway, Levi|Beer, Michael A', 'The Broad Institute of MIT and Harvard, Cambridge, MA, USA.|School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran.|Department of Engineering Science, College of Engineering, University of Tehran, and Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.|The Broad Institute of MIT and Harvard, Cambridge, MA, USA Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.|McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States|United States', 35, 'Algorithms|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw203', '27153639', 'We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our original gkmSVM algorithm. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel.'),
(79882, 2016, 'bio', 'e23D: database and visualization of A-to-I RNA editing sites mapped to 3D protein structures', 'Solomon, Oz|Eyal, Eran|Amariglio, Ninette|Unger, Ron|Rechavi, Gidi', 'Cancer Research Center, Sheba Medical Center, Ramat-Gan, Israel The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.|Cancer Research Center, Sheba Medical Center, Ramat-Gan, Israel.|Cancer Research Center, Sheba Medical Center, Ramat-Gan, Israel The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.|The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.|Cancer Research Center, Sheba Medical Center, Ramat-Gan, Israel Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Animals|Databases, Protein|Drosophila|Genomics|Humans|Mice|Protein Structure, Tertiary|Proteins|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btw204', '27153640', 'e23D, a database of A-to-I RNA editing sites from human, mouse and fly mapped to evolutionary related protein 3D structures, is presented. Genomic coordinates of A-to-I RNA editing sites are converted to protein coordinates and mapped onto 3D structures from PDB or theoretical models from ModBase. e23D allows visualization of the protein structure, modeling of recoding events and orientation of the editing with respect to nearby genomic functional sites from databases of disease causing mutations and genomic polymorphism.'),
(79883, 2016, 'bio', 'SAMFIRE: multi-locus variant calling for time-resolved sequence data', 'Illingworth, C J R', 'Department of Genetics, University of Cambridge, Cambridge CB2 3AS, UK.', 'United Kingdom', 5, 'Alleles|Genomics|Haplotypes|Humans|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw205', '27153641', 'An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natural microbial populations and in evolutionary experiments. In many circumstances, evolutionary pressure acting upon single variants can cause genomic changes at multiple nearby loci. SAMFIRE is an open-access software package for processing and analyzing sequence reads from time-resolved data, calling important single- and multi-locus variants over time, identifying alleles potentially affected by selection, calculating linkage disequilibrium statistics, performing haplotype reconstruction and exploiting time-resolved information to estimate the extent of uncertainty in reported genomic data.'),
(79884, 2016, 'bio', 'Pasha: a versatile R package for piling chromatin HTS data', 'Fenouil, Romain|Descostes, Nicolas|Spinelli, Lionel|Koch, Frederic|Maqbool, Muhammad Ahmad|Benoukraf, Touati|Cauchy, Pierre|Innocenti, Charlène|Ferrier, Pierre|Andrau, Jean-Christophe', 'Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, New York University Langone School of Medicine, New York, NY, USA.|Centre DImmunologie De Marseille-Luminy, Aix Marseille Université UM2, Inserm, U1104, CNRS UMR7280, 13288 Marseille, France.|Department of Developmental Genetics, Max Planck Institute for Molecular Genetics, Berlin 14195, Germany.|Institute of Molecular Genetics of Montpellier (IGMM), UMR5535 CNRS, 34293 Montpellier, France.|Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.|Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK.|CHU Montpellier, INSERM U1203, Institute of Regenerative Medicine and Biotherapy, Montpellier, France.|Centre DImmunologie De Marseille-Luminy, Aix Marseille Université UM2, Inserm, U1104, CNRS UMR7280, 13288 Marseille, France.|Institute of Molecular Genetics of Montpellier (IGMM), UMR5535 CNRS, 34293 Montpellier, France.', 'United Kingdom|United Kingdom|France|Germany|France|Singapore|United Kingdom|France|France|France', 8, 'Chromatin|High-Throughput Nucleotide Sequencing|Nucleosomes|Software', 'Journal Article', '10.1093/bioinformatics/btw206', '27153642', 'We describe an R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha (preprocessing of aligned sequences from HTS analyses) allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-Seq, …) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information.'),
(79885, 2016, 'bio', 'Sparse group factor analysis for biclustering of multiple data sources', 'Bunte, Kerstin|Leppäaho, Eemeli|Saarinen, Inka|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland.', 'Finland|Finland|Finland|Finland', 13, 'Algorithms|Bayes Theorem|Cluster Analysis|Datasets as Topic|Factor Analysis, Statistical|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw207', '27153643', 'Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expression data. We present a Bayesian approach for joint biclustering of multiple data sources, extending a recent method Group Factor Analysis to have a biclustering interpretation with additional sparsity assumptions. The resulting method enables data-driven detection of linear structure present in parts of the data sources.'),
(79886, 2016, 'bio', 'OREMPRO web server: orientation and assessment of atomistic and coarse-grained structures of membrane proteins', 'Postic, Guillaume|Ghouzam, Yassine|Gelly, Jean-Christophe', 'Inserm U1134, Paris, France Univ. Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France Institut National De La Transfusion Sanguine, Paris, France Laboratory of Excellence GR-Ex, Paris, France.|Inserm U1134, Paris, France Univ. Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France Institut National De La Transfusion Sanguine, Paris, France Laboratory of Excellence GR-Ex, Paris, France.|Inserm U1134, Paris, France Univ. Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France Institut National De La Transfusion Sanguine, Paris, France Laboratory of Excellence GR-Ex, Paris, France.', 'France|France|France', 7, 'Computers|Internet|Membrane Proteins|Models, Molecular|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw208', '27153644', ': The experimental determination of membrane protein orientation within the lipid bilayer is extremely challenging, such that computational methods are most often the only solution. Moreover, obtaining all-atom 3D structures of membrane proteins is also technically difficult, and many of the available data are either experimental low-resolution structures or theoretical models, whose structural quality needs to be evaluated. Here, to address these two crucial problems, we propose OREMPRO, a web server capable of both (i) positioning xcexb1-helical and xcexb2-sheet transmembrane domains in the lipid bilayer and (ii) assessing their structural quality. Most importantly, OREMPRO uses the sole alpha carbon coordinates, which makes it the only web server compatible with both high and low structural resolutions. Finally, OREMPRO is also interesting in its ability to process coarse-grained protein models, by using coordinates of backbone beads in place of alpha carbons.'),
(79887, 2016, 'bio', 'Romulus: robust multi-state identification of transcription factor binding sites from DNase-seq data', 'Jankowski, Aleksander|Tiuryn, Jerzy|Prabhakar, Shyam', 'Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warszawa, Poland Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.|Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warszawa, Poland.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.', 'Poland|Poland|Singapore', 10, 'Binding Sites|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw209', '27153645', 'Computational prediction of transcription factor (TF) binding sites in the genome remains a challenging task. Here, we present Romulus, a novel computational method for identifying individual TF binding sites from genome sequence information and cell-type-specific experimental data, such as DNase-seq. It combines the strengths of previous approaches, and improves robustness by reducing the number of free parameters in the model by an order of magnitude.'),
(79888, 2016, 'bio', 'TnT: a set of libraries for visualizing trees and track-based annotations for the web', 'Pignatelli, Miguel', 'Centre for Therapeutic Target Validation and European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom', 3, 'Computational Biology|Computer Graphics|Data Curation|Genomics|Internet|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw210', '27153646', 'There is an increasing need for rich and dynamic biological data visualizations in bioinformatic web applications. New standards in web technologies, like SVG or Canvas, are now supported by most modern web browsers allowing the blossoming of powerful visualizations in biological data analysis. The exploration of different ways to visualize genomic data is still challenging due to the lack of flexible tools to develop them. Here, I present a set of libraries aimed at creating powerful tree- and track-based visualizations for the web. Its modularity and rich API facilitate the development of many different visualizations ranging from simple species trees to complex visualizations comprising per-node data annotations or even simple genome browsers.'),
(79889, 2016, 'bio', 'Partitioning and correlating subgroup characteristics from Aligned Pattern Clusters', 'Lee, En-Shiun Annie|Whelan, Fiona J|Bowdish, Dawn M E|Wong, Andrew K C', 'Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.|Department of Biochemistry and Biomedical Sciences.|Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada.|Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.', 'Canada|-|Canada|Canada', 6, 'Algorithms|Amino Acid Sequence|Amino Acids|Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btw211', '27153647', 'Evolutionarily conserved amino acids within proteins characterize functional or structural regions. Conversely, less conserved amino acids within these regions are generally areas of evolutionary divergence. A priori knowledge of biological function and species can help interpret the amino acid differences between sequences. However, this information is often erroneous or unavailable, hampering discovery with supervised algorithms. Also, most of the current unsupervised methods depend on full sequence similarity, which become inaccurate when proteins diverge (e.g. inversions, deletions, insertions). Due to these and other shortcomings, we developed a novel unsupervised algorithm which discovers highly conserved regions and uses two types of information measures: (i) data measures computed from input sequences, and (ii) class measures computed using a priori class groupings in order to reveal subgroups (i.e. classes) or functional characteristics.'),
(79890, 2016, 'bio', 'Estimating IBD tracts from low coverage NGS data', 'Vieira, Filipe G|Albrechtsen, Anders|Nielsen, Rasmus', 'Centre for GeoGenetics, Natural History Museum of Denmark.|Department of Biology, University of Copenhagen, Copenhagen, Denmark and.|Department of Biology, University of Copenhagen, Copenhagen, Denmark and Department of Integrative Biology, University of California, Berkeley, USA.', 'Denmark|Denmark|Denmark', 14, 'Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Inbreeding|Oryza|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw212', '27153648', 'The amount of IBD in an individual depends on the relatedness of the individuals parents. However, it can also provide information regarding mating system, past history and effective size of the population from which the individual has been sampled.'),
(79891, 2016, 'bio', 'Bayesian nonparametrics in protein remote homology search', 'Margelevixc4x8dius, Mindaugas', 'Institute of Biotechnology, Vilnius University, Vilnius 10257, Lithuania.', 'Lithuania', 4, 'Algorithms|Amino Acid Sequence|Bayes Theorem|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btw213', '27153649', 'Wide application of modeling of three-dimensional protein structures in biomedical research motivates developing protein sequence alignment computer tools featuring high alignment accuracy and sensitivity to remotely homologous proteins. In this paper, we aim at improving the quality of alignments between sequence profiles, encoded multiple sequence alignments. Modeling profile contexts, fixed-length profile fragments, is engaged to achieve this goal.'),
(79892, 2016, 'bio', 'DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases', 'Queralt-Rosinach, Núria|Piñero, Janet|Bravo, Àlex|Sanz, Ferran|Furlong, Laura I', 'Integrative Biomedical Informatics (IBI) Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Doctor Aiguader 88, E-08003 Barcelona, Spain.|Integrative Biomedical Informatics (IBI) Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Doctor Aiguader 88, E-08003 Barcelona, Spain.|Integrative Biomedical Informatics (IBI) Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Doctor Aiguader 88, E-08003 Barcelona, Spain.|Integrative Biomedical Informatics (IBI) Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Doctor Aiguader 88, E-08003 Barcelona, Spain.|Integrative Biomedical Informatics (IBI) Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Doctor Aiguader 88, E-08003 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain', 29, 'Computational Biology|Databases, Factual|Disease|Humans|Internet|Semantics', 'Journal Article', '10.1093/bioinformatics/btw214', '27153650', 'DisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web. Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected linked open data.'),
(79893, 2016, 'bio', 'CaFE: a tool for binding affinity prediction using end-point free energy methods', 'Liu, Hui|Hou, Tingjun', 'College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.', 'China|China', 13, 'Computational Biology|Drug Design|Molecular Dynamics Simulation|Protein Binding|Software', 'Journal Article', '10.1093/bioinformatics/btw215', '27153651', 'Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost. Here we present an easy-to-use pipeline tool named Calculation of Free Energy (CaFE) to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters.'),
(79894, 2016, 'bio', 'ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information', 'Lachmann, Alexander|Giorgi, Federico M|Lopez, Gonzalo|Califano, Andrea', 'Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.', 'United States|United States|United States|United States', 75, 'Algorithms|Computational Biology|Gene Regulatory Networks|Reverse Genetics|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw216', '27153652', 'The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles.'),
(79895, 2016, 'bio', 'Informed kmer selection for de novo transcriptome assembly', 'Durai, Dilip A|Schulz, Marcel H', 'Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, 66123, Germany Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, 66123, Germany.|Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, 66123, Germany Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, 66123, Germany.', 'Germany|Germany', 10, 'Algorithms|Cluster Analysis|High-Throughput Nucleotide Sequencing|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw217', '27153653', 'De novo transcriptome assembly is an integral part for many RNA-seq workflows. Common applications include sequencing of non-model organisms, cancer or meta transcriptomes. Most de novo transcriptome assemblers use the de Bruijn graph (DBG) as the underlying data structure. The quality of the assemblies produced by such assemblers is highly influenced by the exact word length k As such no single kmer value leads to optimal results. Instead, DBGs over different kmer values are built and the assemblies are merged to improve sensitivity. However, no studies have investigated thoroughly the problem of automatically learning at which kmer value to stop the assembly. Instead a suboptimal selection of kmer values is often used in practice.'),
(79896, 2016, 'bio', 'rnaQUAST: a quality assessment tool for de novo transcriptome assemblies', 'Bushmanova, Elena|Antipov, Dmitry|Lapidus, Alla|Suvorov, Vladimir|Prjibelski, Andrey D', '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 Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia.|Research and Development Department, EMC, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia.', '-|-|-|-|-', 33, 'Computational Biology|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw218', '27153654', 'Ability to generate large RNA-Seq datasets created a demand for both de novo and reference-based transcriptome assemblers. However, while many transcriptome assemblers are now available, there is still no unified quality assessment tool for RNA-Seq assemblies. We present rnaQUAST-a tool for evaluating RNA-Seq assembly quality and benchmarking transcriptome assemblers using reference genome and gene database. rnaQUAST calculates various metrics that demonstrate completeness and correctness levels of the assembled transcripts, and outputs them in a user-friendly report.'),
(79897, 2016, 'bio', 'Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction', 'Xu, Ying-Ying|Yang, Fan|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China.|Institute of Image Processing and Pattern Recognition, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China.|Institute of Image Processing and Pattern Recognition, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China.', 'China|China|China', 14, 'Algorithms|Bayes Theorem|Computational Biology|Fluorescent Antibody Technique|Humans|Immunohistochemistry|Organelles|Protein Transport|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btw219', '27153655', 'Bioimages of subcellular protein distribution as a new data source have attracted much attention in the field of automated prediction of proteins subcellular localization. Performance of existing systems is significantly limited by the small number of high-quality images with explicit annotations, resulting in the small sample size learning problem. This limitation is more serious for the multi-location proteins that co-exist at two or more organelles, because it is difficult to accurately annotate those proteins by biological experiments or automated systems.'),
(79898, 2016, 'bio', 'ASAFE: ancestry-specific allele frequency estimation', 'Zhang, Qian S|Browning, Brian L|Browning, Sharon R', 'Department of Medicine Department of Biostatistics and.|Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.|Department of Biostatistics and.', 'United States|United States|United States', 3, 'Algorithms|Computational Biology|Gene Frequency|Genetics, Population|Genome, Human|Genome-Wide Association Study|Genotype|Humans', 'Journal Article', '10.1093/bioinformatics/btw220', '27153656', 'In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individuals genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data.'),
(79899, 2016, 'bio', 'Spectral identification of topological domains', 'Chen, Jie|Hero, Alfred O|Rajapakse, Indika', 'CIAIC, School of Marine Science and Technology, Northwestern Polytechnical University, China Department of Electrical Engineering and Computer Science Department of Computational Medicine & Bioinformatics, Medical School.|Department of Electrical Engineering and Computer Science Department of Biomedical Engineering Department of Statistics.|Department of Computational Medicine & Bioinformatics, Medical School Department of Mathematics, College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, USA.', 'China|United States|United States', 24, 'Algorithms|Chromosomes|Models, Theoretical|Programming Languages|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btw221', '27153657', 'Topological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy.'),
(79900, 2016, 'bio', 'APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks', 'Bonnici, Vincenzo|Busato, Federico|Micale, Giovanni|Bombieri, Nicola|Pulvirenti, Alfredo|Giugno, Rosalba', 'Department of Computer Science, University of Verona, Strada Le Grazie 15 - 37134, Verona.|Department of Computer Science, University of Verona, Strada Le Grazie 15 - 37134, Verona.|Department of Math and Computer Science, University of Catania, Viale a. Doria 6 - 95125, Catania.|Department of Computer Science, University of Verona, Strada Le Grazie 15 - 37134, Verona.|Department of Clinical and Experimental Medicine, University of Catania, via Palermo, 636 - 95122, Catania.|Department of Computer Science, University of Verona, Strada Le Grazie 15 - 37134, Verona Department of Clinical and Experimental Medicine, University of Catania, via Palermo, 636 - 95122, Catania.', '-|-|-|-|-|-', 9, 'Algorithms|Animals|Computational Biology|Gene Ontology|Humans|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btw223', '27153658', 'Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions.'),
(79901, 2016, 'bio', 'Confidence assignment for mass spectrometry based peptide identifications via the extreme value distribution', 'Alves, Gelio|Yu, Yi-Kuo', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States', 3, 'Algorithms|Databases, Protein|Humans|Mass Spectrometry|Peptides|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw225', '27153659', 'There is a growing trend for biomedical researchers to extract evidence and draw conclusions from mass spectrometry based proteomics experiments, the cornerstone of which is peptide identification. Inaccurate assignments of peptide identification confidence thus may have far-reaching and adverse consequences. Although some peptide identification methods report accurate statistics, they have been limited to certain types of scoring function. The extreme value statistics based method, while more general in the scoring functions it allows, demands accurate parameter estimates and requires, at least in its original design, excessive computational resources. Improving the parameter estimate accuracy and reducing the computational cost for this method has two advantages: it provides another feasible route to accurate significance assessment, and it could provide reliable statistics for scoring functions yet to be developed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79902, 2016, 'bio', 'BRAT-nova: fast and accurate mapping of bisulfite-treated reads', 'Harris, Elena Y|Ounit, Rachid|Lonardi, Stefano', 'Department of Computer Science, California State University, Chico, CA 95929, USA.|Department of Computer Science and Eng, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Eng, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States', 6, 'Chromosome Mapping|DNA Methylation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw226', '27153660', 'In response to increasing amounts of sequencing data, faster and faster aligners need to become available. Here, we introduce BRAT-nova, a completely rewritten and improved implementation of the mapping tool BRAT-BW for bisulfite-treated reads (BS-Seq). BRAT-nova is very fast and accurate. On the human genome, BRAT-nova is 2-7 times faster than state-of-the-art aligners, while maintaining the same percentage of uniquely mapped reads and space usage. On synthetic reads, BRAT-nova is 2-8 times faster than state-of-the-art aligners while maintaining similar mapping accuracy, methylation call accuracy, methylation level accuracy and space efficiency.'),
(79903, 2016, 'bio', 'densityCut: an efficient and versatile topological approach for automatic clustering of biological data', 'Ding, Jiarui|Shah, Sohrab|Condon, Anne', 'Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Department of Molecular Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada.|Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Department of Molecular Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada.|Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.', 'Canada|Canada|Canada', 10, 'Algorithms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw227', '27153661', 'Many biological data processing problems can be formalized as clustering problems to partition data points into sensible and biologically interpretable groups.'),
(79904, 2016, 'bio', 'Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm', 'Luo, Huimin|Wang, Jianxin|Li, Min|Luo, Junwei|Peng, Xiaoqing|Wu, Fang-Xiang|Pan, Yi', 'School of Information Science and Engineering, Central South University, ChangSha, 410083, China School of Computer and Information Engineering, Henan University, KaiFeng 475001, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan S7N 5A9, Canada.|Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA.', 'China|China|China|China|China|Canada|Georgia', 0, 'Algorithms|Computational Biology|Drug Repositioning|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw228', '27153662', 'Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug-disease association information on the similarity measures.'),
(79905, 2016, 'bio', 'MEANS: python package for Moment Expansion Approximation, iNference and Simulation', 'Fan, Sisi|Geissmann, Quentin|Lakatos, Eszter|Lukauskas, Saulius|Ale, Angelique|Babtie, Ann C|Kirk, Paul D W|Stumpf, Michael P H', 'Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|MRC Biostatistics Unit, Cambridge CB2 0SR, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Gene Expression|Kinetics|Models, Statistical|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btw229', '27153663', 'Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the systems moments and apply a closure ansatz to obtain a closed set of differential equations, that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems.'),
(79906, 2016, 'bio', 'Combenefit: an interactive platform for the analysis and visualization of drug combinations', 'Di Veroli, Giovanni Y|Fornari, Chiara|Wang, Dennis|Mollard, Séverine|Bramhall, Jo L|Richards, Frances M|Jodrell, Duncan I', 'CRUK Cambridge Institute, University of Cambridge, Cambridge, UK Early Clinical Development, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Cambridge, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.|Bioinformatics, Oncology Innovative Medicines, AstraZeneca, Cambridge, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 120, 'Computational Biology|Drug Combinations|Drug Delivery Systems|High-Throughput Nucleotide Sequencing|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw230', '27153664', 'Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies.'),
(79907, 2016, 'bio', 'DOGMA: domain-based transcriptome and proteome quality assessment', 'Dohmen, Elias|Kremer, Lukas P M|Bornberg-Bauer, Erich|Kemena, Carsten', 'Institute for Evolution and Biodiversity, University of Münster, Münster 48149, Germany Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, Recklinghausen 45665, Germany.|Institute for Evolution and Biodiversity, University of Münster, Münster 48149, Germany.|Institute for Evolution and Biodiversity, University of Münster, Münster 48149, Germany.|Institute for Evolution and Biodiversity, University of Münster, Münster 48149, Germany.', 'Germany|Germany|Germany|Germany', 12, 'Computational Biology|Genome|Proteome|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw231', '27153665', 'Genome studies have become cheaper and easier than ever before, due to the decreased costs of high-throughput sequencing and the free availability of analysis software. However, the quality of genome or transcriptome assemblies can vary a lot. Therefore, quality assessment of assemblies and annotations are crucial aspects of genome analysis pipelines.'),
(79908, 2016, 'bio', 'XLinkDB 20: integrated, large-scale structural analysis of protein crosslinking data', 'Schweppe, Devin K|Zheng, Chunxiang|Chavez, Juan D|Navare, Arti T|Wu, Xia|Eng, Jimmy K|Bruce, James E', 'Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.|University of Washington Proteomics Resource, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.', 'United States|United States|United States|United States|United States|United States|United States', 22, 'Computational Biology|Computer Simulation|Cross-Linking Reagents|Databases, Protein|Humans|Mass Spectrometry|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw232', '27153666', 'Large-scale chemical cross-linking with mass spectrometry (XL-MS) analyses are quickly becoming a powerful means for high-throughput determination of protein structural information and protein-protein interactions. Recent studies have garnered thousands of cross-linked interactions, yet the field lacks an effective tool to compile experimental data or access the network and structural knowledge for these large scale analyses. We present XLinkDB 2.0 which integrates tools for network analysis, Protein Databank queries, modeling of predicted protein structures and modeling of docked protein structures. The novel, integrated approach of XLinkDB 2.0 enables the holistic analysis of XL-MS protein interaction data without limitation to the cross-linker or analytical system used for the analysis.'),
(79909, 2016, 'bio', 'BAM-matcher: a tool for rapid NGS sample matching', 'Wang, Paul P S|Parker, Wendy T|Branford, Susan|Schreiber, Andreas W', 'Department of Genetics and Molecular Pathology, and ACRF Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology, Adelaide, Australia.|Department of Genetics and Molecular Pathology, and ACRF Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology, Adelaide, Australia, School of Pharmacy and Medical Science, University of South Australia, Adelaide, Australia.|Department of Genetics and Molecular Pathology, and School of Pharmacy and Medical Science, University of South Australia, Adelaide, Australia, School of Biological Sciences and School of Medicine, University of Adelaide, Adelaide, Australia.|ACRF Cancer Genomics Facility, Centre for Cancer Biology, SA Pathology, Adelaide, Australia, School of Biological Sciences and.', 'Australia|Australia|Australia|Australia', 10, 'Genome|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw239', '27153667', 'The standard method used by high-throughput genome sequencing facilities for detecting mislabelled samples is to use independently generated high-density SNP data to determine sample identity. However, as it has now become commonplace to have multiple samples sequenced from the same source, such as for analysis of somatic variants using matched tumour and normal samples, we can directly use the genotype information inherent in the sequence data to match samples and thus bypass the need for additional laboratory testing. Here we present BAM-matcher, a tool that can rapidly determine whether two BAM files represent samples from the same biological source by comparing their genotypes. BAM-matcher is designed to be simple to use, provides easily interpretable results, and is suitable for deployment at early stages of data processing pipelines.'),
(79910, 2016, 'bio', 'FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data', 'Xu, Zheng|Zhang, Guosheng|Wu, Cong|Li, Yun|Hu, Ming', 'Department of Biostatistics Department of Genetics Department of Computer Science.|Department of Computer Science Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA.|College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China.|Department of Biostatistics Department of Genetics Department of Computer Science.|Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA.', 'United States|United States|China|United States|United States', 10, 'Algorithms|Bayes Theorem|Chromatin|Chromosome Mapping|Chromosomes|Computer Simulation|Genome|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw240', '27153668', 'How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. In our previous work, we have developed a novel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the non-negligible spatial dependency among adjacent pairs of loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure.'),
(79911, 2016, 'bio', 'OrfM: a fast open reading frame predictor for metagenomic data', 'Woodcroft, Ben J|Boyd, Joel A|Tyson, Gene W', 'Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia', 5, 'Algorithms|Computational Biology|Gene Expression Profiling|Genome|Humans|Metagenomics|Open Reading Frames|Software', 'Journal Article', '10.1093/bioinformatics/btw241', '27153669', 'Finding and translating stretches of DNA lacking stop codons is a task common in the analysis of sequence data. However, the computational tools for finding open reading frames are sufficiently slow that they are becoming a bottleneck as the volume of sequence data grows. This computational bottleneck is especially problematic in metagenomics when searching unassembled reads, or screening assembled contigs for genes of interest. Here, we present OrfM, a tool to rapidly identify open reading frames (ORFs) in sequence data by applying the Aho-Corasick algorithm to find regions uninterrupted by stop codons. Benchmarking revealed that OrfM finds identical ORFs to similar tools (GetOrf and Translate) but is four-five times faster. While OrfM is sequencing platform-agnostic, it is best suited to large, high quality datasets such as those produced by Illumina sequencers.'),
(79912, 2017, 'bio', 'WeSME: uncovering mutual exclusivity of cancer drivers and beyond', 'Kim, Yoo-Ah|Madan, Sanna|Przytycka, Teresa M', 'NCBI, NLM, NIH, Bethesda, MD, 20894, USA.|Poolesville High School, Poolesville, 20837 MD, USA.|NCBI, NLM, NIH, Bethesda, MD, 20894, USA.', 'United States|United States|United States', 25, 'Breast Neoplasms|Computational Biology|Female|Genes, Neoplasm|Humans|Mutation', 'Journal Article', '10.1093/bioinformatics/btw242', '27153670', 'Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute P -values.'),
(79913, 2016, 'bio', 'Bioinformatics programs are 31-fold over-represented among the highest impact scientific papers of the past two decades', 'Wren, Jonathan D', 'Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104-5005, USA, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, USA.', 'United States', 15, 'Computational Biology|Humans|Periodicals as Topic|Publications|Publishing|Software', 'Journal Article', '10.1093/bioinformatics/btw284', '27153671', 'To analyze the relative proportion of bioinformatics papers and their non-bioinformatics counterparts in the top 20 most cited papers annually for the past two decades.'),
(79914, 2016, 'bio', 'RCP: a novel probe design bias correction method for Illumina Methylation BeadChip', 'Niu, Liang|Xu, Zongli|Taylor, Jack A', 'Division of Biostatistics and Bioinformatics, Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.|Epidemiology Branch.|Epidemiology Branch Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA.', 'United States|United States|United States', 26, 'Bias|DNA Methylation|Genome|High-Throughput Nucleotide Sequencing|Nucleic Acid Probes|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw285', '27153672', 'The Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis.'),
(79915, 2016, 'bio', 'Goldilocks: a tool for identifying genomic regions that are just right', 'Nicholls, Samuel M|Clare, Amanda|Randall, Joshua C', 'Department of Computer Science, Aberystwyth University, Aberystwyth, UK Department of Human Genetics Informatics, Wellcome Trust Sanger Institute, Cambridge, UK.|Department of Computer Science, Aberystwyth University, Aberystwyth, UK.|Department of Human Genetics Informatics, Wellcome Trust Sanger Institute, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btw116', '27153673', ': We present Goldilocks: a Python package providing functionality for collecting summary statistics, identifying shifts in variation, discovering outlier regions and locating and extracting interesting regions from one or more arbitrary genomes for further analysis, for a user-provided definition of interesting.'),
(79916, 2016, 'bio', 'PhamDB: a web-based application for building Phamerator databases', 'Lamine, James G|DeJong, Randall J|Nelesen, Serita M', 'Department of Computer Science.|Department of Biology, Calvin College, Grand Rapids, MI, USA.|Department of Computer Science.', 'United States|United States|United States', 5, 'Bacteriophages|Databases, Nucleic Acid|Genes, Viral|Internet|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw106', '27153674', 'PhamDB is a web application which creates databases of bacteriophage genes, grouped by gene similarity. It is backwards compatible with the existing Phamerator desktop software while providing an improved database creation workflow. Key features include a graphical user interface, validation of uploaded GenBank files, and abilities to import phages from existing databases, modify existing databases and queue multiple jobs.'),
(79917, 2016, 'bio', 'TreeDom: a graphical web tool for analysing domain architecture evolution', 'Haider, Christian|Kavic, Marina|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Solna SE-17121, Sweden FH OÖ - University of Applied Sciences Upper Austria, Hagenberg 4232, Austria.|Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Solna SE-17121, Sweden FH OÖ - University of Applied Sciences Upper Austria, Hagenberg 4232, Austria.|Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Solna SE-17121, Sweden.', 'Austria|Austria|Sweden', 1, 'Computer Graphics|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw140', '27153675', 'We present TreeDom, a web tool for graphically analysing the evolutionary history of domains in multi-domain proteins. Individual domains on the same protein chain may have distinct evolutionary histories, which is important to grasp in order to understand protein function. For instance, it may be important to know whether a domain was duplicated recently or long ago, to know the origin of inserted domains, or to know the pattern of domain loss within a protein family. TreeDom uses the Pfam database as the source of domain annotations, and displays these on a sequence tree. An advantage of TreeDom is that the user can limit the analysis to N sequences that are most similar to a query, or provide a list of sequence IDs to include. Using the Pfam alignment of the selected sequences, a tree is built and displayed together with the domain architecture of each sequence.Availablility and implementation: http://TreeDom.sbc.su.se'),
(79918, 2016, 'bio', 'Isoform-level ribosome occupancy estimation guided by transcript abundance with Ribomap', 'Wang, Hao|McManus, Joel|Kingsford, Carl', 'Computational Biology Department, School of Computer Science and.|Computational Biology Department, School of Computer Science and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, School of Computer Science and.', 'United States|United States|United States', 21, 'Alternative Splicing|High-Throughput Nucleotide Sequencing|Protein Isoforms|Ribosomes|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw085', '27153676', ': Ribosome profiling is a recently developed high-throughput sequencing technique that captures approximately 30xe2x80x89bp long ribosome-protected mRNA fragments during translation. Because of alternative splicing and repetitive sequences, a ribosome-protected read may map to many places in the transcriptome, leading to discarded or arbitrary mappings when standard approaches are used. We present a technique and software that addresses this problem by assigning reads to potential origins proportional to estimated transcript abundance. This yields a more accurate estimate of ribosome profiles compared with a naxc3xafve mapping.'),
(79919, 2016, 'bio', 'Assessing statistical significance in multivariable genome wide association analysis', 'Buzdugan, Laura|Kalisch, Markus|Navarro, Arcadi|Schunk, Daniel|Fehr, Ernst|Bühlmann, Peter', 'Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland Department of Economics, University of Zürich, Zürich 8006, Switzerland.|Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland.|Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Barcelona 08003, Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Center for Genomic Regulation (CRG), Barcelona Biomedical Research Park (PRBB), Barcelona 08003, Spain.|Department of Economics, University of Mainz, Mainz, Germany.|Department of Economics, University of Zürich, Zürich 8006, Switzerland.|Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland.', 'Switzerland|Switzerland|Spain|Germany|Switzerland|Switzerland', 13, 'Cluster Analysis|Computational Biology|Computer Simulation|Genome-Wide Association Study|Genotype|Humans|Linear Models|Phenotype|Polymorphism, Single Nucleotide|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btw128', '27153677', 'Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS.'),
(79920, 2016, 'bio', '2dSpAn: semiautomated 2-d segmentation, classification and analysis of hippocampal dendritic spine plasticity', 'Basu, Subhadip|Plewczynski, Dariusz|Saha, Satadal|Roszkowska, Matylda|Magnowska, Marta|Baczynska, Ewa|Wlodarczyk, Jakub', 'Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.|Center of New Technologies, University of Warsaw, Warsaw, Poland.|Department of Electronics and Communication Engineering, MCKV Institute of Engineering, Howrah 711204, India.|Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.|Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.|Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.|Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.', 'India|Poland|India|Poland|Poland|Poland|Poland', 15, 'Dendrites|Dendritic Spines|Hippocampus|Microscopy, Confocal|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btw172', '27153678', 'Accurate and effective dendritic spine segmentation from the dendrites remains as a challenge for current neuroimaging research community. In this article, we present a new method (2dSpAn) for 2-d segmentation, classification and analysis of structural/plastic changes of hippocampal dendritic spines. A user interactive segmentation method with convolution kernels is designed to segment the spines from the dendrites. Formal morphological definitions are presented to describe key attributes related to the shape of segmented spines. Spines are automatically classified into one of four classes: Stubby, Filopodia, Mushroom and Spine-head Protrusions.'),
(79921, 2016, 'bio', 'Coala: an R framework for coalescent simulation', 'Staab, Paul R|Metzler, Dirk', 'Department of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany.|Department of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany.', 'Germany|Germany', 11, 'Computational Biology|Computer Simulation|Evolution, Molecular|Genetics, Population|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btw098', '27153679', 'Simulation programs based on the coalescent efficiently generate genetic data according to a given model of evolution. We present coala, an R package for calling coalescent simulators with a unified syntax. It can execute simulations with several programs, calculate additional summary statistics and combine multiple simulations to create biologically more realistic data.'),
(79922, 2016, 'bio', 'Transcript structure and domain display: a customizable transcript visualization tool', 'Watanabe, Kenneth A|Ma, Kaiwang|Homayouni, Arielle|Rushton, Paul J|Shen, Qingxi J', 'School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.|School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA School of Medical Technology & Engineering, Henan University of Science & Technology, Luoyang, 471003, China.|School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.|Texas A&M AgriLife Research, Dallas, TX 75252, USA.|School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.', 'United States|China|United States|United States|United States', 1, 'Computational Biology|Internet|Protein Domains|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw095', '27153680', 'Transcript Structure and Domain Display (TSDD) is a publicly available, web-based program that provides publication quality images of transcript structures and domains. TSDD is capable of producing transcript structures from GFF/GFF3 and BED files. Alternatively, the GFF files of several model organisms have been pre-loaded so that users only needs to enter the locus IDs of the transcripts to be displayed. Visualization of transcripts provides many benefits to researchers, ranging from evolutionary analysis of DNA-binding domains to predictive function modeling.'),
(79923, 2016, 'bio', 'OpenSegSPIM: a user-friendly segmentation tool for SPIM data', 'Gole, Laurent|Ong, Kok Haur|Boudier, Thomas|Yu, Weimiao|Ahmed, Sohail', 'Institute of Medical Biology, Agency for Science, Technology and Research (A*STAR).|Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR).|IPAL UMI 2955 (CNRS, UPMC, UJF, IMT, I2R, NUS), BioInformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 13867, Singapore.|Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR).|Institute of Medical Biology, Agency for Science, Technology and Research (A*STAR).', '-|-|Singapore|-|-', 4, 'Algorithms|Animals|Cell Nucleus|Humans|Image Interpretation, Computer-Assisted|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btw093', '27153681', 'OpenSegSPIM is an open access and user friendly 3D automatic quantitative analysis tool for Single Plane Illumination Microscopy data. The software is designed to extract, in a user-friendly way, quantitative relevant information from SPIM image stacks, such as the number of nuclei or cells. It provides quantitative measurement (volume, sphericity, distance, intensity) on Light Sheet Fluorescent Microscopy images.'),
(79924, 2016, 'bio', 'RIPPER: a framework for MS1 only metabolomics and proteomics label-free relative quantification', 'Van Riper, Susan K|Higgins, LeeAnn|Carlis, John V|Griffin, Timothy J', 'Department of Biomedical Informatics and Computational Biology, University of Minnesota, Rochester University of Minnesota Informatics Institute, University of Minnesota, St Paul.|Department of Biochemistry, Molecular Biology, and Biophysics.|Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Biochemistry, Molecular Biology, and Biophysics.', 'United States|United States|United States|United States', 8, 'Algorithms|Computational Biology|Humans|Mass Spectrometry|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btw091', '27153682', 'RIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS.'),
(79925, 2016, 'bio', 'Assembly scaffolding with PE-contaminated mate-pair libraries', 'Sahlin, Kristoffer|Chikhi, Rayan|Arvestad, Lars', 'Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden.|CNRS, CRIStAL, UMR 9189, Villeneuve Dascq 59650, France and.|Swedish e-Science Research Centre, Science for Life Laboratory, and Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.', 'Sweden|France|Sweden', 12, 'Algorithms|Bacteria|Gene Library|Genomics|Humans|Programming, Linear|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw064', '27153683', 'Scaffolding is often an essential step in a genome assembly process, in which contigs are ordered and oriented using read pairs from a combination of paired-end libraries and longer-range mate-pair libraries. Although a simple idea, scaffolding is unfortunately hard to get right in practice. One source of problems is so-called PE-contamination in mate-pair libraries, in which a non-negligible fraction of the read pairs get the wrong orientation and a much smaller insert size than what is expected. This contamination has been discussed before, in relation to integrated scaffolders, but solutions rely on the orientation being observable, e.g. by finding the junction adapter sequence in the reads. This is not always possible, making orientation and insert size of a read pair stochastic. To our knowledge, there is neither previous work on modeling PE-contamination, nor a study on the effect PE-contamination has on scaffolding quality.'),
(79926, 2016, 'bio', 'solarius: an R interface to SOLAR for variance component analysis in pedigrees', 'Ziyatdinov, Andrey|Brunel, Helena|Martinez-Perez, Angel|Buil, Alfonso|Perera, Alexandre|Soria, Jose Manuel', 'Unitat De Genòmica De Malalties Complexes, Institut Dinvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Unitat De Genòmica De Malalties Complexes, Institut Dinvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Unitat De Genòmica De Malalties Complexes, Institut Dinvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Department of Genetics Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|B2SLab, Department ESAII, Universitat Politècnica De Catalunya, Barcelona, Spain CIBER in Bioengineering Biomaterials and Nanomedicine, Barcelona, Spain.|Unitat De Genòmica De Malalties Complexes, Institut Dinvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.', 'Spain|Spain|Spain|Switzerland|Spain|Spain', 10, 'Analysis of Variance|Genetic Linkage|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Statistical|Multivariate Analysis|Pedigree|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btw080', '27153684', ': The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is one of the standard software programs to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It parses also the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses that makes the calculation of genome-wide scans more efficient.'),
(79927, 2016, 'bio', 'BALL-SNPgp-from genetic variants toward computational diagnostics', 'Mueller, Sabine C|Backes, Christina|Gress, Alexander|Baumgarten, Nina|Kalinina, Olga V|Moll, Andreas|Kohlbacher, Oliver|Meese, Eckart|Keller, Andreas', 'Chair for Clinical Bioinformatics, Saarland University, Saarbrxc3xbccken 66123, Germany Department of Human Genetics, Saarland University, Homburg 66421, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrxc3xbccken 66123, Germany.|Max Planck Institute for Informatics, Saarland University, Saarbrxc3xbccken 66123, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrxc3xbccken 66123, Germany.|Max Planck Institute for Informatics, Saarland University, Saarbrxc3xbccken 66123, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrxc3xbccken 66123, Germany.|Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center Department of Computer Science, University of Tuebingen, Txc3xbcbingen 72076, Germany, Biomolecular Interactions, Max Planck Institute for Developmental Biology, Txc3xbcbingen 72076, Germany.|Department of Human Genetics, Saarland University, Homburg 66421, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrxc3xbccken 66123, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Gene Library|Genetic Variation|Humans|Molecular Diagnostic Techniques|Polymorphism, Single Nucleotide|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btw084', '27153685', 'In medical research, it is crucial to understand the functional consequences of genetic alterations, for example, non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a three-dimensional visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites.'),
(79928, 2016, 'bio', 'MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks', 'Pang, Chao|van Enckevort, David|de Haan, Mark|Kelpin, Fleur|Jetten, Jonathan|Hendriksen, Dennis|de Boer, Tommy|Charbon, Bart|Winder, Erwin|van der Velde, K Joeri|Doiron, Dany|Fortier, Isabel|Hillege, Hans|Swertz, Morris A', 'Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands.|Research Institute of the McGill University Health Centre and Department of Medicine, McGill University, Montreal, Canada.|Research Institute of the McGill University Health Centre and Department of Medicine, McGill University, Montreal, Canada.|Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Algorithms|Biological Ontologies|Biological Specimen Banks|Computational Biology|Humans|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btw155', '27153686', 'While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration.'),
(79929, 2016, 'bio', 'EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes', 'Liu, Jin|Wan, Xiang|Ma, Shuangge|Yang, Can', 'Center of Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.|Department of Computer Science, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon, Hong Kong.|Department of Biostatistics, Yale University, New Heaven, CT, USA.|Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong.', 'Singapore|Hong Kong|United States|Hong Kong', 13, 'Bayes Theorem|Genome-Wide Association Study|Risk|Software', 'Journal Article', '10.1093/bioinformatics/btw081', '27153687', 'Researchers worldwide have generated a huge volume of genomic data, including thousands of genome-wide association studies (GWAS) and massive amounts of gene expression data from different tissues. How to perform a joint analysis of these data to gain new biological insights has become a critical step in understanding the etiology of complex diseases. Due to the polygenic architecture of complex diseases, the identification of risk genes remains challenging. Motivated by the shared risk genes found in complex diseases and tissue-specific gene expression patterns, we propose as an Empirical Bayes approach to integrating Pleiotropy and Tissue-Specific information (EPS) for prioritizing risk genes.'),
(79930, 2016, 'bio', 'A simple method to control over-alignment in the MAFFT multiple sequence alignment program', 'Katoh, Kazutaka|Standley, Daron M', 'Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.|Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan Institute for Virus Research, Kyoto University, Kyoto 606-8507, Japan.', 'Japan|Japan', 167, 'Algorithms|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw108', '27153688', 'We present a new feature of the MAFFT multiple alignment program for suppressing over-alignment (aligning unrelated segments). Conventional MAFFT is highly sensitive in aligning conserved regions in remote homologs, but the risk of over-alignment is recently becoming greater, as low-quality or noisy sequences are increasing in protein sequence databases, due, for example, to sequencing errors and difficulty in gene prediction.'),
(79931, 2016, 'bio', 'metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis', 'Cichonska, Anna|Rousu, Juho|Marttinen, Pekka|Kangas, Antti J|Soininen, Pasi|Lehtimäki, Terho|Raitakari, Olli T|Järvelin, Marjo-Riitta|Salomaa, Veikko|Ala-Korpela, Mika|Ripatti, Samuli|Pirinen, Matti', 'Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland, Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland.|Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.|Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland.|Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland, Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.|Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, UK, Centre for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland, Biocenter Oulu, University of Oulu, Oulu, Finland, Unit of Primary Care, Oulu University Hospital, Oulu, Finland.|National Institute for Health and Welfare, Helsinki, Finland.|Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland, Public Health, University of Helsinki, Helsinki, Finland and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 42, 'Algorithms|Computational Biology|Genome-Wide Association Study|Genotype|Humans|Multivariate Analysis|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw052', '27153689', 'A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.'),
(79932, 2016, 'bio', 'ORFanFinder: automated identification of taxonomically restricted orphan genes', 'Ekstrom, Alex|Yin, Yanbin', 'Department of Computer Science.|Department of Biological Sciences, Montgomery Hall 325A, Northern Illinois University, DeKalb, IL, USA.', 'United States|United States', 4, 'Algorithms|Computational Biology|Eukaryota|Genome|Genomics|Internet|Open Reading Frames|Prokaryotic Cells|Software', 'Journal Article', '10.1093/bioinformatics/btw122', '27153690', 'Orphan genes, also known as ORFans, are newly evolved genes in a genome that enable the organism to adapt to specific living environment. The gene content of every sequenced genome can be classified into different age groups, based on how widely/narrowly a genes homologs are distributed in the context of species taxonomy. Those having homologs restricted to organisms of particular taxonomic ranks are classified as taxonomically restricted ORFans.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79933, 2016, 'bio', 'SharpViSu: integrated analysis and segmentation of super-resolution microscopy data', 'Andronov, Leonid|Lutz, Yves|Vonesch, Jean-Luc|Klaholz, Bruno P', 'Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France Université de Strasbourg, Strasbourg, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France Université de Strasbourg, Strasbourg, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France Université de Strasbourg, Strasbourg, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France Université de Strasbourg, Strasbourg, France.', 'France|France|France|France', 0, 'Computer Graphics|Image Processing, Computer-Assisted|Microscopy|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw123', '27153691', 'We introduce SharpViSu, an interactive open-source software with a graphical user interface, which allows performing processing steps for localization data in an integrated manner. This includes common features and new tools such as correction of chromatic aberrations, drift correction based on iterative cross-correlation calculations, selection of localization events, reconstruction of 2D and 3D datasets in different representations, estimation of resolution by Fourier ring correlation, clustering analysis based on Voronoi diagrams and Ripleys functions. SharpViSu is optimized to work with eventlist tables exported from most popular localization software. We show applications of these on single and double-labelled super-resolution data.'),
(79934, 2016, 'bio', 'Reaction Decoder Tool (RDT): extracting features from chemical reactions', 'Rahman, Syed Asad|Torrance, Gilliean|Baldacci, Lorenzo|Martínez Cuesta, Sergio|Fenninger, Franz|Gopal, Nimish|Choudhary, Saket|May, John W|Holliday, Gemma L|Steinbeck, Christoph|Thornton, Janet M', 'European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Congenica Ltd, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK DISI, University of Bologna, V.Le Risorgimento 2, Bologna, Italy.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Michael Smith Laboratories, the University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Innovation Centre (Unit 23), Cambridge Science Park, NextMove Software Ltd, Cambridge CB4 0EY, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Bioengineering, UCSF School of Pharmacy, San Francisco, CA 94158, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|Italy|United Kingdom|Canada|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Biochemistry|Computational Biology|Data Mining|Databases, Chemical|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btw096', '27153692', 'Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features.'),
(79935, 2016, 'bio', 'XIBD: software for inferring pairwise identity by descent on the X chromosome', 'Henden, Lyndal|Wakeham, David|Bahlo, Melanie', 'Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia Department of Medical Biology.|School of Physics.|Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia Department of Medical Biology School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia.', 'Australia|-|Australia', 5, 'Chromosomes, Human, X|Female|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Humans|Male|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw124', '27153693', 'XIBD performs pairwise relatedness mapping on the X chromosome using dense single nucleotide polymorphism (SNP) data from either SNP chips or next generation sequencing data. It correctly accounts for the difference in chromosomal numbers between males and females and estimates global relatedness as well as regions of the genome that are identical by descent (IBD). XIBD also generates novel graphical summaries of all pairwise IBD tracts for a cohort making it very useful for disease locus mapping.'),
(79936, 2016, 'bio', 'Direct calculation of minimal cut sets involving a specific reaction knock-out', 'Tobalina, Luis|Pey, Jon|Planes, Francisco J', 'CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain.|CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain.|CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Computational Biology|Escherichia coli|Humans|Metabolic Flux Analysis|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btw072', '27153694', 'The concept of Minimal Cut Sets (MCSs) is used in metabolic network modeling to describe minimal groups of reactions or genes whose simultaneous deletion eliminates the capability of the network to perform a specific task. Previous work showed that MCSs where closely related to Elementary Flux Modes (EFMs) in a particular dual problem, opening up the possibility to use the tools developed for computing EFMs to compute MCSs. Until recently, however, there existed no method to compute an EFM with some specific characteristic, meaning that, in the case of MCSs, the only strategy to obtain them was to enumerate them using, for example, the standard K-shortest EFMs algorithm.'),
(79937, 2016, 'bio', 'SASpy: a PyMOL plugin for manipulation and refinement of hybrid models against small angle X-ray scattering data', 'Panjkovich, Alejandro|Svergun, Dmitri I', 'European Molecular Biology Laboratory, Hamburg Outstation, EMBL C/O DESY, Hamburg 22607, Germany.|European Molecular Biology Laboratory, Hamburg Outstation, EMBL C/O DESY, Hamburg 22607, Germany.', 'Germany|Germany', 15, 'Macromolecular Substances|Models, Molecular|Scattering, Small Angle|X-Ray Diffraction|X-Rays', 'Journal Article', '10.1093/bioinformatics/btw071', '27153695', 'Complex formation and conformational transitions of biological macromolecules in solution can be effectively studied using the information about overall shape and size provided by small angle X-ray scattering (SAXS). Hybrid modeling is often applied to integrate high-resolution models into SAXS data analysis. To facilitate this task, we present SASpy, a PyMOL plugin that provides an easy-to-use graphical interface for SAXS-based hybrid modeling. Through a few mouse clicks in SASpy, low-resolution models can be superimposed to high-resolution structures, theoretical scattering profiles and fits can be calculated and displayed on-the-fly. Mouse-based manual rearrangements of complexes are conveniently applied to rapidly check and interactively refine tentative models. Interfaces to automated rigid-body and flexible refinement of macromolecular models against the experimental SAXS data are provided.'),
(79938, 2016, 'bio', 'll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks', 'Saa, Pedro A|Nielsen, Lars K', 'Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD, Australia.|Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD, Australia.', 'Australia|Australia', 15, 'Algorithms|Metabolic Networks and Pathways|Models, Theoretical|Research Design|Software', 'Journal Article', '10.1093/bioinformatics/btw132', '27153696', 'Random sampling of the solution space has emerged as a popular tool to explore and infer properties of large metabolic networks. However, conventional sampling approaches commonly used do not eliminate thermodynamically unfeasible loops.'),
(79939, 2016, 'bio', 'FRAGSION: ultra-fast protein fragment library generation by IOHMM sampling', 'Bhattacharya, Debswapna|Adhikari, Badri|Li, Jilong|Cheng, Jianlin', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Department of Computer Science, Informatics Institute and C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States|United States', 0, 'Amino Acid Sequence|Markov Chains|Peptide Library|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw067', '27153697', 'Speed, accuracy and robustness of building protein fragment library have important implications in de novo protein structure prediction since fragment-based methods are one of the most successful approaches in template-free modeling (FM). Majority of the existing fragment detection methods rely on database-driven search strategies to identify candidate fragments, which are inherently time-consuming and often hinder the possibility to locate longer fragments due to the limited sizes of databases. Also, it is difficult to alleviate the effect of noisy sequence-based predicted features such as secondary structures on the quality of fragment.'),
(79940, 2017, 'bio', 'ISCBs initial reaction to New England Journal of Medicine editorial on data sharing', 'Berger, Bonnie|Gaasterland, Theresa|Lengauer, Thomas|Orengo, Christine A|Gaeta, Bruno|Markel, Scott|Valencia, Alfonso', 'International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.|International Society for Computational Biology, Inc. (ISCB), 9650 Rockville Pike Bethesda, Maryland 20814, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, 'Editorial Policies|Humans|Information Dissemination|Periodicals as Topic|Publishing', 'Journal Article', '10.1093/bioinformatics/btw090', '27153698', '-'),
(79941, 2016, 'bio', 'fqtools: an efficient software suite for modern FASTQ file manipulation', 'Droop, Alastair P', 'MRC Medical Bioinformatics Centre, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.', 'United Kingdom', 11, 'Biometry|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw088', '27153699', 'Many Next Generation Sequencing analyses involve the basic manipulation of input sequence data before downstream processing (e.g. searching for specific sequences, format conversion or basic file statistics). The rapidly increasing data volumes involved in NGS make any dataset manipulation a time-consuming and error-prone process. I have developed fqtools, a fast and reliable FASTQ file manipulation suite that can process the full set of valid FASTQ files, including those with multi-line sequences, whilst identifying invalid files. Fqtools is faster than similar tools, and is designed for use in automatic processing pipelines.'),
(79942, 2016, 'bio', 'dbDSM: a manually curated database for deleterious synonymous mutations', 'Wen, Pengbo|Xiao, Peng|Xia, Junfeng', 'Institute of Health Sciences, School of Life Sciences, Anhui University, Hefei, Anhui 230601, China and.|Institute of Health Sciences, School of Life Sciences, Anhui University, Hefei, Anhui 230601, China and.|Institute of Health Sciences, School of Life Sciences, Anhui University, Hefei, Anhui 230601, China and Co-Innovation Center for Information Supply and Assurance Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.', 'China|China|China', 0, 'Databases, Genetic|Humans|Proteins|RNA Splicing|Silent Mutation', 'Journal Article', '10.1093/bioinformatics/btw086', '27153700', 'Synonymous mutations (SMs), which changed the sequence of a gene without directly altering the amino acid sequence of the encoded protein, were thought to have no functional consequences for a long time. They are often assumed to be neutral in models of mutation and selection and were completely ignored in many studies. However, accumulating experimental evidence has demonstrated that these mutations exert their impact on gene functions via splicing accuracy, mRNA stability, translation fidelity, protein folding and expression, and some of these mutations are implicated in human diseases. To the best of our knowledge, there is still no database specially focusing on disease-related SMs.'),
(79943, 2016, 'bio', 'Tally: a scoring tool for boundary determination between repetitive and non-repetitive protein sequences', 'Richard, François D|Alves, Ronnie|Kajava, Andrey V', 'Centre de Recherche en Biologie cellulaire de Montpellier (CRBM), UMR 5237 CNRS, Université Montpellier 1919 Route de Mende, Cedex 5, Montpellier 34293, France Institut de Biologie Computationnelle (IBC), Montpellier 34095, France.|Institut de Biologie Computationnelle (IBC), Montpellier 34095, France Pós-Graduação em Ciência da Computação (PPGCC), Universidade Federal do Pará, Belém, Brazil.|Centre de Recherche en Biologie cellulaire de Montpellier (CRBM), UMR 5237 CNRS, Université Montpellier 1919 Route de Mende, Cedex 5, Montpellier 34293, France Institut de Biologie Computationnelle (IBC), Montpellier 34095, France University ITMO, Institute of Bioengineering, St. Petersburg 197101, Russia.', 'France|Brazil|France', 1, 'Algorithms|Amino Acid Sequence|Computational Biology|Machine Learning|Proteins|Proteome|Sequence Analysis, Protein|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btw118', '27153701', 'Tandem Repeats (TRs) are abundant in proteins, having a variety of fundamental functions. In many cases, evolution has blurred their repetitive patterns. This leads to the problem of distinguishing between sequences that contain highly imperfect TRs, and the sequences without TRs. The 3D structure of proteins can be used as a benchmarking criterion for TR detection in sequences, because the vast majority of proteins having TRs in sequences are built of repetitive 3D structural blocks. According to our benchmark, none of the existing scoring methods are able to clearly distinguish, based on the sequence analysis, between structures with and without 3D TRs.'),
(79944, 2016, 'bio', 'SweepFinder2: increased sensitivity, robustness and flexibility', 'DeGiorgio, Michael|Huber, Christian D|Hubisz, Melissa J|Hellmann, Ines|Nielsen, Rasmus', 'Department of Biology and Institute for CyberScience, Pennsylvania State University, University Park, PA, USA.|Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.|Department Biologie II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany and.|Department of Integrative Biology, University of California, Berkeley, CA, USA.', 'United States|United States|United States|Germany|United States', 54, 'Evolution, Molecular|Humans|Likelihood Functions|Mutation Rate|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btw051', '27153702', 'SweepFinder is a widely used program that implements a powerful likelihood-based method for detecting recent positive selection, or selective sweeps. Here, we present SweepFinder2, an extension of SweepFinder with increased sensitivity and robustness to the confounding effects of mutation rate variation and background selection. Moreover, SweepFinder2 has increased flexibility that enables the user to specify test sites, set the distance between test sites and utilize a recombination map.'),
(79945, 2016, 'bio', 'Phasing for medical sequencing using rare variants and large haplotype reference panels', 'Sharp, Kevin|Kretzschmar, Warren|Delaneau, Olivier|Marchini, Jonathan', 'Department of Statistics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK and.|Département De Génétique Et Développement (GEDEV), University of Geneva, Geneva, Switzerland.|Department of Statistics, University of Oxford, Oxford, UK, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK and.', 'United Kingdom|United Kingdom|Switzerland|United Kingdom', 0, 'Algorithms|Alleles|Computational Biology|Genetic Variation|Genotype|Haplotypes|Humans', 'Journal Article', '10.1093/bioinformatics/btw065', '27153703', 'There is growing recognition that estimating haplotypes from high coverage sequencing of single samples in clinical settings is an important problem. At the same time very large datasets consisting of tens and hundreds of thousands of high-coverage sequenced samples will soon be available. We describe a method that takes advantage of these huge human genetic variation resources and rare variant sharing patterns to estimate haplotypes on single sequenced samples. Sharing rare variants between two individuals is more likely to arise from a recent common ancestor and, hence, also more likely to indicate similar shared haplotypes over a substantial flanking region of sequence.'),
(79946, 2016, 'bio', 'A web application for sample size and power calculation in case-control microbiome studies', 'Mattiello, Federico|Verbist, Bie|Faust, Karoline|Raes, Jeroen|Shannon, William D|Bijnens, Luc|Thas, Olivier', 'Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent, 9000.|Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, 2340, Belgium.|KU Leuven, Laboratory of Molecular Bacteriology and Department of Microbiology and Immunology, Herestraat 49, Leuven, 3000, Belgium.|KU Leuven, Laboratory of Molecular Bacteriology and Department of Microbiology and Immunology, Herestraat 49, Leuven, 3000, Belgium.|BioRankings, 4041 Forest Park Ave, St.Louis, MO 63108, USA.|Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, 2340, Belgium.|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent, 9000 University of Wollongong, National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, Australia.', 'United States|Belgium|Belgium|Belgium|United States|Belgium|Australia', 16, 'Case-Control Studies|Computational Biology|Humans|Internet|Microbiota|Models, Theoretical|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btw099', '27153704', ': When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the HMP R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power.'),
(79947, 2016, 'bio', 'KiT: a MATLAB package for kinetochore tracking', 'Armond, Jonathan W|Vladimirou, Elina|McAinsh, Andrew D|Burroughs, Nigel J', 'Division of Biomedical Cell Biology, Mechanochemical Cell Biology Building, Warwick Medical School Warwick Systems Biology Centre and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.|Division of Biomedical Cell Biology, Mechanochemical Cell Biology Building, Warwick Medical School.|Division of Biomedical Cell Biology, Mechanochemical Cell Biology Building, Warwick Medical School.|Warwick Systems Biology Centre and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Computer Graphics|Fluorescence|Humans|Image Enhancement|Kinetochores|Software|Spindle Apparatus|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw087', '27153705', 'During mitosis, chromosomes are attached to the mitotic spindle via large protein complexes called kinetochores. The motion of kinetochores throughout mitosis is intricate and automated quantitative tracking of their motion has already revealed many surprising facets of their behaviour. Here, we present KiT (Kinetochore Tracking)-an easy-to-use, open-source software package for tracking kinetochores from live-cell fluorescent movies. KiT supports 2D, 3D and multi-colour movies, quantification of fluorescence, integrated deconvolution, parallel execution and multiple algorithms for particle localization.'),
(79948, 2016, 'bio', 'Comment on Discovering hospital admission patterns using models learnt from electronic hospital records The importance of using the right codes', 'Lopez-Campos, Guillermo H|Martin-Sanchez, Fernando|Gray, Kathleen', 'Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, VIC, Australia and.|Department of Healthcare Policy and Research. Division of Health Informatics. Weill Cornell Medicine, New York, NY, USA.|Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, VIC, Australia and.', 'Australia|United States|Australia', 0, 'Hospital Records|Humans', 'Letter', '10.1093/bioinformatics/btw078', '27153706', 'guillermo.lopez@unimelb.edu.au.'),
(79949, 2016, 'bio', 'PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks', 'Lu, Hui-Chun|Herrera Braga, Julián|Fraternali, Franca', 'Randall Division of Cell and Molecular Biophysics, Kings College London, London SE1 1UL, UK.|Randall Division of Cell and Molecular Biophysics, Kings College London, London SE1 1UL, UK.|Randall Division of Cell and Molecular Biophysics, Kings College London, London SE1 1UL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Computational Biology|Genetic Variation|Humans|Polymorphism, Single Nucleotide|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw153', '27153707', 'We present a practical computational pipeline to readily perform data analyses of protein-protein interaction networks by using genetic and functional information mapped onto protein structures. We provide a 3D representation of the available protein structure and its regions (surface, interface, core and disordered) for the selected genetic variants and/or SNPs, and a prediction of the mutants impact on the protein as measured by a range of methods. We have mapped in total 2587 genetic disorder-related SNPs from OMIM, 587 873 cancer-related variants from COSMIC, and 1 484 045 SNPs from dbSNP. All result data can be downloaded by the user together with an R-script to compute the enrichment of SNPs/variants in selected structural regions.'),
(79950, 2016, 'bio', 'BLESS 2: accurate, memory-efficient and fast error correction method', 'Heo, Yun|Ramachandran, Anand|Hwu, Wen-Mei|Ma, Jian|Chen, Deming', 'Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Computers|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw146', '27153708', 'The most important features of error correction tools for sequencing data are accuracy, memory efficiency and fast runtime. The previous version of BLESS was highly memory-efficient and accurate, but it was too slow to handle reads from large genomes. We have developed a new version of BLESS to improve runtime and accuracy while maintaining a small memory usage. The new version, called BLESS 2, has an error correction algorithm that is more accurate than BLESS, and the algorithm has been parallelized using hybrid MPI and OpenMP programming. BLESS 2 was compared with five top-performing tools, and it was found to be the fastest when it was executed on two computing nodes using MPI, with each node containing twelve cores. Also, BLESS 2 showed at least 11% higher gain while retaining the memory efficiency of the previous version for large genomes.'),
(79951, 2016, 'bio', 'repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data', 'Elhanati, Yuval|Marcou, Quentin|Mora, Thierry|Walczak, Aleksandra M', 'Laboratoire de physique théorique, CNRS, UPMC and Ecole normale supérieure, Paris, France.|Laboratoire de physique théorique, CNRS, UPMC and Ecole normale supérieure, Paris, France.|Laboratoire de physique statistique, CNRS, UPMC and Ecole normale supérieure, Paris, France.|Laboratoire de physique théorique, CNRS, UPMC and Ecole normale supérieure, Paris, France.', 'France|France|France|France', 19, 'Algorithms|Gene Rearrangement, T-Lymphocyte|Humans|Probability|Receptors, Antigen, T-Cell|Sequence Alignment|Software|V(D)J Recombination', 'Journal Article', '10.1093/bioinformatics/btw112', '27153709', 'The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertions-described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence.'),
(79952, 2016, 'bio', 'Detecting critical state before phase transition of complex biological systems by hidden Markov model', 'Chen, Pei|Liu, Rui|Li, Yongjun|Chen, Luonan', 'School of Computer Science and Engineering.|School of Mathematics, South China University of Technology, Guangzhou 510640, China.|School of Computer Science and Engineering.|Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.', '-|China|-|China', 27, 'Acute Lung Injury|Algorithms|Carcinoma, Hepatocellular|Computational Biology|Gene Regulatory Networks|Humans|Liver Neoplasms|Markov Chains|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw154', '27153710', 'Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e. before-transition state, pre-transition state and after-transition state, which can be considered as three different Markov processes.'),
(79953, 2016, 'bio', 'Simulated single molecule microscopy with SMeagol', 'Lindxc3xa9n, Martin|xc4x86urixc4x87, Vladimir|Boucharin, Alexis|Fange, David|Elf, Johan', 'Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 11, 'Algorithms|Programming Languages|Single Molecule Imaging|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw109', '27153711', 'SMeagol is a software tool to simulate highly realistic microscopy data based on spatial systems biology models, in order to facilitate development, validation and optimization of advanced analysis methods for live cell single molecule microscopy data.'),
(79954, 2016, 'bio', 'Computational clustering for viral reference proteomes', 'Chen, Chuming|Huang, Hongzhan|Mazumder, Raja|Natale, Darren A|McGarvey, Peter B|Zhang, Jian|Polson, Shawn W|Wang, Yuqi|Wu, Cathy H|UniProt Consortium', 'Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.|Department of Biochemistry and Molecular Medicine, The George Washington University, Washington, DC 20037, USA.|Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.|Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.|Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Amino Acid Sequence|Cluster Analysis|Computational Biology|Databases, Protein|Knowledge Bases|Proteome|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btw110', '27153712', 'The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies.'),
(79955, 2016, 'bio', 'ecceTERA: comprehensive gene tree-species tree reconciliation using parsimony', 'Jacox, Edwin|Chauve, Cedric|Szxc3xb6llxc5x91si, Gergely J|Ponty, Yann|Scornavacca, Celine', 'ISE-M, Universitxc3xa9 Montpellier, CNRS, IRD, EPHE, Montpellier, France.|Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.|ELTE-MTA Lendxc3xbclet Biophysics Research Group, Budapest, Hungary.|LIX, CNRS/Inria AMIB, Ecole Polytechnique, Palaiseau, France.|ISE-M, Universitxc3xa9 Montpellier, CNRS, IRD, EPHE, Montpellier, France Institut de Biologie Computationnelle (IBC), Montpellier, France.', 'France|Canada|Hungary|France|France', 36, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Duplication|Models, Theoretical|Multigene Family|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btw105', '27153713', ': A gene tree-species tree reconciliation explains the evolution of a gene tree within the species tree given a model of gene-family evolution. We describe ecceTERA, a program that implements a generic parsimony reconciliation algorithm, which accounts for gene duplication, loss and transfer (DTL) as well as speciation, involving sampled and unsampled lineages, within undated, fully dated or partially dated species trees. The ecceTERA reconciliation model and algorithm generalize or improve upon most published DTL parsimony algorithms for binary species trees and binary gene trees. Moreover, ecceTERA can estimate accurate species-tree aware gene trees using amalgamation.'),
(79956, 2016, 'bio', 'NSimScan: DNA comparison tool with increased speed, sensitivity and accuracy', 'Novichkov, Vladimir|Kaznadzey, Anna|Alexandrova, Natalia|Kaznadzey, Denis', 'Independent Researcher, Bridgewater, NJ, USA.|Institute for Information Transmission Problems, RAS, Moscow, Russia.|Genome Designs, Inc, Walnut Creek, CA, USA.|Thermo-Fisher, South San Francisco, CA, USA.', 'United States|United States|United States|United States', 5, 'DNA|Databases, Nucleic Acid|Genomics|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btw126', '27153714', 'Nucleotide Similarity Scanner (NSimScan) is specialized for searching massive DNA databases for distant similarities. Its targeted applications include phylogenomics, comparative and functional studies of non-coding sequences, contamination detection, etc. NSimScan outperforms industry standard tools in combined sensitivity, accuracy and speed, operating at sensitivity similar to BLAST, accuracy of ssearch and speed of MegaBLAST.'),
(79957, 2016, 'bio', 'cit: hypothesis testing software for mediation analysis in genomic applications', 'Millstein, Joshua|Chen, Gary K|Breton, Carrie V', 'Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA.', 'United States|United States|United States', 11, 'Gene Library|Genome|Genomics|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw135', '27153715', 'The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches.'),
(79958, 2016, 'bio', 'Multiple structure single parameter: analysis of a single protein nano environment descriptor characterizing a shared loci on structurally aligned proteins', 'Salim, José Augusto|Borro, Luiz|Mazoni, Ivan|Yano, Inácio|Jardine, José G|Neshich, Goran', 'EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and State University of Campinas/UNICAMP, Campinas, 13081-970, Brazil.|EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and State University of Campinas/UNICAMP, Campinas, 13081-970, Brazil.|EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and.|EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and.|EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and.|EMBRAPA Agriculture Informatics, Computational Biology Research Group, Brazilian Agricultural Research Corporation, Campinas 13083-886, Brazil and.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Amino Acids|Databases, Protein|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw082', '27153716', 'A graphical representation of physicochemical and structural descriptors attributed to amino acid residues occupying the same topological position in different, structurally aligned proteins can provide a more intuitive way to associate possible functional implications to identified variations in structural characteristics. This could be achieved by observing selected characteristics of amino acids and of their corresponding nano environments, described by the numerical value of matching descriptor. For this purpose, a web-based tool called multiple structure single parameter (MSSP) was developed and here presented.'),
(79959, 2016, 'bio', 'FRED 2: an immunoinformatics framework for Python', 'Schubert, Benjamin|Walzer, Mathias|Brachvogel, Hans-Philipp|Szolek, András|Mohr, Christopher|Kohlbacher, Oliver', 'Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany Quantitative Biology Center, Tübingen 72076, Germany Faculty of Medicine, University of Tübingen, Tübingen 72076, Germany Max Planck Institute for Developmental Biology, Biomolecular Interactions, Tübingen 72076, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 19, 'Computational Biology|Epitopes, T-Lymphocyte|Humans|Information Storage and Retrieval|Software|Vaccines', 'Journal Article', '10.1093/bioinformatics/btw113', '27153717', 'Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications.'),
(79960, 2016, 'bio', 'Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects', 'Raimondi, Daniele|Gazzo, Andrea M|Rooman, Marianne|Lenaerts, Tom|Vranken, Wim F', 'Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Universitxc3xa9 Libre De Bruxelles, Brussels 1050, Belgium Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium Structural Biology Research Centre, VIB, Brussels 1050, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Universitxc3xa9 Libre De Bruxelles, Brussels 1050, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium 3BIO-BioInfo Group, Universitxc3xa9 Libre De Bruxelles, Brussels 1050, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Machine Learning Group, Universitxc3xa9 Libre De Bruxelles, Brussels 1050, Belgium Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels 1050, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium Structural Biology Research Centre, VIB, Brussels 1050, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 13, 'Databases, Protein|Genetic Variation|INDEL Mutation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw094', '27153718', 'There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants.'),
(79961, 2016, 'bio', 'MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis', 'Kim, SungHwan|Lin, Chien-Wei|Tseng, George C', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA Department of Statistics, Korea University, Seoul, South Korea.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA Department of Computational and Systems Biology Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States', 14, 'Algorithms|Breast Neoplasms|DNA Methylation|Gene Expression Profiling|Genomics|Humans|Idiopathic Pulmonary Fibrosis|Meta-Analysis as Topic|Prognosis|Research Design|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btw115', '27153719', 'Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.'),
(79962, 2016, 'bio', 'PON-Sol: prediction of effects of amino acid substitutions on protein solubility', 'Yang, Yang|Niroula, Abhishek|Shen, Bairong|Vihinen, Mauno', 'Center for Systems Biology, School of Computer Science and Technology, Soochow University, Suzhou 215006, China and Department of Experimental Medical Science, Lund University, Lund SE 221 84, Sweden.|Department of Experimental Medical Science, Lund University, Lund SE 221 84, Sweden.|Center for Systems Biology.|Department of Experimental Medical Science, Lund University, Lund SE 221 84, Sweden.', 'China|Sweden|-|Sweden', 0, 'Amino Acid Substitution|Computational Biology|Humans|Proteins|Proteomics|Software|Solubility', 'Journal Article', '10.1093/bioinformatics/btw066', '27153720', 'Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases.'),
(79963, 2016, 'bio', 'Haplotype synthesis analysis reveals functional variants underlying known genome-wide associated susceptibility loci', 'Lacour, Andrxc3xa9|Ellinghaus, David|Schreiber, Stefan|Franke, Andre|Becker, Tim', 'German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.|Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany.|Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany.|Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany.|Institute for Community Medicine, Ernst Moritz Arndt University Greifswald, Greifswald 17475, Germany.', 'Germany|Germany|Germany|Germany|Germany', 2, 'Computational Biology|Diabetes Mellitus, Type 2|Genetic Predisposition to Disease|Genome-Wide Association Study|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw125', '27153721', 'The functional mechanisms underlying disease association remain unknown for Genome-wide Association Studies (GWAS) susceptibility variants located outside coding regions. Synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret findings. We define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets, we search for haplotype markers that are highly correlated with associated variants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79964, 2016, 'bio', 'AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms', 'Hosny, Abdelrahman|Vera-Licona, Paola|Laubenbacher, Reinhard|Favre, Thibauld', 'Center for Quantitative Medicine.|Center for Quantitative Medicine Department of Cell Biology Institute for Systems Genomics, UConn Health, CT, USA.|Center for Quantitative Medicine Department of Cell Biology Institute for Systems Genomics, UConn Health, CT, USA Jackson Laboratory for Genomic Medicine, CT, USA.|Democratech, Paris, France.', 'United States|United States|United States|France', 16, 'Algorithms|Computational Biology|Programming Languages|Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btw120', '27153722', 'There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies.'),
(79965, 2016, 'bio', 'Branch: an interactive, web-based tool for testing hypotheses and developing predictive models', 'Gangavarapu, Karthik|Babji, Vyshakh|Meißner, Tobias|Su, Andrew I|Good, Benjamin M', 'Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.', 'United States|United States|United States|United States|United States', 0, 'Biomedical Research|Datasets as Topic|Decision Trees|Humans|Internet|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw117', '27153723', 'Branch is a web application that provides users with the ability to interact directly with large biomedical datasets. The interaction is mediated through a collaborative graphical user interface for building and evaluating decision trees. These trees can be used to compose and test sophisticated hypotheses and to develop predictive models. Decision trees are built and evaluated based on a library of imported datasets and can be stored in a collective area for sharing and re-use.'),
(79966, 2016, 'bio', 'Cas-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cas9', 'Park, Jeongbin|Kim, Jin-Soo|Bae, Sangsu', 'Department of Chemistry, Hanyang University, Seoul 133-791, South Korea.|Center for Genome Engineering, Institute for Basic Science, Seoul 151-747, South Korea Department of Chemistry, Seoul National University, Seoul 151-747, South Korea.|Department of Chemistry, Hanyang University, Seoul 133-791, South Korea Institute for Materials Design, Hanyang University, Seoul 133-791, South Korea.', '-|-|-', 15, 'CRISPR-Cas Systems|Databases, Genetic|Gene Knockout Techniques|Gene Library|Internet|RNA, Guide|Streptococcus pyogenes', 'Journal Article', '10.1093/bioinformatics/btw103', '27153724', 'CRISPR-derived RNA guided endonucleases (RGENs) have been widely used for both gene knockout and knock-in at the level of single or multiple genes. RGENs are now available for forward genetic screens at genome scale, but single guide RNA (sgRNA) selection at this scale is difficult.'),
(79967, 2016, 'bio', 'NMRPro: an integrated web component for interactive processing and visualization of NMR spectra', 'Mohamed, Ahmed|Nguyen, Canh Hao|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.', 'Japan|Japan|Japan', 4, 'Databases, Factual|Image Processing, Computer-Assisted|Internet|Magnetic Resonance Spectroscopy|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btw102', '27153725', 'The popularity of using NMR spectroscopy in metabolomics and natural products has driven the development of an array of NMR spectral analysis tools and databases. Particularly, web applications are well used recently because they are platform-independent and easy to extend through reusable web components. Currently available web applications provide the analysis of NMR spectra. However, they still lack the necessary processing and interactive visualization functionalities. To overcome these limitations, we present NMRPro, a web component that can be easily incorporated into current web applications, enabling easy-to-use online interactive processing and visualization. NMRPro integrates server-side processing with client-side interactive visualization through three parts: a python package to efficiently process large NMR datasets on the server-side, a Django App managing server-client interaction, and SpecdrawJS for client-side interactive visualization.'),
(79968, 2016, 'bio', 'A mutation profile for top-k patient search exploiting Gene-Ontology and orthogonal non-negative matrix factorization', 'Kim, Sungchul|Sael, Lee|Yu, Hwanjo', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.', 'Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw104', '27153726', '-'),
(79969, 2016, 'bio', 'VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules', 'Wala, Jeremiah|Zhang, Cheng-Zhong|Meyerson, Matthew|Beroukhim, Rameen', 'The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.|The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.|The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.|The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 4, 'Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw111', '27153727', 'We developed VariantBam, a Cxe2x80x89++xe2x80x89read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis.'),
(79970, 2016, 'bio', 'IsomiR Bank: a research resource for tracking IsomiRs', 'Zhang, Yuanwei|Zang, Qiguang|Xu, Bo|Zheng, Wei|Ban, Rongjun|Zhang, Huan|Yang, Yifan|Hao, Qiaomei|Iqbal, Furhan|Li, Ao|Shi, Qinghua', 'Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China.|School of Information Science and Biology, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China.|Reproductive Medical Centre of Anhui, Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China.|School of Information Science and Biology, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China.|Department of Statistics, University of Kentucky, Lexington, KY 40536, USA and.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China.|School of Information Science and Biology, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, Anhui, China, Hefei Institute of Physical Science, China Academy of Science, Hefei 230027, China.', 'China|China|China|China|China|China|-|China|China|China|China', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Plants', 'Journal Article', '10.1093/bioinformatics/btw070', '27153728', ': Next-Generation Sequencing (NGS) technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent differences from their corresponding mature reference sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). These isomiRs mainly originate via the imprecise and alternative cleavage during the pre-miRNA processing and post-transcriptional modifications that influence miRNA stability, their sub-cellular localization and target selection. Although several tools for the identification of isomiR have been reported, no bioinformatics resource dedicated to gather isomiRs from public NGS data and to provide functional analysis of these isomiRs is available to date. Thus, a free online database, IsomiR Bank has been created to integrate isomiRs detected by our previously published algorithm CPSS. In total, 2727 samples (Small RNA NGS data downloaded from ArrayExpress) from eight species (Arabidopsis thaliana, Drosophila melanogaster, Danio rerio, Homo sapiens, Mus musculus, Oryza sativa, Solanum lycopersicum and Zea mays) are analyzed. At present, 308 919 isomiRs from 4706 mature miRNAs are collected into IsomiR Bank. In addition, IsomiR Bank provides target prediction and enrichment analysis to evaluate the effects of isomiRs on target selection.'),
(79971, 2016, 'bio', 'UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB', 'Doxc4x9fan, Tunca|MacDougall, Alistair|Saidi, Rabie|Poggioli, Diego|Bateman, Alex|ODonovan, Claire|Martin, Maria J', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Amino Acid Sequence|Databases, Protein|Knowledge Bases|Molecular Sequence Annotation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw114', '27153729', 'Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins.'),
(79972, 2016, 'bio', 'Rapid genotype refinement for whole-genome sequencing data using multi-variate normal distributions', 'Arthur, Rudy|OConnell, Jared|Schulz-Trieglaff, Ole|Cox, Anthony J', 'Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Algorithms|Genotype|Humans|Linkage Disequilibrium|Normal Distribution|Software', 'Journal Article', '10.1093/bioinformatics/btw097', '27153730', 'Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD)-based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed.'),
(79973, 2016, 'bio', 'Efficient privacy-preserving string search and an application in genomics', 'Shimizu, Kana|Nuida, Koji|Rxc3xa4tsch, Gunnar', 'Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan, Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY 1275 York, USA.|Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan and Japan Science and Technology Agency (JST) PRESTO Researcher, Tokyo, Japan.|Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY 1275 York, USA.', 'Japan|Japan|United States', 0, 'Algorithms|Genomics|Privacy', 'Journal Article', '10.1093/bioinformatics/btw050', '27153731', 'Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database.'),
(79974, 2016, 'bio', 'Detection and removal of spatial bias in multiwell assays', 'Lachmann, Alexander|Giorgi, Federico M|Alvarez, Mariano J|Califano, Andrea', 'Department of Biomedical Informatics (DBMI) Department of Systems Biology Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, USA.|Department of Systems Biology Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, USA Scuola Superiore SantAnna, Pisa, Italy.|Department of Systems Biology.|Department of Biomedical Informatics (DBMI) Department of Systems Biology Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, USA Department of Biochemistry and Molecular Biophysics Institute for Cancer Genetics Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.', 'United States|Italy|United States|United States', 0, 'Algorithms|Automation|Bias|Biological Assay|Gene Library|Humans|Microfluidic Analytical Techniques|Principal Component Analysis|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw092', '27153732', 'Multiplex readout assays are now increasingly being performed using microfluidic automation in multiwell format. For instance, the Library of Integrated Network-based Cellular Signatures (LINCS) has produced gene expression measurements for tens of thousands of distinct cell perturbations using a 384-well plate format. This dataset is by far the largest 384-well gene expression measurement assay ever performed. We investigated the gene expression profiles of a million samples from the LINCS dataset and found that the vast majority (96%) of the tested plates were affected by a significant 2D spatial bias.'),
(79975, 2016, 'bio', 'GSP: a web-based platform for designing genome-specific primers in polyploids', 'Wang, Yi|Tiwari, Vijay K|Rawat, Nidhi|Gill, Bikram S|Huo, Naxin|You, Frank M|Coleman-Derr, Devin|Gu, Yong Q', 'USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA USDA-ARS, Plant Gene Expression Center, Albany, CA 94710, USA.|Wheat Genetic Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA.|Wheat Genetic Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA.|Wheat Genetic Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|Cereal Research Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.|USDA-ARS, Plant Gene Expression Center, Albany, CA 94710, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.', 'United States|United States|United States|United States|United States|Canada|United States|United States', 11, 'DNA Primers|Internet|Polyploidy|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw134', '27153733', 'The sequences among subgenomes in a polyploid species have high similarity, making it difficult to design genome-specific primers for sequence analysis.'),
(79976, 2016, 'bio', 'EuGene: maximizing synthetic gene design for heterologous expression', 'Gaspar, Paulo|Luís Oliveira, José|Frommlet, Jörg|Santos, Manuel A S|Moura, Gabriela', 'USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Genes, Synthetic|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw063', '27153734', '-'),
(79977, 2016, 'bio', 'Evaluation of hybrid and non-hybrid methods for de novo assembly of nanopore reads', 'Sovixc4x87, Ivan|Krixc5xbeanovixc4x87, Krexc5xa1imir|Skala, Karolj|xc5xa0ikixc4x87, Mile', 'Centre for Informatics and Computing, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.|Centre for Informatics and Computing, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia Bioinformatics Institute, Singapore 138671, Singapore.', 'Croatia|Croatia|Croatia|Croatia', 26, 'Escherichia coli|Escherichia coli K12|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Nanopores|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw237', '27162186', 'Recent emergence of nanopore sequencing technology set a challenge for established assembly methods. In this work, we assessed how existing hybrid and non-hybrid de novo assembly methods perform on long and error prone nanopore reads.'),
(79978, 2016, 'bio', 'A computational model to predict the immune system activation by citrus-derived vaccine adjuvants', 'Pappalardo, Francesco|Fichera, Epifanio|Paparone, Nicoletta|Lombardo, Alessandro|Pennisi, Marzio|Russo, Giulia|Leotta, Marco|Pappalardo, Francesco|Pedretti, Alessandro|De Fiore, Francesco|Motta, Santo', 'Department of Drug Sciences, University of Catania.|Etna Biotech S.R.L, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.|Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.|Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.|Department of Mathematics and Computer Science, University of Catania.|Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.|Department of Drug Sciences, University of Catania.|Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.|Department of Pharmaceutical Sciences, University of Milan, Milan, Italy.|Softeco Sismat, Italy.|Department of Mathematics and Computer Science, University of Catania.', '-|-|-|-|-|Italy|-|-|Italy|Italy|-', 0, 'Adjuvants, Immunologic|Aged|Antigens|Citrus|Forecasting|Humans|Immune System|Immunocompromised Host|Infant, Newborn|Influenza Vaccines|Patient-Specific Modeling', 'Journal Article', '10.1093/bioinformatics/btw293', '27162187', 'Vaccines represent the most effective and cost-efficient weapons against a wide range of diseases. Nowadays new generation vaccines based on subunit antigens reduce adverse effects in high risk individuals. However, vaccine antigens are often poor immunogens when administered alone. Adjuvants represent a good strategy to overcome such hurdles, indeed they are able to: enhance the immune response, allow antigens sparing, accelerate the specific immune response, and increase vaccine efficacy in vulnerable groups such as newborns, elderly or immuno-compromised people. However, due to safety concerns and adverse reactions, there are only a few adjuvants approved for use in humans. Moreover, in practice current adjuvants sometimes fail to confer adequate stimulation. Hence, there is an imperative need to develop novel adjuvants that overcome the limitations of the currently available licensed adjuvants.'),
(79979, 2016, 'bio', 'SimLoRD: Simulation of Long Read Data', 'Stöcker, Bianca K|Köster, Johannes|Rahmann, Sven', 'Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen, Essen, 45147, Germany.|Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam 1098 XG, The Netherlands Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.|Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen, Essen, 45147, Germany.', 'Germany|Netherlands|Germany', 0, 'Computer Simulation|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw286', '27166244', 'Third generation sequencing methods provide longer reads than second generation methods and have distinct error characteristics. While there exist many read simulators for second generation data, there is a very limited choice for third generation data.'),
(79980, 2016, 'bio', 'Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration', 'Lobo, Daniel|Morokuma, Junji|Levin, Michael', 'Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.|Center for Regenerative and Developmental Biology Department of Biology, Tufts University, Medford, MA 02155, USA.|Center for Regenerative and Developmental Biology Department of Biology, Tufts University, Medford, MA 02155, USA.', 'United States|United States|United States', 0, 'Animals|Computational Biology|Computer Graphics|Databases, Factual|Genes, Regulator|Hepatocyte Nuclear Factor 4|Humans|Models, Biological|Planarians|Regeneration|Software', 'Journal Article', '10.1093/bioinformatics/btw299', '27166245', 'Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology.'),
(79981, 2016, 'bio', 'SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences', 'Pickett, B D|Karlinsey, S M|Penrod, C E|Cormier, M J|Ebbert, M T W|Shiozawa, D K|Whipple, C J|Ridge, P G', 'Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Databases, Nucleic Acid|Genetic Markers|Microsatellite Repeats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw298', '27170037', 'Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software.'),
(79982, 2016, 'bio', 'A two-part mixed-effects model for analyzing longitudinal microbiome compositional data', 'Chen, Eric Z|Li, Hongzhe', 'Genomics and Computational Biology Graduate Group Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Genomics and Computational Biology Graduate Group Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States', 0, 'Humans|Logistic Models|Microbiota|Models, Statistical|Regression Analysis|Research Design', 'Journal Article', '10.1093/bioinformatics/btw308', '27187200', 'The human microbial communities are associated with many human diseases such as obesity, diabetes and inflammatory bowel disease. High-throughput sequencing technology has been widely used to quantify the microbial composition in order to understand its impacts on human health. Longitudinal measurements of microbial communities are commonly obtained in many microbiome studies. A key question in such microbiome studies is to identify the microbes that are associated with clinical outcomes or environmental factors. However, microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures in longitudinal studies are correlated. A method that takes into account these features is needed for association analysis in longitudinal microbiome data.'),
(79983, 2016, 'bio', 'Top-down analysis of protein samples by de novo sequencing techniques', 'Vyatkina, Kira|Wu, Si|Dekker, Lennard J M|VanDuijn, Martijn M|Liu, Xiaowen|Tolixc4x87, Nikola|Luider, Theo M|Paxc5xa1a-Tolixc4x87, Ljiljana|Pevzner, Pavel A', 'Algorithmic Biology Laboratory, Saint Petersburg Academic University, St Petersburg, Russia Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, St Petersburg, Russia.|Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA.|Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands.|Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.|Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, St Petersburg, Russia Department of Computer Science and Engineering, University of California, San Diego, CA, USA.', 'United States|United States|Netherlands|Netherlands|India|United States|Netherlands|United States|United States', 7, 'Algorithms|Amino Acid Sequence|Protein Processing, Post-Translational|Proteins|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw307', '27187201', 'Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data.'),
(79984, 2016, 'bio', 'Calculating and scoring high quality multiple flexible protein structure alignments', 'Ritchie, David W', 'Inria Nancy - Grand Est, 54600 Villers-lès-Nancy, France.', 'France', 0, 'Algorithms|Models, Statistical|Normal Distribution|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw300', '27187202', 'Calculating multiple protein structure alignments (MSAs) is important for understanding functional and evolutionary relationships between protein families, and for modeling protein structures by homology. While incorporating backbone flexibility promises to circumvent many of the limitations of rigid MSA algorithms, very few flexible MSA algorithms exist today. This article describes several novel improvements to the Kpax algorithm which allow high quality flexible MSAs to be calculated. This article also introduces a new Gaussian-based MSA quality measure called M-score, which circumvents the pitfalls of RMSD-based quality measures.'),
(79985, 2016, 'bio', 'A simple yet accurate correction for winners curse can predict signals discovered in much larger genome scans', 'Bigdeli, T Bernard|Lee, Donghyung|Webb, Bradley Todd|Riley, Brien P|Vladimirov, Vladimir I|Fanous, Ayman H|Kendler, Kenneth S|Bacanu, Silviu-Alin', 'Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics Center for Biomarker Research & Personalized Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD 21205, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Bias|Data Interpretation, Statistical|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw303', '27187203', 'For genetic studies, statistically significant variants explain far less trait variance than sub-threshold association signals. To dimension follow-up studies, researchers need to accurately estimate true effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winners curse biases, which are reduced only by laborious winners curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities.'),
(79986, 2016, 'bio', 'seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data', 'Kolde, Raivo|Märtens, Kaspar|Lokk, Kaie|Laur, Sven|Vilo, Jaak', 'Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA.|Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.|Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia.|Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.|Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.', 'Estonia|Estonia|Estonia|Estonia|Estonia', 0, 'DNA Methylation|Datasets as Topic|Genome|Genomics|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1093/bioinformatics/btw304', '27187204', 'One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach.'),
(79987, 2016, 'bio', 'PRESS: PRotEin S-Sulfenylation server', 'Sakka, Marianna|Tzortzis, Grigorios|Mantzaris, Michalis D|Bekas, Nick|Kellici, Tahsin F|Likas, Aristidis|Galaris, Dimitrios|Gerothanassis, Ioannis P|Tzakos, Andreas G', 'Department of Chemistry.|Department of Computer Science and Engineering.|Laboratory of Biological Chemistry Faculty of Medicine, University of Ioannina, Ioannina, Greece.|Department of Chemistry.|Department of Chemistry.|Department of Computer Science and Engineering.|Laboratory of Biological Chemistry Faculty of Medicine, University of Ioannina, Ioannina, Greece.|Department of Chemistry.|Department of Chemistry.', '-|-|Greece|-|-|-|Greece|-|-', 0, 'Computer Simulation|Cysteine|Humans|Oxidation-Reduction|Protein Processing, Post-Translational|Proteins|Sequence Analysis, Protein|Sulfhydryl Compounds|Sulfur Acids', 'Journal Article', '10.1093/bioinformatics/btw301', '27187205', 'Transient S-sulfenylation of cysteine thiols mediated by reactive oxygen species plays a critical role in pathology, physiology and cell signaling. Therefore, discovery of new S-sulfenylated sites in proteins is of great importance towards understanding how protein function is regulated upon redox conditions.'),
(79988, 2016, 'bio', 'SoFIA: a data integration framework for annotating high-throughput datasets', 'Childs, Liam Harold|Mamlouk, Soulafa|Brandt, Jörgen|Sers, Christine|Leser, Ulf', 'Wissenmanagement in der Bioinformatik, Humboldt-Universität zu Berlin, Berlin, Germany.|DKTK Deutsches Konsortium Für Translationale Krebsforschung, Partner site Charite Berlin, Berlin, Germany.|Wissenmanagement in der Bioinformatik, Humboldt-Universität zu Berlin, Berlin, Germany.|DKTK Deutsches Konsortium Für Translationale Krebsforschung, Partner site Charite Berlin, Berlin, Germany.|Wissenmanagement in der Bioinformatik, Humboldt-Universität zu Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 4, 'Data Curation|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btw302', '27187206', 'Integrating heterogeneous datasets from several sources is a common bioinformatics task that often requires implementing a complex workflow intermixing database access, data filtering, format conversions, identifier mapping, among further diverse operations. Data integration is especially important when annotating next generation sequencing data, where a multitude of diverse tools and heterogeneous databases can be used to provide a large variety of annotation for genomic locations, such a single nucleotide variants or genes. Each tool and data source is potentially useful for a given project and often more than one are used in parallel for the same purpose. However, software that always produces all available data is difficult to maintain and quickly leads to an excess of data, creating an information overload rather than the desired goal-oriented and integrated result.'),
(79989, 2016, 'bio', 'PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation', 'Tang, Haiming|Thomas, Paul D', 'Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033, USA.|Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033, USA.', 'United States|United States', 0, 'Evolution, Molecular|Genetic Variation|Humans|Phylogeny|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw222', '27193693', 'PANTHER-PSEP is a new software tool for predicting non-synonymous genetic variants that may play a causal role in human disease. Several previous variant pathogenicity prediction methods have been proposed that quantify evolutionary conservation among homologous proteins from different organisms. PANTHER-PSEP employs a related but distinct metric based on evolutionary preservation: homologous proteins are used to reconstruct the likely sequences of ancestral proteins at nodes in a phylogenetic tree, and the history of each amino acid can be traced back in time from its current state to estimate how long that state has been preserved in its ancestors. Here, we describe the PSEP tool, and assess its performance on standard benchmarks for distinguishing disease-associated from neutral variation in humans. On these benchmarks, PSEP outperforms not only previous tools that utilize evolutionary conservation, but also several highly used tools that include multiple other sources of information as well. For predicting pathogenic human variants, the trace back of course starts with a human reference protein sequence, but the PSEP tool can also be applied to predicting deleterious or pathogenic variants in reference proteins from any of the xe2x88xbc100 other species in the PANTHER database.'),
(79990, 2016, 'bio', 'TreesimJ: a flexible, forward time population genetic simulator', 'OFallon, Brendan', 'Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw236', '27197814', '-'),
(79991, 2016, 'bio', 'PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances', 'Tang, Zheng-Zheng|Chen, Guanhua|Alekseyenko, Alexander V', 'Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.|Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.|Biomedical Informatics Center, Department of Public Health Sciences and Department of Oral Health Sciences, Medical University of South Carolina, Charleston, SC 29403, USA.', 'United States|United States|United States', 0, 'Analysis of Variance|Computer Simulation|High-Throughput Nucleotide Sequencing|Humans|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btw311', '27197815', 'Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings.'),
(79992, 2016, 'bio', 'Complex heatmaps reveal patterns and correlations in multidimensional genomic data', 'Gu, Zuguang|Eils, Roland|Schlesner, Matthias', 'Division of Theoretical Bioinformatics Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Theoretical Bioinformatics Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany.|Division of Theoretical Bioinformatics.', 'Germany|Germany|-', 675, 'Computer Graphics|Gene Expression|Genomics|Humans|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btw313', '27207943', 'Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.'),
(79993, 2016, 'bio', 'Differential rhythmicity: detecting altered rhythmicity in biological data', 'Thaben, Paul F|Westermark, Pål O', 'Institute for Theoretical Biology, Charité - Universitätsmedizin Berlin, D-10115 Berlin, Germany.|Institute for Theoretical Biology, Charité - Universitätsmedizin Berlin, D-10115 Berlin, Germany.', 'Germany|Germany', 0, 'Animals|Biological Clocks|Circadian Clocks|Circadian Rhythm|Darkness|Gene Expression Regulation|Light|Liver|Mice|Models, Statistical|Photoperiod|RNA', 'Journal Article', '10.1093/bioinformatics/btw309', '27207944', 'Biological rhythms, such as rhythms in gene expression controlled by the cell cycle or the circadian clock, are important in cell physiology. A common type of experiment compares rhythmicity in tissues or cells either kept under different conditions or having different genotypes. Such investigations provide insights into underlying mechanisms as well as functions of rhythms.'),
(79994, 2016, 'bio', 'TarPmiR: a new approach for microRNA target site prediction', 'Ding, Jun|Li, Xiaoman|Hu, Haiyan', 'Department of Electrical Engineering and Computer Science.|Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL 32816, USA.|Department of Electrical Engineering and Computer Science.', 'United States|United States|United States', 41, 'Algorithms|Animals|Binding Sites|Computational Biology|Gene Expression Regulation|High-Throughput Screening Assays|Humans|Mice|MicroRNAs|Software', 'Journal Article', '10.1093/bioinformatics/btw318', '27207945', 'The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods.'),
(79995, 2016, 'bio', 'Weighted mutual information analysis substantially improves domain-based functional network models', 'Shim, Jung Eun|Lee, Insuk', 'Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.|Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.', '-|-', 0, 'Algorithms|Computational Biology|Databases, Protein|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Protein Interaction Maps|Proteins', 'Journal Article', '10.1093/bioinformatics/btw320', '27207946', 'Functional protein-protein interaction (PPI) networks elucidate molecular pathways underlying complex phenotypes, including those of human diseases. Extrapolation of domain-domain interactions (DDIs) from known PPIs is a major domain-based method for inferring functional PPI networks. However, the protein domain is a functional unit of the protein. Therefore, we should be able to effectively infer functional interactions between proteins based on the co-occurrence of domains.'),
(79996, 2016, 'bio', 'methylFlow: cell-specific methylation pattern reconstruction from high-throughput bisulfite-converted DNA sequencing', 'Dorri, Faezeh|Mendelowitz, Lee|Corrada Bravo, Héctor', 'Center for Bioinformatics and Computational Biology Department of Computer Science.|Center for Bioinformatics and Computational Biology Applied Mathematics, Statistics and Scientific Compoutation Program, University of Maryland, College Park, MD 20745, USA.|Center for Bioinformatics and Computational Biology Department of Computer Science.', 'United States|United States|United States', 0, 'DNA Methylation|Epigenomics|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Sulfites', 'Journal Article', '10.1093/bioinformatics/btw287', '27246923', 'DNA methylation aberrations are now known to, almost universally, accompany the initiation and progression of cancers. In particular, the colon cancer epigenome contains specific genomic regions that, along with differences in methylation levels with respect to normal colon tissue, also show increased epigenetic and gene expression heterogeneity at the population level, i.e. across tumor samples, in comparison with other regions in the genome. Tumors are highly heterogeneous at the clonal level as well, and the relationship between clonal and population heterogeneity is poorly understood.');
