INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77664, 2013, 'bio', 'Identification of deleterious synonymous variants in human genomes', 'Buske, Orion J|Manickaraj, AshokKumar|Mital, Seema|Ray, Peter N|Brudno, Michael', 'Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.|Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.|Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.|Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.|Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Artificial Intelligence|Ciliary Motility Disorders|Computer Simulation|Disease|Encephalocele|Exons|Genome, Human|Genomics|Humans|Mutation|Polycystic Kidney Diseases|Polymorphism, Genetic|Retinitis Pigmentosa', 'Journal Article', '10.1093/bioinformatics/btt308', '23736532', 'The prioritization and identification of disease-causing mutations is one of the most significant challenges in medical genomics. Currently available methods address this problem for non-synonymous single nucleotide variants (SNVs) and variation in promoters/enhancers, however, recent research has implicated synonymous (silent) exonic mutations in a number of disorders.'),
(77665, 2013, 'bio', 'EcoGene-RefSeq: EcoGene tools applied to the RefSeq prokaryotic genomes', 'Zhou, Jindan|Richardson, Andrew J|Rudd, Kenneth E', 'Department of Biochemistry and Molecular Biology, The Miller School of Medicine at the University of Miami, Miami, FL 33136, USA. j.zhou1@miami.edu|Department of Biochemistry and Molecular Biology, The Miller School of Medicine at the University of Miami, Miami, FL 33136, USA. j.zhou1@miami.edu|Department of Biochemistry and Molecular Biology, The Miller School of Medicine at the University of Miami, Miami, FL 33136, USA. j.zhou1@miami.edu', 'United States|United States|United States', 0, 'Databases, Nucleic Acid|Escherichia coli K12|Genome, Archaeal|Genome, Bacterial|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt302', '23736533', 'EcoGene.org is a genome database and website dedicated to Escherichia coli K-12 substrain MG1655 that is revised daily using information derived from the biomedical literature and in-house analysis. EcoGene is a major source of annotation updates for the MG1655 Genbank record, one of only a few Genbank genome records that are updated by a community effort. The Reference Sequence (RefSeq) database, built by The National Center for Biotechnology Information, comprises a set of duplicate Genbank genome records that can be modified by the NCBI staff annotators. EcoGene-RefSeq is being developed as a stand-alone internet resource to facilitate the usage of EcoGene-based tools on any of the >2400 completed prokaryotic genome records that are currently available at the RefSeq database.'),
(77666, 2013, 'bio', 'Insights into polypharmacology from drug-domain associations', 'Moya-García, Aurelio A|Ranea, Juan A G', 'Department of Molecular Biology and Biochemistry, University of Malaga, Málaga, Spain.|Department of Molecular Biology and Biochemistry, University of Malaga, Málaga, Spain.', 'Spain|Spain', 16, 'Humans|Phylogeny|Polypharmacology|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btt321', '23740740', 'Polypharmacology (the ability of a single drug to affect multiple targets) is a key feature that may explain part of the decreasing success of conventional drug discovery strategies driven by the quest for drugs to act selectively on a single target. Most drug targets are proteins that are composed of domains (their structural and functional building blocks).'),
(77667, 2013, 'bio', 'DIGEP-Pred: web service for in silico prediction of drug-induced gene expression profiles based on structural formula', 'Lagunin, Alexey|Ivanov, Sergey|Rudik, Anastasia|Filimonov, Dmitry|Poroikov, Vladimir', 'Laboratory for Structure-Function Based Drug Design, Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow, Russia. alexey.lagunin@ibmc.msk.ru|Laboratory for Structure-Function Based Drug Design, Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow, Russia. alexey.lagunin@ibmc.msk.ru|Laboratory for Structure-Function Based Drug Design, Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow, Russia. alexey.lagunin@ibmc.msk.ru|Laboratory for Structure-Function Based Drug Design, Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow, Russia. alexey.lagunin@ibmc.msk.ru|Laboratory for Structure-Function Based Drug Design, Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow, Russia. alexey.lagunin@ibmc.msk.ru', '-|-|-|-|-', 0, 'Computer Simulation|Databases, Genetic|Internet|Pharmaceutical Preparations|Proteins|RNA, Messenger|Software|Structure-Activity Relationship|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt322', '23740741', 'Experimentally found gene expression profiles are used to solve different problems in pharmaceutical studies, such as drug repositioning, resistance, toxicity and drug-drug interactions. A special web service, DIGEP-Pred, for prediction of drug-induced changes of gene expression profiles based on structural formulae of chemicals has been developed. Structure-activity relationships for prediction of drug-induced gene expression profiles were determined by Prediction of Activity Spectra for Substances (PASS) software. Comparative Toxicogenomics Database with data on the known drug-induced gene expression profiles of chemicals was used to create mRNA- and protein-based training sets. An average prediction accuracy for the training sets (ROC AUC) calculated by leave-one-out cross-validation on the basis of mRNA data (1385 compounds, 952 genes, 500 up- and 475 down-regulations) and protein data (1451 compounds, 139 genes, 93 up- and 55 down-regulations) exceeded 0.85.'),
(77668, 2013, 'bio', 'WhichCyp: prediction of cytochromes P450 inhibition', 'Rostkowski, Michal|Spjuth, Ola|Rydberg, Patrik', 'Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, Copenhagen, Denmark.|Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, Copenhagen, Denmark.|Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, Copenhagen, Denmark.', 'Denmark|Denmark|Denmark', 0, 'Cytochrome P-450 Enzyme Inhibitors|Enzyme Inhibitors|Internet|Isoenzymes|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt325', '23740742', 'In this work we present WhichCyp, a tool for prediction of which cytochromes P450 isoforms (among 1A2, 2C9, 2C19, 2D6 and 3A4) a given molecule is likely to inhibit. The models are built from experimental high-throughput data using support vector machines and molecular signatures.'),
(77669, 2013, 'bio', 'Alternative polyadenylation sites reveal distinct chromatin accessibility and histone modification in human cell lines', 'Lee, Che-yu|Chen, Liang', 'Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States', 0, 'Cell Line|Chromatin|Deoxyribonuclease I|Histones|Humans|K562 Cells|Nucleosomes|Polyadenylation', 'Journal Article', '10.1093/bioinformatics/btt288', '23740743', 'In addition to alternative splicing, alternative polyadenylation has also been identified as a critical and prevalent regulatory mechanism in human gene expression. However, the mechanism of alternative polyadenylation selection and the involved factors is still largely unknown.'),
(77670, 2013, 'bio', 'opm: an R package for analysing OmniLog(R) phenotype microarray data', 'Vaas, Lea A I|Sikorski, Johannes|Hofner, Benjamin|Fiebig, Anne|Buddruhs, Nora|Klenk, Hans-Peter|Göker, Markus', 'CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.|CBS-KNAW Fungal Biodiversity Centre, Bioinformatics, Uppsalalaan 8. 3584 CT Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btt291', '23740744', 'opm is an R package designed to analyse multidimensional OmniLog® phenotype microarray (PM) data. opm provides management, visualization and statistical analysis of PM data, including curve-parameter estimation and discretization, dedicated and customizable plots, metadata management, automated generation of textual and tabular reports, mapping of substrates to databases, batch conversion of files and export to phylogenetic software in the YAML markup language.'),
(77671, 2013, 'bio', 'Triplex-Inspector: an analysis tool for triplex-mediated targeting of genomic loci', 'Buske, Fabian A|Bauer, Denis C|Mattick, John S|Bailey, Timothy L', 'Division of Genomics and Computational Biology, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.|Division of Genomics and Computational Biology, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.|Division of Genomics and Computational Biology, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.|Division of Genomics and Computational Biology, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.', 'Australia|Australia|Australia|Australia', 11, 'DNA|Gene Targeting|Genetic Loci|Genomics|Humans|Peptide Nucleic Acids|Software', 'Journal Article', '10.1093/bioinformatics/btt315', '23740745', 'At the heart of many modern biotechnological and therapeutic applications lies the need to target specific genomic loci with pinpoint accuracy. Although landmark experiments demonstrate technological maturity in manufacturing and delivering genetic material, the genomic sequence analysis to find suitable targets lags behind. We provide a computational aid for the sophisticated design of sequence-specific ligands and selection of appropriate targets, taking gene location and genomic architecture into account.'),
(77672, 2013, 'bio', 'Exploration of conformational transition pathways from coarse-grained simulations', 'Sfriso, Pedro|Hospital, Adam|Emperador, Agustí|Orozco, Modesto', 'Institute for Research in Biomedicine (IRB Barcelona), Joint IRB-BSC Program in Computational Biology, Baldiri Reixac 10, Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), Joint IRB-BSC Program in Computational Biology, Baldiri Reixac 10, Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), Joint IRB-BSC Program in Computational Biology, Baldiri Reixac 10, Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), Joint IRB-BSC Program in Computational Biology, Baldiri Reixac 10, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Ligands|Molecular Dynamics Simulation|Protein Conformation', 'Journal Article', '10.1093/bioinformatics/btt324', '23740746', 'A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, …) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD).'),
(77673, 2013, 'bio', 'Pathway Processor 20: a web resource for pathway-based analysis of high-throughput data', 'Beltrame, Luca|Bianco, Luca|Fontana, Paolo|Cavalieri, Duccio', 'Translational Genomics Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20159 Milano, Italy.|Translational Genomics Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20159 Milano, Italy.|Translational Genomics Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20159 Milano, Italy.|Translational Genomics Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20159 Milano, Italy.', 'Italy|Italy|Italy|Italy', 4, 'Animals|High-Throughput Nucleotide Sequencing|Humans|Internet|Mice|Oligonucleotide Array Sequence Analysis|Rats|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt292', '23740747', 'Pathway Processor 2.0 is a web application designed to analyze high-throughput datasets, including but not limited to microarray and next-generation sequencing, using a pathway centric logic. In addition to well-established methods such as the Fishers test and impact analysis, Pathway Processor 2.0 offers innovative methods that convert gene expression into pathway expression, leading to the identification of differentially regulated pathways in a dataset of choice.'),
(77674, 2013, 'bio', 'GSATools: analysis of allosteric communication and functional local motions using a structural alphabet', 'Pandini, Alessandro|Fornili, Arianna|Fraternali, Franca|Kleinjung, Jens', 'Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK. alessandro.pandini@kcl.ac.uk|Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK. alessandro.pandini@kcl.ac.uk|Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK. alessandro.pandini@kcl.ac.uk|Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK. alessandro.pandini@kcl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 26, 'Allosteric Regulation|Motion|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btt326', '23740748', 'GSATools is a free software package to analyze conformational ensembles and to detect functional motions in proteins by means of a structural alphabet. The software integrates with the widely used GROMACS simulation package and can generate a range of graphical outputs. Three applications can be supported: (i) investigation of the conformational variability of local structures, (ii) detection of allosteric communication, and (iii) identification of local regions that are critical for global functional motions. These analyses provide insights into the dynamics of proteins and allow for targeted design of functional mutants in theoretical and experimental studies.'),
(77675, 2013, 'bio', 'An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues', 'Xu, Ying-Ying|Yang, Fan|Zhang, Yang|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, 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, 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, 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, China.', 'China|China|China|China', 0, 'Algorithms|Biomarkers, Tumor|Humans|Immunohistochemistry|Neoplasm Proteins|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt320', '23740749', 'Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer.'),
(77676, 2013, 'bio', 'Pathview: an R/Bioconductor package for pathway-based data integration and visualization', 'Luo, Weijun|Brouwer, Cory', 'Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC 28223, USA. luo_weijun@yahoo.com|Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC 28223, USA. luo_weijun@yahoo.com', 'United States|United States', 0, 'Computer Graphics|Gene Expression|Metabolic Networks and Pathways|Signal Transduction|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btt285', '23740750', 'Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines.'),
(77677, 2013, 'bio', 'iAnn: an event sharing platform for the life sciences', 'Jimenez, Rafael C|Albar, Juan P|Bhak, Jong|Blatter, Marie-Claude|Blicher, Thomas|Brazas, Michelle D|Brooksbank, Cath|Budd, Aidan|De Las Rivas, Javier|Dreyer, Jacqueline|van Driel, Marc A|Dunn, Michael J|Fernandes, Pedro L|van Gelder, Celia W G|Hermjakob, Henning|Ioannidis, Vassilios|Judge, David P|Kahlem, Pascal|Korpelainen, Eija|Kraus, Hans-Joachim|Loveland, Jane|Mayer, Christine|McDowall, Jennifer|Moran, Federico|Mulder, Nicola|Nyronen, Tommi|Rother, Kristian|Salazar, Gustavo A|Schneider, Reinhard|Via, Allegra|Villaveces, Jose M|Yu, Ping|Schneider, Maria V|Attwood, Teresa K|Corpas, Manuel', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 3, 'Anniversaries and Special Events|Biological Science Disciplines|Congresses as Topic|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt306', '23742982', 'We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available.'),
(77678, 2013, 'bio', 'REDItools: high-throughput RNA editing detection made easy', 'Picardi, Ernesto|Pesole, Graziano', 'Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari, 70125 Bari, Italy. ernesto.picardi@uniba.it|Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari, 70125 Bari, Italy. ernesto.picardi@uniba.it', 'Italy|Italy', 0, 'High-Throughput Nucleotide Sequencing|Programming Languages|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btt287', '23742983', 'The reliable detection of RNA editing sites from massive sequencing data remains challenging and, although several methodologies have been proposed, no computational tools have been released to date. Here, we introduce REDItools a suite of python scripts to perform high-throughput investigation of RNA editing using next-generation sequencing data.'),
(77679, 2013, 'bio', 'Selection of human tissue-specific elementary flux modes using gene expression data', 'Rezola, Alberto|Pey, Jon|de Figueiredo, Luis F|Podhorski, Adam|Schuster, Stefan|Rubio, Angel|Planes, Francisco J', 'Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.|Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 21, 'Algorithms|Gene Expression|Genome, Human|Humans|Liver|Metabolic Networks and Pathways|Organ Specificity|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt328', '23742984', 'The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising.'),
(77680, 2013, 'bio', 'Automated design of paralogue ratio test assays for the accurate and rapid typing of copy number variation', 'Veal, Colin D|Xu, Hang|Reekie, Katherine|Free, Robert|Hardwick, Robert J|McVey, David|Brookes, Anthony J|Hollox, Edward J|Talbot, Christopher J', 'Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.|Department of Genetics, University of Leicester, Leicester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Comparative Genomic Hybridization|DNA Copy Number Variations|Gene Dosage|Genome, Human|Genomics|Genotyping Techniques|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btt330', '23742985', 'Genomic copy number variation (CNV) can influence susceptibility to common diseases. High-throughput measurement of gene copy number on large numbers of samples is a challenging, yet critical, stage in confirming observations from sequencing or array Comparative Genome Hybridization (CGH). The paralogue ratio test (PRT) is a simple, cost-effective method of accurately determining copy number by quantifying the amplification ratio between a target and reference amplicon. PRT has been successfully applied to several studies analyzing common CNV. However, its use has not been widespread because of difficulties in assay design.'),
(77681, 2013, 'bio', 'Inferring disease association using clinical factors in a combinatorial manner and their use in drug repositioning', 'Jung, Jinmyung|Lee, Doheon', 'Department of Bio and Brain Engineering, Korea Advanced Institutre of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea. jmjung.kr@gmail.com|Department of Bio and Brain Engineering, Korea Advanced Institutre of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea. jmjung.kr@gmail.com', '-|-', 0, 'Algorithms|Disease|Drug Repositioning|Humans|Nutrition Surveys|Risk Factors', 'Journal Article', '10.1093/bioinformatics/btt327', '23749956', 'Complex physiological relationships exist among human diseases. Thus, the identification of disease associations could provide new methods of disease care and diagnosis. To this end, numerous studies have investigated disease associations. However, combinatorial effect of physiological factors, which is the main characteristic of biological systems, has not been considered in most previous studies.'),
(77682, 2013, 'bio', 'Differential network analysis for the identification of condition-specific pathway activity and regulation', 'Gambardella, Gennaro|Moretti, Maria Nicoletta|de Cegli, Rossella|Cardone, Luca|Peron, Adriano|di Bernardo, Diego', 'The Telethon Institute of Genetics and Medicine TIGEM.|The Telethon Institute of Genetics and Medicine TIGEM.|The Telethon Institute of Genetics and Medicine TIGEM.|The Telethon Institute of Genetics and Medicine TIGEM.|The Telethon Institute of Genetics and Medicine TIGEM.|The Telethon Institute of Genetics and Medicine TIGEM.', '-|-|-|-|-|-', 0, 'Animals|Carcinoma, Hepatocellular|Cell Line, Tumor|Cells, Cultured|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Hepatocytes|Humans|Liver Neoplasms|Metabolic Networks and Pathways|Mice|Organ Specificity|Transcription Factors|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btt290', '23749957', 'Identification of differential expressed genes has led to countless new discoveries. However, differentially expressed genes are only a proxy for finding dysregulated pathways. The problem is to identify how the network of regulatory and physical interactions rewires in different conditions or in disease.'),
(77683, 2013, 'bio', 'PurBayes: estimating tumor cellularity and subclonality in next-generation sequencing data', 'Larson, Nicholas B|Fridley, Brooke L', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55901, USA. larson.nicholas@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55901, USA. larson.nicholas@mayo.edu', 'United States|United States', 0, 'Bayes Theorem|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt293', '23749958', 'We have developed a novel Bayesian method, PurBayes, to estimate tumor purity and detect intratumor heterogeneity based on next-generation sequencing data of paired tumor-normal tissue samples, which uses finite mixture modeling methods. We demonstrate our approach using simulated data and discuss its performance under varying conditions.'),
(77684, 2013, 'bio', 'SVGenes: a library for rendering genomic features in scalable vector graphic format', 'Etherington, Graham J|MacLean, Daniel', 'The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.|The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.', 'United Kingdom|United Kingdom', 0, 'Computer Graphics|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt294', '23749959', 'Drawing genomic features in attractive and informative ways is a key task in visualization of genomics data. Scalable Vector Graphics (SVG) format is a modern and flexible open standard that provides advanced features including modular graphic design, advanced web interactivity and animation within a suitable client. SVGs do not suffer from loss of image quality on re-scaling and provide the ability to edit individual elements of a graphic on the whole object level independent of the whole image. These features make SVG a potentially useful format for the preparation of publication quality figures including genomic objects such as genes or sequencing coverage and for web applications that require rich user-interaction with the graphical elements.'),
(77685, 2013, 'bio', 'Network2Canvas: network visualization on a canvas with enrichment analysis', 'Tan, Christopher M|Chen, Edward Y|Dannenfelser, Ruth|Clark, Neil R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.', 'United States|United States|United States|United States|United States', 26, 'Animals|Computer Graphics|Drug-Related Side Effects and Adverse Reactions|Embryonic Stem Cells|Gene Library|Gene Regulatory Networks|Humans|Internet|Mice|Pharmaceutical Preparations|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt319', '23749960', 'Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than xe2x88xbc100 nodes and xe2x88xbc200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a nodes brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses.'),
(77686, 2013, 'bio', 'NOA: a cytoscape plugin for network ontology analysis', 'Zhang, Chao|Wang, Jiguang|Hanspers, Kristina|Xu, Dong|Chen, Luonan|Pico, Alexander R', 'Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States|United States|United States|United States', 18, 'Algorithms|Cell Cycle|Disease|Gene Ontology|Gene Regulatory Networks|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btt334', '23749961', 'The Network Ontology Analysis (NOA) plugin for Cytoscape implements the NOA algorithm for network-based enrichment analysis, which extends Gene Ontology annotations to network links, or edges. The plugin facilitates the annotation and analysis of one or more networks in Cytoscape according to user-defined parameters. In addition to tables, the NOA plugin also presents results in the form of heatmaps and overview networks in Cytoscape, which can be exported for publication figures.'),
(77687, 2013, 'bio', 'TASUKE: a web-based visualization program for large-scale resequencing data', 'Kumagai, Masahiko|Kim, Jungsok|Itoh, Ryutaro|Itoh, Takeshi', 'Bioinformatics Research Unit, Agrogenomics Research Center, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-8602, Japan.|Bioinformatics Research Unit, Agrogenomics Research Center, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-8602, Japan.|Bioinformatics Research Unit, Agrogenomics Research Center, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-8602, Japan.|Bioinformatics Research Unit, Agrogenomics Research Center, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-8602, Japan.', 'Japan|Japan|Japan|Japan', 22, 'Computer Graphics|Genetic Variation|Genome, Human|Genome, Plant|High-Throughput Nucleotide Sequencing|Humans|Internet|Oryza|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt295', '23749962', 'Because an enormous amount of sequence data is being collected, a method to effectively display sequence variation information is urgently needed. tasuke is a web application that visualizes large-scale resequencing data generated by next-generation sequencing technologies and is suitable for rapid data release to the public on the web. The variation and read depths of multiple genomes, as well as annotations, can be shown simultaneously at various scales. We demonstrate the use of TASUKE by applying it to 50 rice and 100 human genome resequencing datasets.'),
(77688, 2013, 'bio', 'Joint network and node selection for pathway-based genomic data analysis', 'Zhe, Shandian|Naqvi, Syed A Z|Yang, Yifan|Qi, Yuan', 'Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Carcinoma, Pancreatic Ductal|Colorectal Neoplasms|Databases, Factual|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Humans|Lymphoma, Large B-Cell, Diffuse|Pancreatic Neoplasms', 'Journal Article', '10.1093/bioinformatics/btt335', '23749986', 'By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.'),
(77689, 2013, 'bio', 'DMEAS: DNA methylation entropy analysis software', 'He, Jianlin|Sun, Xinxi|Shao, Xiaojian|Liang, Liji|Xie, Hehuang', 'Center in Computation Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Center in Computation Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Center in Computation Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Center in Computation Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Center in Computation Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China', 15, 'DNA Methylation|Entropy|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btt332', '23749987', 'DMEAS is the first user-friendly tool dedicated to analyze the distribution of DNA methylation patterns for the quantification of epigenetic heterogeneity. It supports the analysis of both locus-specific and genome-wide bisulfite sequencing data. DMEAS progressively scans the mapping results of bisulfite sequencing reads to extract DNA methylation patterns for contiguous CpG dinucleotides. It determines the DNA methylation level and calculates methylation entropy for genomic segments to enable the quantitative assessment of DNA methylation variations observed in cell populations.'),
(77690, 2014, 'bio', 'Using Genome Query Language to uncover genetic variation', 'Kozanitis, Christos|Heiberg, Andrew|Varghese, George|Bafna, Vineet', 'Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92123 and Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA.|Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92123 and Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA.|Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92123 and Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA.|Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92123 and Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA.', 'United States|United States|United States|United States', 17, 'Algorithms|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt250', '23751181', 'With high-throughput DNA sequencing costs dropping <$1000 for human genomes, data storage, retrieval and analysis are the major bottlenecks in biological studies. To address the large-data challenges, we advocate a clean separation between the evidence collection and the inference in variant calling. We define and implement a Genome Query Language (GQL) that allows for the rapid collection of evidence needed for calling variants.'),
(77691, 2013, 'bio', 'Bayesian evolutionary model testing in the phylogenomics era: matching model complexity with computational efficiency', 'Baele, Guy|Lemey, Philippe', 'Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be.|Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be.', 'Belgium|Belgium', 51, 'Bayes Theorem|Codon|Genome, Mitochondrial|Genomics|Likelihood Functions|Models, Genetic|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btt340', '23766415', 'The advent of new sequencing technologies has led to increasing amounts of data being available to perform phylogenetic analyses, with genomic data giving rise to the field of phylogenomics. High-performance computing is becoming an indispensable research tool to fit complex evolutionary models, which take into account specific genomic properties, to large datasets. Here, we perform an extensive Bayesian phylogenetic model selection study, comparing codon and nucleotide substitution models, including codon position partitioning for nucleotide data as well gene-specific substitution models for both data types. For the best fitting partitioned models, we also compare independent partitioning with standard diffuse prior specification to conditional partitioning via hierarchical prior specification. To compare the different models, we use state-of-the-art marginal likelihood estimation techniques, including path sampling and stepping-stone sampling.'),
(77692, 2013, 'bio', 'PiHelper: an open source framework for drug-target and antibody-target data', 'Aksoy, Bülent Arman|Gao, Jianjiong|Dresdner, Gideon|Wang, Weiqing|Root, Alex|Jing, Xiaohong|Cerami, Ethan|Sander, Chris', 'Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Antibodies|Databases, Factual|Drug Discovery|Internet|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt345', '23766416', 'The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets.'),
(77693, 2013, 'bio', 'UniNovo: a universal tool for de novo peptide sequencing', 'Jeong, Kyowon|Kim, Sangtae|Pevzner, Pavel A', 'Department of Electrical and Computer Engineering and Department of Computer Science and Engineering, University of California-San Diego, CA 92093, USA. kwj@ucsd.edu|Department of Electrical and Computer Engineering and Department of Computer Science and Engineering, University of California-San Diego, CA 92093, USA. kwj@ucsd.edu|Department of Electrical and Computer Engineering and Department of Computer Science and Engineering, University of California-San Diego, CA 92093, USA. kwj@ucsd.edu', 'United States|United States|United States', 0, 'Algorithms|Peptides|Sequence Analysis, Protein|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt338', '23766417', 'Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77694, 2013, 'bio', 'Metingear: a development environment for annotating genome-scale metabolic models', 'May, John W|James, A Gordon|Steinbeck, Christoph', 'Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. johnmay@ebi.ac.uk|Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. johnmay@ebi.ac.uk|Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. johnmay@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Genome|Metabolism|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt342', '23766418', 'Genome-scale metabolic models often lack annotations that would allow them to be used for further analysis. Previous efforts have focused on associating metabolites in the model with a cross reference, but this can be problematic if the reference is not freely available, multiple resources are used or the metabolite is added from a literature review. Associating each metabolite with chemical structure provides unambiguous identification of the components and a more detailed view of the metabolism. We have developed an open-source desktop application that simplifies the process of adding database cross references and chemical structures to genome-scale metabolic models. Annotated models can be exported to the Systems Biology Markup Language open interchange format.'),
(77695, 2013, 'bio', 'Porter, PaleAle 40: high-accuracy prediction of protein secondary structure and relative solvent accessibility', 'Mirabello, Claudio|Pollastri, Gianluca', 'School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland.|School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland.', 'Ireland|Ireland', 51, 'Internet|Protein Structure, Secondary|Proteins|Software|Solvents', 'Journal Article', '10.1093/bioinformatics/btt344', '23772049', 'Protein secondary structure and solvent accessibility predictions are a fundamental intermediate step towards protein structure and function prediction. We present new systems for the ab initio prediction of protein secondary structure and solvent accessibility, Porter 4.0 and PaleAle 4.0. Porter 4.0 predicts secondary structure correctly for 82.2% of residues. PaleAle 4.0s accuracy is 80.0% for prediction in two classes with a 25% accessibility threshold. We show that the increasing training set sizes that come with the continuing growth of the Protein Data Bank keep yielding prediction quality improvements and examine the impact of protein resolution on prediction performances.'),
(77696, 2013, 'bio', 'Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions', 'Chen, Xiaowei|Slack, Frank J|Zhao, Hongyu', 'Program in Computational Biology and Bioinformatics, Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA.', 'United States|United States|United States', 0, 'Breast Neoplasms|Cystadenocarcinoma, Serous|Female|Gene Expression Profiling|Glioblastoma|Humans|MicroRNAs|Neoplasms|Ovarian Neoplasms', 'Journal Article', '10.1093/bioinformatics/btt341', '23772050', 'MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA-gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA-gene interactions without combining all the data into one single dataset.'),
(77697, 2013, 'bio', 'AdvISER-PYRO: Amplicon Identification using SparsE Representation of PYROsequencing signal', 'Ambroise, Jérôme|Piette, Anne-Sophie|Delcorps, Cathy|Rigouts, Leen|de Jong, Bouke C|Irenge, Leonid|Robert, Annie|Gala, Jean-Luc', 'Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be|Center for Applied Molecular Technologies, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Clos Chapelle-aux-Champs 30, Brussels, Belgium. jerome.ambroise@uclouvain.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Genotyping Techniques|Mycobacterium|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt339', '23772051', 'Converting a pyrosequencing signal into a nucleotide sequence appears highly challenging when signal intensities are low (unitary peak heights ) or when complex signals are produced by several target amplicons. In these cases, the pyrosequencing software fails to provide correct nucleotide sequences. Accordingly, the objective was to develop the AdvISER-PYRO algorithm, performing an automated, fast and reliable analysis of pyrosequencing signals that circumvents those limitations.'),
(77698, 2013, 'bio', 'GAT: a simulation framework for testing the association of genomic intervals', 'Heger, Andreas|Webber, Caleb|Goodson, Martin|Ponting, Chris P|Lunter, Gerton', 'MRC CGAT Programme and Functional Genomics Unit, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. andreas.heger@dpag.ox.ac.uk|MRC CGAT Programme and Functional Genomics Unit, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. andreas.heger@dpag.ox.ac.uk|MRC CGAT Programme and Functional Genomics Unit, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. andreas.heger@dpag.ox.ac.uk|MRC CGAT Programme and Functional Genomics Unit, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. andreas.heger@dpag.ox.ac.uk|MRC CGAT Programme and Functional Genomics Unit, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. andreas.heger@dpag.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 80, 'Binding Sites|Chromatin Immunoprecipitation|Computer Simulation|Deoxyribonuclease I|Genomics|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt343', '23782611', 'A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, when interpreting ChIP-Seq or RNA-Seq data in functional terms. Because genome organization is complex, answering this question is non-trivial.'),
(77699, 2013, 'bio', 'Exact algorithms for haplotype assembly from whole-genome sequence data', 'Chen, Zhi-Zhong|Deng, Fei|Wang, Lusheng', 'Division of Information System Design, Tokyo Denki University, Saitama, Japan. zzchen@mail.dendai.ac.jp|Division of Information System Design, Tokyo Denki University, Saitama, Japan. zzchen@mail.dendai.ac.jp|Division of Information System Design, Tokyo Denki University, Saitama, Japan. zzchen@mail.dendai.ac.jp', 'Japan|Japan|Japan', 32, 'Algorithms|Genome, Human|Haplotypes|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt349', '23782612', 'Haplotypes play a crucial role in genetic analysis and have many applications such as gene disease diagnoses, association studies, ancestry inference and so forth. The development of DNA sequencing technologies makes it possible to obtain haplotypes from a set of aligned reads originated from both copies of a chromosome of a single individual. This approach is often known as haplotype assembly. Exact algorithms that can give optimal solutions to the haplotype assembly problem are highly demanded. Unfortunately, previous algorithms for this problem either fail to output optimal solutions or take too long time even executed on a PC cluster.'),
(77700, 2013, 'bio', 'GPViz: dynamic visualization of genomic regions and variants affecting protein domains', 'Snajder, Rene|Trajanoski, Zlatko|Hackl, Hubert', 'Biocenter, Division of Bioinformatics, Innsbruck Medical University and Oncotyrol Center for Personalized Cancer Medicine GmbH, 6020 Innsbruck, Austria.|Biocenter, Division of Bioinformatics, Innsbruck Medical University and Oncotyrol Center for Personalized Cancer Medicine GmbH, 6020 Innsbruck, Austria.|Biocenter, Division of Bioinformatics, Innsbruck Medical University and Oncotyrol Center for Personalized Cancer Medicine GmbH, 6020 Innsbruck, Austria.', 'Austria|Austria|Austria', 0, 'Alternative Splicing|Computer Graphics|Exons|Genetic Variation|Genome|Genomics|Humans|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btt354', '23782613', 'GPViz is a versatile Java-based software for dynamic gene-centered visualization of genomic regions and/or variants. User-defined data can be loaded in common formats as resulting from analysis workflows used in sequencing applications and studied in the context of the gene, the corresponding transcript isoforms, proteins and their domains or other protein features. Both the genomic regions and variants can be also defined interactively. Various gene filter options are provided to enable an intersection of variants, genomic regions and affected protein features. Finally, by using GPViz, we identified differentially expressed exons, which could indicate alternative splicing events, and found somatic variants in different cancer types affecting metabolic proteins. GPViz is freely available at http://icbi.at/gpviz (released under GNU general public license), is based on Java 7 and can be used as a stand-alone or Web Start application.'),
(77701, 2013, 'bio', 'Crowdsourcing for bioinformatics', 'Good, Benjamin M|Su, Andrew I', 'Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. bgood@scripps.edu|Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. bgood@scripps.edu', 'United States|United States', 0, 'Computational Biology|Crowdsourcing|Games, Experimental|Sequence Alignment|Volunteers', 'Journal Article', '10.1093/bioinformatics/btt333', '23782614', 'Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains.'),
(77702, 2013, 'bio', 'A Sensitive and Accurate protein domain cLassification Tool (SALT) for short reads', 'Zhang, Yuan|Sun, Yanni|Cole, James R', 'Department of Computer Science and Engineering and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States', 7, 'Algorithms|Animals|Arabidopsis Proteins|Contig Mapping|Genome|High-Throughput Nucleotide Sequencing|Markov Chains|Molecular Sequence Annotation|Protein Structure, Tertiary|Proteins|Sequence Analysis, RNA|Snails|Software', 'Journal Article', '10.1093/bioinformatics/btt357', '23782615', 'Protein domain classification is an important step in functional annotation for next-generation sequencing data. For RNA-Seq data of non-model organisms that lack quality or complete reference genomes, existing protein domain analysis pipelines are applied to short reads directly or to contigs that are generated using de novo sequence assembly tools. However, these strategies do not provide satisfactory performance in classifying short reads into their native domain families.'),
(77703, 2013, 'bio', 'ChroMoS: an integrated web tool for SNP classification, prioritization and functional interpretation', 'Barenboim, Maxim|Manke, Thomas', 'Bioinformatics and Deep-Sequencing Unit, Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany. barenboim@ie-freiburg.mpg.de|Bioinformatics and Deep-Sequencing Unit, Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany. barenboim@ie-freiburg.mpg.de', 'Germany|Germany', 0, 'Algorithms|Epigenesis, Genetic|Genome-Wide Association Study|Humans|Internet|MicroRNAs|Polymorphism, Single Nucleotide|Software|Systems Integration|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt356', '23782616', 'Genome-wide association studies and re-sequencing projects are revealing an increasing number of disease-associated SNPs, a large fraction of which are non-coding. Although they could have relevance for disease susceptibility and progression, the lack of information about regulatory regions impedes the assessment of their functionality. Here we present a web server, ChroMoS (Chromatin Modified SNPs), which combines genetic and epigenetic data with the goal of facilitating SNPs classification, prioritization and prediction of their functional consequences. ChroMoS uses a large database of SNPs and chromatin states, but allows a user to provide his/her own genetic information. Based on the SNP classification and interactive prioritization, a user can compute the functional impact of multiple SNPs using two prediction tools, one for differential analysis of transcription factor binding (sTRAP) and another for SNPs with potential impact on binding of miRNAs (MicroSNiPer).'),
(77704, 2013, 'bio', 'Charge asymmetry in the proteins of the outer membrane', 'Slusky, Joanna S G|Dunbrack, Roland L', 'Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, PA 19111, USA. joanna.slusky@fccc.edu|Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, PA 19111, USA. joanna.slusky@fccc.edu', 'United States|United States', 31, 'Amino Acids|Bacterial Outer Membrane Proteins|Chloroplast Proteins|Gram-Negative Bacteria|Humans|Hydrophobic and Hydrophilic Interactions|Lipid Bilayers|Membrane Lipids|Membrane Proteins|Mitochondrial Proteins|Protein Structure, Secondary', 'Journal Article', '10.1093/bioinformatics/btt355', '23782617', 'Outer membrane beta-barrels (OMBBs) are the proteins found in the outer membrane of bacteria, mitochondria and chloroplasts. There are thousands of beta-barrels reported in genomic databases with xe2x88xbc2-3% of the genes in gram-negative bacteria encoding these proteins. These proteins have a wide variety of biological functions including active and passive transport, cell adhesion, catalysis and structural anchoring. Of the non-redundant OMBB structures in the Protein Data Bank, half have been solved during the past 5 years. This influx of information provides new opportunities for understanding the chemistry of these proteins. The distribution of charges in proteins in the outer membrane has implications for how the mechanism of outer membrane protein insertion is understood. Understanding the distribution of charges might also assist in organism selection for the heterologous expression of mitochondrial OMBBs.'),
(77705, 2013, 'bio', 'Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets', 'Zhou, Xi|Chen, Pengcheng|Wei, Qiang|Shen, Xueling|Chen, Xin', 'Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China.', 'China|China|China|China|China', 4, 'Gene Ontology|Genes|Humans|Internet|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btt353', '23782618', 'A molecular interaction network can be viewed as a network in which genes with related functions are connected. Therefore, at a systems level, connections between individual genes in a molecular interaction network can be used to infer the collective functional linkages between biologically meaningful gene sets.'),
(77706, 2013, 'bio', 'Harnessing virtual machines to simplify next-generation DNA sequencing analysis', 'Nocq, Julie|Celton, Magalie|Gendron, Patrick|Lemieux, Sebastien|Wilhelm, Brian T', 'Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.|Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.|Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.|Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.|Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.', 'Canada|Canada|Canada|Canada|Canada', 20, 'Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt352', '23786767', 'The growth of next-generation sequencing (NGS) has not only dramatically accelerated the pace of research in the field of genomics, but it has also opened the door to personalized medicine and diagnostics. The resulting flood of data has led to the rapid development of large numbers of bioinformatic tools for data analysis, creating a challenging situation for researchers when choosing and configuring a variety of software for their analysis, and for other researchers trying to replicate their analysis. As NGS technology continues to expand from the research environment into clinical laboratories, the challenges associated with data analysis have the potential to slow the adoption of this technology.'),
(77707, 2013, 'bio', 'Quikr: a method for rapid reconstruction of bacterial communities via compressive sensing', 'Koslicki, David|Foucart, Simon|Rosen, Gail', 'Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43201, USA. koslicki.1@mbi.osu.edu|Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43201, USA. koslicki.1@mbi.osu.edu|Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43201, USA. koslicki.1@mbi.osu.edu', 'United States|United States|United States', 27, 'Algorithms|Bacteria|Bayes Theorem|Classification|Metagenomics|Microbiota|Phylogeny|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt336', '23786768', 'Many metagenomic studies compare hundreds to thousands of environmental and health-related samples by extracting and sequencing their 16S rRNA amplicons and measuring their similarity using beta-diversity metrics. However, one of the first steps--to classify the operational taxonomic units within the sample--can be a computationally time-consuming task because most methods rely on computing the taxonomic assignment of each individual read out of tens to hundreds of thousands of reads.'),
(77708, 2013, 'bio', 'PING 20: an R/Bioconductor package for nucleosome positioning using next-generation sequencing data', 'Woo, Sangsoon|Zhang, Xuekui|Sauteraud, Renan|Robert, François|Gottardo, Raphael', 'Vaccine and Infectious Diseases and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Diseases and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Diseases and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Diseases and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Diseases and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.', 'United States|United States|United States|United States|United States', 14, 'High-Throughput Nucleotide Sequencing|Nucleosomes|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt348', '23786769', 'MNase-Seq and ChIP-Seq have evolved as popular techniques to study chromatin and histone modification. Although many tools have been developed to identify enriched regions, software tools for nucleosome positioning are still limited. We introduce a flexible and powerful open-source R package, PING 2.0, for nucleosome positioning using MNase-Seq data or MNase- or sonicated- ChIP-Seq data combined with either single-end or paired-end sequencing. PING uses a model-based approach, which enables nucleosome predictions even in the presence of low read counts. We illustrate PING using two paired-end datasets from Saccharomyces cerevisiae and compare its performance with nucleR and ChIPseqR.'),
(77709, 2013, 'bio', 'Co-regulation in embryonic stem cells via context-dependent binding of transcription factors', 'Lee, Yuju|Zhou, Qing', 'Department of Statistics, University of California-Los Angeles, CA 90095, USA.|Department of Statistics, University of California-Los Angeles, CA 90095, USA.', 'United States|United States', 6, 'Animals|Binding Sites|Chromatin Immunoprecipitation|Embryonic Stem Cells|Gene Expression Profiling|Gene Regulatory Networks|Mice|Nucleotide Motifs|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt365', '23793746', 'With the accumulation of genome-wide binding data for many transcription factors (TFs) in the same cell type or cellular condition, it is of great current interest to systematically infer the complex regulatory logic among multiple TFs. In particular, ChIP-Seq data have been generated for 14 core TFs critical to the maintenance and reprogramming of mouse embryonic stem cells (ESCs). This provides a great opportunity to study the regulatory collaboration and interaction among these TFs and with other unknown co-regulators.'),
(77710, 2013, 'bio', 'Linc2GO: a human LincRNA function annotation resource based on ceRNA hypothesis', 'Liu, Ke|Yan, Zhangming|Li, Yuchao|Sun, Zhirong', 'MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, School of Life Sciences, Tsinghua University, Beijing, China.', 'China|China|China|China', 70, 'Databases, Nucleic Acid|Humans|MicroRNAs|Molecular Sequence Annotation|RNA, Long Noncoding|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btt361', '23793747', 'Large numbers of long intergenic non-coding RNA (lincRNA) have been detected through high-throughput sequencing technology. However, currently we still know very little about their functions. Therefore, a lincRNA function annotation database is needed to facilitate the study in this field. In this article, we present Linc2GO, a web resource that aims to provide comprehensive functional annotations for human lincRNA. MicroRNA-mRNA and microRNA-lincRNA interaction data were integrated to generate lincRNA functional annotations based on the competing endogenous RNA hypothesis. To the best of our knowledge, Linc2GO is the first database that makes use of the competing endogenous RNA hypothesis to predict lincRNA functions.'),
(77711, 2013, 'bio', 'The human genome contracts again', 'Pavlichin, Dmitri S|Weissman, Tsachy|Yona, Golan', 'Department of Physics and Department of Electrical Engineering, Stanford University, CA 94305, USA.|Department of Physics and Department of Electrical Engineering, Stanford University, CA 94305, USA.|Department of Physics and Department of Electrical Engineering, Stanford University, CA 94305, USA.', 'United States|United States|United States', 20, 'Algorithms|Data Compression|Genome, Human|Genomics|Haplotypes|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btt362', '23793748', 'The number of human genomes that have been sequenced completely for different individuals has increased rapidly in recent years. Storing and transferring complete genomes between computers for the purpose of applying various applications and analysis tools will soon become a major hurdle, hindering the analysis phase. Therefore, there is a growing need to compress these data efficiently. Here, we describe a technique to compress human genomes based on entropy coding, using a reference genome and known Single Nucleotide Polymorphisms (SNPs). Furthermore, we explore several intrinsic features of genomes and information in other genomic databases to further improve the compression attained. Using these methods, we compress James Watsons genome to 2.5 megabytes (MB), improving on recent work by 37%. Similar compression is obtained for most genomes available from the 1000 Genomes Project. Our biologically inspired techniques promise even greater gains for genomes of lower organisms and for human genomes as more genomic data become available.'),
(77712, 2013, 'bio', 'Mechanistic insights into mutually exclusive splicing in dynamin 1', 'Suyama, Mikita', 'Medical Institute of Bioregulation, Kyushu University and Japan Science and Technology Agency, CREST, Higashi-ku, Fukuoka, Japan. mikita@bioreg.kyushu-u.ac.jp', 'Japan', 10, 'Alternative Splicing|Animals|Base Sequence|Conserved Sequence|Dynamin I|Humans|Models, Genetic|Phylogeny|RNA Precursors|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btt368', '23793749', 'Mutually exclusive splicing is a strictly regulated pattern of alternative splicing. A specific group of mutually exclusive splicing events has been shown to be regulated by the formation of specific RNA secondary structures. This type of regulation has been shown to exist only in arthropods. The present study involved a detailed sequence analysis of human gene structures that undergo mutually exclusive splicing, which showed that this type of regulation may also occur in dynamin 1 in mammals. A phylogenetic analysis revealed that the dynamin 1 orthologs in invertebrates did not share the same sequence features, which suggests that the regulatory mechanism has independently evolved in the mammalian lineage. Therefore, the emergence of this elaborate mechanism for mutually exclusive splicing may be attributable to mechanistic convergence.'),
(77713, 2013, 'bio', 'cisExpress: motif detection in DNA sequences', 'Triska, Martin|Grocutt, David|Southern, James|Murphy, Denis J|Tatarinova, Tatiana', 'Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK.|Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK.|Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK.|Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK.|Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|Arabidopsis|DNA|Genomics|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt366', '23793750', 'One of the major challenges for contemporary bioinformatics is the analysis and accurate annotation of genomic datasets to enable extraction of useful information about the functional role of DNA sequences. This article describes a novel genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. This new tool, cisExpress, is especially designed for use with large datasets, such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node. We demonstrate the robust nature and validity of the proposed method. It is applicable for use with a wide range of genomic databases for any species of interest.'),
(77714, 2013, 'bio', 'Differential gene expression analysis using coexpression and RNA-Seq data', 'Yang, Ei-Wen|Girke, Thomas|Jiang, Tao', 'Department of Computer Science and Engineering, Department of Botany and Plant Sciences and Institute of Integative Genome Biology, University of California, Riverside, CA 92521, USA. yyang027@ucr.edu|Department of Computer Science and Engineering, Department of Botany and Plant Sciences and Institute of Integative Genome Biology, University of California, Riverside, CA 92521, USA. yyang027@ucr.edu|Department of Computer Science and Engineering, Department of Botany and Plant Sciences and Institute of Integative Genome Biology, University of California, Riverside, CA 92521, USA. yyang027@ucr.edu', 'United States|United States|United States', 17, 'Algorithms|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Markov Chains|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt363', '23793751', 'RNA-Seq is increasingly being used for differential gene expression analysis, which was dominated by the microarray technology in the past decade. However, inferring differential gene expression based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. The differential expression estimation may be biased against low read count values such that the differential expression of genes with high read counts is more easily detected. The estimation bias may further propagate in downstream analyses at the systems biology level if it is not corrected.'),
(77715, 2013, 'bio', 'Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction', 'Heider, Dominik|Senge, Robin|Cheng, Weiwei|Hüllermeier, Eyke', 'Department of Bioinformatics, University of Duisburg-Essen, Essen, Germany.|Department of Bioinformatics, University of Duisburg-Essen, Essen, Germany.|Department of Bioinformatics, University of Duisburg-Essen, Essen, Germany.|Department of Bioinformatics, University of Duisburg-Essen, Essen, Germany.', 'Germany|Germany|Germany|Germany', 40, 'Anti-HIV Agents|Drug Resistance, Viral|HIV Reverse Transcriptase|HIV-1|Mutation|Reverse Transcriptase Inhibitors|Sequence Analysis|Virus Replication', 'Journal Article', '10.1093/bioinformatics/btt331', '23793752', 'Antiretroviral treatment regimens can sufficiently suppress viral replication in human immunodeficiency virus (HIV)-infected patients and prevent the progression of the disease. However, one of the factors contributing to the progression of the disease despite ongoing antiretroviral treatment is the emergence of drug resistance. The high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure, thus to failure of antiretroviral treatment due to the evolution of drug-resistant variants. Moreover, cross-resistance phenomena have been frequently found in HIV-1, leading to resistance not only against a drug from the current treatment, but also to other not yet applied drugs. Automatic classification and prediction of drug resistance is increasingly important in HIV research as well as in clinical settings, and to this end, machine learning techniques have been widely applied. Nevertheless, cross-resistance information was not taken explicitly into account, yet.'),
(77716, 2013, 'bio', 'Sipros/ProRata: a versatile informatics system for quantitative community proteomics', 'Wang, Yingfeng|Ahn, Tae-Hyuk|Li, Zhou|Pan, Chongle', 'Computer Science and Mathematics Division, Oak Ridge National Laboratory and Graduate School of Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory and Graduate School of Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory and Graduate School of Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory and Graduate School of Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States|United States|United States', 28, 'Databases, Protein|Protein Processing, Post-Translational|Proteins|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt329', '23793753', 'Sipros/ProRata is an open-source software package for end-to-end data analysis in a wide variety of community proteomics measurements. A database-searching program, Sipros 3.0, was developed for accurate general-purpose protein identification and broad-range post-translational modification searches. Hybrid Message Passing Interface/OpenMP parallelism of the new Sipros architecture allowed its computation to be scalable from desktops to supercomputers. The upgraded ProRata 3.0 performs label-free quantification and isobaric chemical labeling quantification in addition to metabolic labeling quantification. Sipros/ProRata is a versatile informatics system that enables identification and quantification of proteins and their variants in many types of community proteomics studies.'),
(77717, 2013, 'bio', 'Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns', 'Ortuño, Francisco M|Valenzuela, Olga|Rojas, Fernando|Pomares, Hector|Florido, Javier P|Urquiza, Jose M|Rojas, Ignacio', 'Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es|Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 34, 'Algorithms|Databases, Protein|Phylogeny|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt360', '23793754', 'Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences.'),
(77718, 2013, 'bio', 'Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server', 'Olimpieri, Pier Paolo|Chailyan, Anna|Tramontano, Anna|Marcatili, Paolo', 'Department of Physics, Sapienza University and Istituto Pasteur - Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University and Istituto Pasteur - Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University and Istituto Pasteur - Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University and Istituto Pasteur - Fondazione Cenci Bolognetti, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 42, 'Antigen-Antibody Complex|Artificial Intelligence|Internet|Models, Molecular|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btt369', '23803466', 'Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode.'),
(77719, 2013, 'bio', 'GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML', 'Vlaic, Sebastian|Hoffmann, Bianca|Kupfer, Peter|Weber, Michael|Dräger, Andreas', 'Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany. Sebastian.Vlaic@hki-jena.de|Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany. Sebastian.Vlaic@hki-jena.de|Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany. Sebastian.Vlaic@hki-jena.de|Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany. Sebastian.Vlaic@hki-jena.de|Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany. Sebastian.Vlaic@hki-jena.de', 'Germany|Germany|Germany|Germany|Germany', 4, 'Algorithms|Gene Regulatory Networks|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt370', '23803467', 'GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage.'),
(77720, 2013, 'bio', 'Pipit: visualizing functional impacts of structural variations', 'Sakai, Ryo|Moisse, Matthieu|Reumers, Joke|Aerts, Jan', 'Department of Electrical Engineering-ESAT, KU Leuven, SCD-SISTA, Leuven, Belgium. ryo.sakai@esat.kuleuven.be|Department of Electrical Engineering-ESAT, KU Leuven, SCD-SISTA, Leuven, Belgium. ryo.sakai@esat.kuleuven.be|Department of Electrical Engineering-ESAT, KU Leuven, SCD-SISTA, Leuven, Belgium. ryo.sakai@esat.kuleuven.be|Department of Electrical Engineering-ESAT, KU Leuven, SCD-SISTA, Leuven, Belgium. ryo.sakai@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Animals|Computer Graphics|Genes|Genome|Genomic Structural Variation|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btt367', '23803468', 'Pipit is a gene-centric interactive visualization tool designed to study structural genomic variations. Through focusing on individual genes as the functional unit, researchers are able to study and generate hypotheses on the biological impact of different structural variations, for instance, the deletion of dosage-sensitive genes or the formation of fusion genes. Pipit is a cross-platform Java application that visualizes structural variation data from Genome Variation Format files.'),
(77721, 2013, 'bio', 'Cake: a bioinformatics pipeline for the integrated analysis of somatic variants in cancer genomes', 'Rashid, Mamunur|Robles-Espinoza, Carla Daniela|Rust, Alistair G|Adams, David J', 'Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.|Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.|Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.|Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Breast Neoplasms|Carcinoma, Hepatocellular|Exome|Female|Genetic Variation|Genome|Genomics|Humans|Liver Neoplasms|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt371', '23803469', 'We have developed Cake, a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than any one algorithm alone. Cake can be run on a high-performance computer cluster or used as a stand-alone application. Availabilty: Cake is open-source and is available from http://cakesomatic.sourceforge.net/'),
(77722, 2013, 'bio', 'LNETWORK: an efficient and effective method for constructing phylogenetic networks', 'Wang, Juan|Guo, Maozu|Liu, Xiaoyan|Liu, Yang|Wang, Chunyu|Xing, Linlin|Che, Kai', 'Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|China|China|China|China|China', 17, 'Algorithms|Cluster Analysis|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btt378', '23811095', 'The evolutionary history of species is traditionally represented with a rooted phylogenetic tree. Each tree comprises a set of clusters, i.e. subsets of the species that are descended from a common ancestor. When rooted phylogenetic trees are built from several different datasets (e.g. from different genes), the clusters are often conflicting. These conflicting clusters cannot be expressed as a simple phylogenetic tree, however, they can be expressed in a phylogenetic network. Phylogenetic networks are a generalization of phylogenetic trees that can account for processes such as hybridization, horizontal gene transfer and recombination, which are difficult to represent in standard tree-like models of evolutionary histories. There is currently a large body of research aimed at developing appropriate methods for constructing phylogenetic networks from cluster sets. The Cass algorithm can construct a much simpler network than other available methods, but is extremely slow for large datasets or for datasets that need lots of reticulate nodes. The networks constructed by Cass are also greatly dependent on the order of input data, i.e. it generally derives different phylogenetic networks for the same dataset when different input orders are used.'),
(77723, 2013, 'bio', 'Two new areas were defined: Metabolic Networks and RNA Bioinformatics', 'Ben-Tal, Nir', 'Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China', 2, 'Computational Biology|Metabolic Networks and Pathways|RNA', 'Editorial', '10.1093/bioinformatics/btt282', '23812973', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77724, 2013, 'bio', 'Genome-wide identification and predictive modeling of tissue-specific alternative polyadenylation', 'Hafez, Dina|Ni, Ting|Mukherjee, Sayan|Zhu, Jun|Ohler, Uwe', 'Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States', 0, '3 Untranslated Regions|Adult|Genomics|Humans|Linear Models|Organ Specificity|Poly A|Polyadenylation|Regulatory Sequences, Ribonucleic Acid|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt233', '23812974', 'Pre-mRNA cleavage and polyadenylation are essential steps for 3-end maturation and subsequent stability and degradation of mRNAs. This process is highly controlled by cis-regulatory elements surrounding the cleavage/polyadenylation sites (polyA sites), which are frequently constrained by sequence content and position. More than 50% of human transcripts have multiple functional polyA sites, and the specific use of alternative polyA sites (APA) results in isoforms with variable 3-untranslated regions, thus potentially affecting gene regulation. Elucidating the regulatory mechanisms underlying differential polyA preferences in multiple cell types has been hindered both by the lack of suitable data on the precise location of cleavage sites, as well as of appropriate tests for determining APAs with significant differences across multiple libraries.'),
(77725, 2013, 'bio', 'Stability selection for regression-based models of transcription factor-DNA binding specificity', 'Mordelet, Fantine|Horton, John|Hartemink, Alexander J|Engelhardt, Barbara E|Gordân, Raluca', 'Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States', 29, 'Algorithms|Binding Sites|DNA|Genome|Humans|Linear Models|Protein Array Analysis|Protein Binding|Saccharomyces cerevisiae Proteins|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt221', '23812975', 'The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret.'),
(77726, 2013, 'bio', 'Predicting drug-target interactions using restricted Boltzmann machines', 'Wang, Yuhao|Zeng, Jianyang', 'Department of Automation and Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.|Department of Automation and Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.', 'China|China', 97, 'Algorithms|Artificial Intelligence|Computer Simulation|Drug Discovery|Drug Repositioning|Humans|Pharmaceutical Preparations|Proteins', 'Journal Article', '10.1093/bioinformatics/btt234', '23812976', 'In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action.'),
(77727, 2013, 'bio', 'Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets', 'Kotera, Masaaki|Tabei, Yasuo|Yamanishi, Yoshihiro|Tokimatsu, Toshiaki|Goto, Susumu', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Enzymes|Linear Models|Metabolic Networks and Pathways|Metabolome|Metabolomics|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt244', '23812977', 'The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most important challenge in metabolomics is the automated de novo reconstruction of metabolic pathways, which includes the elucidation of previously unknown reactions to bridge the metabolic gaps.'),
(77728, 2013, 'bio', 'CAMPways: constrained alignment framework for the comparative analysis of a pair of metabolic pathways', 'Abaka, Gamze|Bxc4xb1yxc4xb1koxc4x9flu, Txc3xbcrker|Erten, Cesim', 'Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.', 'Turkey|Turkey|Turkey', 12, 'Algorithms|Metabolic Engineering|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btt235', '23812978', 'Given a pair of metabolic pathways, an alignment of the pathways corresponds to a mapping between similar substructures of the pair. Successful alignments may provide useful applications in phylogenetic tree reconstruction, drug design and overall may enhance our understanding of cellular metabolism.'),
(77729, 2013, 'bio', 'Simple topological properties predict functional misannotations in a metabolic network', 'Liberal, Rodrigo|Pinney, John W', 'Department of Life Sciences and Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK.|Department of Life Sciences and Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Enzymes|Genome|Humans|Metabolic Networks and Pathways|Molecular Sequence Annotation|Phylogeny|Plasmodium falciparum', 'Journal Article', '10.1093/bioinformatics/btt236', '23812979', 'Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organisms metabolic network, a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation.'),
(77730, 2013, 'bio', 'IBD-Groupon: an efficient method for detecting group-wise identity-by-descent regions simultaneously in multiple individuals based on pairwise IBD relationships', 'He, Dan', 'Computational Genomics, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA. dhe@us.ibm.com', 'United States', 10, 'Algorithms|Alleles|Genetics, Population|Humans|Markov Chains|Pedigree|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btt237', '23812980', 'Detecting IBD tracts is an important problem in genetics. Most of the existing methods focus on detecting pairwise IBD tracts, which have relatively low power to detect short IBD tracts. Methods to detect IBD tracts among multiple individuals simultaneously, or group-wise IBD tracts, have better performance for short IBD tracts detection. Group-wise IBD tracts can be applied to a wide range of applications, such as disease mapping, pedigree reconstruction and so forth. The existing group-wise IBD tract detection method is computationally inefficient and is only able to handle small datasets, such as 20, 30 individuals with hundreds of SNPs. It also requires a previous specification of the number of IBD groups, or partitions of the individuals where all the individuals in the same partition are IBD with each other, which may not be realistic in many cases. The method can only handle a small number of IBD groups, such as two or three, because of scalability issues. What is more, it does not take LD (linkage disequilibrium) into consideration.'),
(77731, 2013, 'bio', 'Efficient network-guided multi-locus association mapping with graph cuts', 'Azencott, Chloé-Agathe|Grimm, Dominik|Sugiyama, Mahito|Kawahara, Yoshinobu|Borgwardt, Karsten M', 'Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 33, 'Arabidopsis|Flowers|Genetic Loci|Genome-Wide Association Study|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btt238', '23812981', 'As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings.'),
(77732, 2013, 'bio', 'Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans', 'Aerni, Sarah J|Liu, Xiao|Do, Chuong B|Gross, Samuel S|Nguyen, Andy|Guo, Stephen D|Long, Fuhui|Peng, Hanchuan|Kim, Stuart S|Batzoglou, Serafim', 'Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu|Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA. saerni@cs.stanford.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Caenorhabditis elegans|Cell Division|Cell Lineage|Gene Expression Profiling|Imaging, Three-Dimensional|Microscopy, Confocal', 'Journal Article', '10.1093/bioinformatics/btt223', '23812982', 'Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like development and aging. However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes.'),
(77733, 2013, 'bio', 'Inference of historical migration rates via haplotype sharing', 'Palamara, Pier Francesco|Peer, Itsik', 'Department of Computer Science, Columbia University, 500 West 120th, New York City, NY 10027, USA.|Department of Computer Science, Columbia University, 500 West 120th, New York City, NY 10027, USA.', 'United States|United States', 0, 'HapMap Project|Haplotypes|Human Migration|Humans|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btt239', '23812983', 'Pairs of individuals from a study cohort will often share long-range haplotypes identical-by-descent. Such haplotypes are transmitted from common ancestors that lived tens to hundreds of generations in the past, and they can now be efficiently detected in high-resolution genomic datasets, providing a novel source of information in several domains of genetic analysis. Recently, haplotype sharing distributions were studied in the context of demographic inference, and they were used to reconstruct recent demographic events in several populations. We here extend the framework to handle demographic models that contain multiple demes interacting through migration. We extensively test our formulation in several demographic scenarios, compare our approach with methods based on ancestry deconvolution and use this method to analyze Masai samples from the HapMap 3 dataset.'),
(77734, 2013, 'bio', 'Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations', 'Chowdhury, Salim Akhter|Shackney, Stanley E|Heselmeyer-Haddad, Kerstin|Ried, Thomas|Schäffer, Alejandro A|Schwartz, Russell', 'Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu|Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu|Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu|Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu|Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu|Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sachowdh@andrew.cmu.edu', 'United States|United States|United States|United States|United States|United States', 30, 'Algorithms|Breast Neoplasms|Disease Progression|Female|Gene Dosage|Humans|In Situ Hybridization, Fluorescence|Phylogeny|Software|Uterine Cervical Neoplasms', 'Journal Article', '10.1093/bioinformatics/btt205', '23812984', 'Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution.'),
(77735, 2013, 'bio', 'Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding', 'Cannistraci, Carlo Vittorio|Alanis-Lobato, Gregorio|Ravasi, Timothy', 'Integrative Systems Biology Laboratory, Biological and Environmental Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia. kalokagathos.agon@gmail.com|Integrative Systems Biology Laboratory, Biological and Environmental Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia. kalokagathos.agon@gmail.com|Integrative Systems Biology Laboratory, Biological and Environmental Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia. kalokagathos.agon@gmail.com', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 63, 'Algorithms|Gene Ontology|Protein Interaction Mapping|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt208', '23812985', 'Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.'),
(77736, 2013, 'bio', 'A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks', 'Lan, Alexander|Ziv-Ukelson, Michal|Yeger-Lotem, Esti', 'Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.|Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.|Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.', 'Israel|Israel|Israel', 0, 'Algorithms|Gene Ontology|Gene Regulatory Networks|Humans|Lung|Models, Biological|Orthomyxoviridae|Protein Interaction Mapping|Signal Transduction|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btt240', '23812986', 'A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions.'),
(77737, 2013, 'bio', 'Multitask learning for host-pathogen protein interactions', 'Kshirsagar, Meghana|Carbonell, Jaime|Klein-Seetharaman, Judith', 'Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA.', 'United States|United States|United States', 47, 'Algorithms|Artificial Intelligence|Bacterial Proteins|Host-Pathogen Interactions|Humans|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btt245', '23812987', 'An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host-pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called multitask learning, which considers the problem of building models across tasks that are related to each other. A task in our scenario is the set of host-pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.'),
(77738, 2013, 'bio', 'Identifying proteins controlling key disease signaling pathways', 'Gitter, Anthony|Bar-Joseph, Ziv', 'Computer Science Department and Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.|Computer Science Department and Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.', 'United States|United States', 20, 'Algorithms|Disease Progression|Gene Expression Profiling|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Influenza A Virus, H1N1 Subtype|Influenza A Virus, H5N1 Subtype|Influenza, Human|Protein Interaction Mapping|RNA Interference|Signal Transduction|Transcription Factors|Viral Load', 'Journal Article', '10.1093/bioinformatics/btt241', '23812988', 'Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.'),
(77739, 2013, 'bio', 'Predicting protein interactions via parsimonious network history inference', 'Patro, Rob|Kingsford, Carl', 'Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. robp@cs.cmu.edu|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. robp@cs.cmu.edu', 'United States|United States', 7, 'Algorithms|Basic-Leucine Zipper Transcription Factors|Biological Evolution|Herpesviridae|Phylogeny|Probability|Protein Interaction Mapping|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btt224', '23812989', 'Reconstruction of the network-level evolutionary history of protein-protein interactions provides a principled way to relate interactions in several present-day networks. Here, we present a general framework for inferring such histories and demonstrate how it can be used to determine what interactions existed in the ancestral networks, which present-day interactions we might expect to exist based on evolutionary evidence and what information extant networks contain about the order of ancestral protein duplications.'),
(77740, 2013, 'bio', 'ThreaDom: extracting protein domain boundary information from multiple threading alignments', 'Xue, Zhidong|Xu, Dong|Wang, Yan|Zhang, Yang', '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 Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States', 47, 'Algorithms|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt209', '23812990', 'Protein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions.'),
(77741, 2013, 'bio', 'Protein threading using context-specific alignment potential', 'Ma, Jianzhu|Wang, Sheng|Zhao, Feng|Xu, Jinbo', 'Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.', 'United States|United States|United States|United States', 80, 'Algorithms|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt210', '23812991', 'Template-based modeling, including homology modeling and protein threading, is the most reliable method for protein 3D structure prediction. However, alignment errors and template selection are still the main bottleneck for current template-base modeling methods, especially when proteins under consideration are distantly related.'),
(77742, 2013, 'bio', 'Predicting protein contact map using evolutionary and physical constraints by integer programming', 'Wang, Zhiyong|Xu, Jinbo', 'Toyota Technological Institute at Chicago, 6045 S Kenwood, IL 60637, USA.|Toyota Technological Institute at Chicago, 6045 S Kenwood, IL 60637, USA.', 'United States|United States', 70, 'Artificial Intelligence|Evolution, Molecular|Programming, Linear|Protein Structure, Secondary|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btt211', '23812992', 'Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole-contact map. A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.'),
(77743, 2013, 'bio', 'Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields', 'Pruteanu-Malinici, Iulian|Majoros, William H|Ohler, Uwe', 'Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States', 6, 'Algorithms|Animals|Bayes Theorem|Drosophila|Embryonic Development|Factor Analysis, Statistical|Gene Expression Profiling|Image Processing, Computer-Assisted|In Situ Hybridization|Models, Statistical|RNA, Messenger|Statistics, Nonparametric|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btt206', '23812993', 'Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously.'),
(77744, 2013, 'bio', 'Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms', 'Xu, Min|Alber, Frank', 'University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.|University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.', 'United States|United States', 12, 'Electron Microscope Tomography|Freezing|Imaging, Three-Dimensional|Leptospira interrogans|Macromolecular Substances|Ribosomes', 'Journal Article', '10.1093/bioinformatics/btt225', '23812994', 'Cryo-electron tomography allows the imaging of macromolecular complexes in near living conditions. To enhance the nominal resolution of a structure it is necessary to align and average individual subtomograms each containing identical complexes. However, if the sample of complexes is heterogeneous, it is necessary to first classify subtomograms into groups of identical complexes. This task becomes challenging when tomograms contain mixtures of unknown complexes extracted from a crowded environment. Two main challenges must be overcomed: First, classification of subtomograms must be performed without knowledge of template structures. However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms. Second, subtomograms extracted from crowded cellular environments, contain often fragments of other structures besides the target complex. However, alignment methods generally assume that each subtomogram only contains one complex. Automatic methods are needed to identify the target complexes in a subtomogram even when its shape is unknown.'),
(77745, 2013, 'bio', 'Compressive genomics for protein databases', 'Daniels, Noah M|Gallant, Andrew|Peng, Jian|Cowen, Lenore J|Baym, Michael|Berger, Bonnie', 'Department of Computer Science, Tufts University, Medford, MA 02451, USA.|Department of Computer Science, Tufts University, Medford, MA 02451, USA.|Department of Computer Science, Tufts University, Medford, MA 02451, USA.|Department of Computer Science, Tufts University, Medford, MA 02451, USA.|Department of Computer Science, Tufts University, Medford, MA 02451, USA.|Department of Computer Science, Tufts University, Medford, MA 02451, USA.', 'United States|United States|United States|United States|United States|United States', 26, 'Algorithms|Data Compression|Databases, Protein|Genomics|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btt214', '23812995', 'The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast, we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed-up homology search directly but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools.'),
(77746, 2013, 'bio', 'GeneScissors: a comprehensive approach to detecting and correcting spurious transcriptome inference owing to RNA-seq reads misalignment', 'Zhang, Zhaojun|Huang, Shunping|Wang, Jack|Zhang, Xiang|Pardo Manuel de Villena, Fernando|McMillan, Leonard|Wang, Wei', 'Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Animals|Artificial Intelligence|Gene Expression Profiling|Genomics|Mice|Pseudogenes|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt216', '23812996', 'RNA-seq techniques provide an unparalleled means for exploring a transcriptome with deep coverage and base pair level resolution. Various analysis tools have been developed to align and assemble RNA-seq data, such as the widely used TopHat/Cufflinks pipeline. A common observation is that a sizable fraction of the fragments/reads align to multiple locations of the genome. These multiple alignments pose substantial challenges to existing RNA-seq analysis tools. Inappropriate treatment may result in reporting spurious expressed genes (false positives) and missing the real expressed genes (false negatives). Such errors impact the subsequent analysis, such as differential expression analysis. In our study, we observe that ~3.5% of transcripts reported by TopHat/Cufflinks pipeline correspond to annotated nonfunctional pseudogenes. Moreover, ~10.0% of reported transcripts are not annotated in the Ensembl database. These genes could be either novel expressed genes or false discoveries.'),
(77747, 2013, 'bio', 'The RNA Newton polytope and learnability of energy parameters', 'Forouzmand, Elmirasadat|Chitsaz, Hamidreza', 'Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.', 'United States|United States', 3, 'Algorithms|Artificial Intelligence|Base Pairing|Models, Molecular|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1093/bioinformatics/btt226', '23812998', 'Computational RNA structure prediction is a mature important problem that has received a new wave of attention with the discovery of regulatory non-coding RNAs and the advent of high-throughput transcriptome sequencing. Despite nearly two score years of research on RNA secondary structure and RNA-RNA interaction prediction, the accuracy of the state-of-the-art algorithms are still far from satisfactory. So far, researchers have proposed increasingly complex energy models and improved parameter estimation methods, experimental and/or computational, in anticipation of endowing their methods with enough power to solve the problem. The output has disappointingly been only modest improvements, not matching the expectations. Even recent massively featured machine learning approaches were not able to break the barrier. Why is that?'),
(77748, 2013, 'bio', 'A weighted sampling algorithm for the design of RNA sequences with targeted secondary structure and nucleotide distribution', 'Reinharz, Vladimir|Ponty, Yann|Waldispühl, Jérôme', 'School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, QC, Canada.|School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, QC, Canada.|School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, QC, Canada.', 'Canada|Canada|Canada', 24, 'Algorithms|Base Composition|Base Sequence|Models, Statistical|Nucleic Acid Conformation|Nucleotides|RNA|Sequence Analysis, RNA|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btt217', '23812999', 'The design of RNA sequences folding into predefined secondary structures is a milestone for many synthetic biology and gene therapy studies. Most of the current software uses similar local search strategies (i.e. a random seed is progressively adapted to acquire the desired folding properties) and more importantly do not allow the user to control explicitly the nucleotide distribution such as the GC-content in their sequences. However, the latter is an important criterion for large-scale applications as it could presumably be used to design sequences with better transcription rates and/or structural plasticity.'),
(77749, 2013, 'bio', 'Poly(A) motif prediction using spectral latent features from human DNA sequences', 'Xie, Bo|Jankovic, Boris R|Bajic, Vladimir B|Song, Le|Gao, Xin', 'College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia', 0, '3 Untranslated Regions|Algorithms|Artificial Intelligence|DNA|Humans|Markov Chains|Nucleotide Motifs|Poly A|Polyadenylation|Sequence Analysis, DNA|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt218', '23813000', 'Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.'),
(77750, 2013, 'bio', 'IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels', 'Peng, Yu|Leung, Henry C M|Yiu, Siu-Ming|Lv, Ming-Ju|Zhu, Xin-Guang|Chin, Francis Y L', 'Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China.', 'China|China|China|China|China|China', 92, 'Algorithms|Computer Graphics|Gene Expression Profiling|Genome|High-Throughput Nucleotide Sequencing|Oryza|Sensitivity and Specificity|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt219', '23813001', 'RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges. Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.'),
(77751, 2013, 'bio', 'A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains', 'Kundu, Kousik|Costa, Fabrizio|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.', 'Germany|Germany|Germany', 0, 'Artificial Intelligence|Genome, Human|Humans|Ligands|Models, Molecular|Peptides|Sequence Analysis, Protein|Software|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btt220', '23813002', 'State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains.'),
(77752, 2013, 'bio', 'Using state machines to model the Ion Torrent sequencing process and to improve read error rates', 'Golan, David|Medvedev, Paul', 'Department of Statistics and Operations Research, School of Mathematical Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. davidgo5@post.tau.ac.il|Department of Statistics and Operations Research, School of Mathematical Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. davidgo5@post.tau.ac.il', 'Israel|Israel', 0, 'Algorithms|Models, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt212', '23813003', 'The importance of fast and affordable DNA sequencing methods for current day life sciences, medicine and biotechnology is hard to overstate. A major player is Ion Torrent, a pyrosequencing-like technology which produces flowgrams--sequences of incorporation values--which are converted into nucleotide sequences by a base-calling algorithm. Because of its exploitation of ubiquitous semiconductor technology and innovation in chemistry, Ion Torrent has been gaining popularity since its debut in 2011. Despite the advantages, however, Ion Torrent read accuracy remains a significant concern.'),
(77753, 2013, 'bio', 'Haplotype assembly in polyploid genomes and identical by descent shared tracts', 'Aguiar, Derek|Istrail, Sorin', 'Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.', 'United States|United States', 51, 'Algorithms|Genome, Human|Genomics|Haplotypes|Humans|Polyploidy|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt213', '23813004', 'Genome-wide haplotype reconstruction from sequence data, or haplotype assembly, is at the center of major challenges in molecular biology and life sciences. For complex eukaryotic organisms like humans, the genome is vast and the population samples are growing so rapidly that algorithms processing high-throughput sequencing data must scale favorably in terms of both accuracy and computational efficiency. Furthermore, current models and methodologies for haplotype assembly (i) do not consider individuals sharing haplotypes jointly, which reduces the size and accuracy of assembled haplotypes, and (ii) are unable to model genomes having more than two sets of homologous chromosomes (polyploidy). Polyploid organisms are increasingly becoming the target of many research groups interested in the genomics of disease, phylogenetics, botany and evolution but there is an absence of theory and methods for polyploid haplotype reconstruction.'),
(77754, 2013, 'bio', 'FuncISH: learning a functional representation of neural ISH images', 'Liscovitch, Noa|Shalit, Uri|Chechik, Gal', 'Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel. noalis@gmail.com|Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel. noalis@gmail.com|Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel. noalis@gmail.com', 'Israel|Israel|Israel', 8, 'Animals|Brain|GABAergic Neurons|Gene Expression|Gene Expression Profiling|Image Processing, Computer-Assisted|In Situ Hybridization|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btt207', '23813005', 'High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77755, 2013, 'bio', 'Short read alignment with populations of genomes', 'Huang, Lin|Popic, Victoria|Batzoglou, Serafim', 'Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States', 0, 'Algorithms|Genetic Variation|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt215', '23813006', 'The increasing availability of high-throughput sequencing technologies has led to thousands of human genomes having been sequenced in the past years. Efforts such as the 1000 Genomes Project further add to the availability of human genome variation data. However, to date, there is no method that can map reads of a newly sequenced human genome to a large collection of genomes. Instead, methods rely on aligning reads to a single reference genome. This leads to inherent biases and lower accuracy. To tackle this problem, a new alignment tool BWBBLE is introduced in this article. We (i) introduce a new compressed representation of a collection of genomes, which explicitly tackles the genomic variation observed at every position, and (ii) design a new alignment algorithm based on the Burrows-Wheeler transform that maps short reads from a newly sequenced genome to an arbitrary collection of two or more (up to millions of) genomes with high accuracy and no inherent bias to one specific genome.'),
(77756, 2013, 'bio', 'A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text', 'Miwa, Makoto|Ohta, Tomoko|Rak, Rafal|Rowley, Andrew|Kell, Douglas B|Pyysalo, Sampo|Ananiadou, Sophia', 'The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Algorithms|Artificial Intelligence|Biochemical Phenomena|Data Mining|MEDLINE|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt227', '23813008', 'To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge.'),
(77757, 2013, 'bio', 'Information-theoretic evaluation of predicted ontological annotations', 'Clark, Wyatt T|Radivojac, Predrag', 'Department of Computer Science and Informatics, Indiana University, Bloomington, IN 47405, USA. predrag@indiana.edu|Department of Computer Science and Informatics, Indiana University, Bloomington, IN 47405, USA. predrag@indiana.edu', 'India|India', 46, 'Algorithms|Bayes Theorem|Data Interpretation, Statistical|Gene Ontology|Genes|Humans|Molecular Sequence Annotation|Proteins', 'Journal Article', '10.1093/bioinformatics/btt228', '23813009', 'The development of effective methods for the prediction of ontological annotations is an important goal in computational biology, with protein function prediction and disease gene prioritization gaining wide recognition. Although various algorithms have been proposed for these tasks, evaluating their performance is difficult owing to problems caused both by the structure of biomedical ontologies and biased or incomplete experimental annotations of genes and gene products.'),
(77758, 2013, 'bio', 'Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM', 'Sedgewick, Andrew J|Benz, Stephen C|Rabizadeh, Shahrooz|Soon-Shiong, Patrick|Vaske, Charles J', 'Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.', 'United States|United States|United States|United States|United States', 23, 'Algorithms|Bayes Theorem|Breast Neoplasms|Cluster Analysis|Estrogen Receptor alpha|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Models, Statistical|Neoplasms|Ovarian Neoplasms|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btt229', '23813010', 'High-dimensional -omics profiling provides a detailed molecular view of individual cancers, however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237-i245), a pathway analysis method for combining multiple -omics data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature. Using genomic and mRNA expression data from 1936 samples in The Cancer Genome Atlas (TCGA) cohort, we learned interactions that provided support for and relative strength of 7138 (78%) of the curated links. Gene set enrichment found that genes involved in the strongest interactions were significantly enriched for transcriptional regulation, apoptosis, cell cycle regulation and response to tumor cells. Within the TCGA breast cancer cohort, we assessed different interaction strengths between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. PARADIGM with the Naive Bayesian assumption produced gene activity predictions that, when clustered, found groups of patients with better separation in survival than both the original version of PARADIGM and a version without the assumption. We found that this Naive Bayes assumption was valid for the vast majority of co-regulators, indicating that most co-regulators act independently on their shared target.'),
(77759, 2013, 'bio', 'Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancers', 'Orenstein, Yaron|Shamir, Ron', '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', 8, 'Algorithms|DNA Probes|DNA-Binding Proteins|Enhancer Elements, Genetic|Protein Array Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt230', '23813011', 'Novel technologies can generate large sets of short double-stranded DNA sequences that can be used to measure their regulatory effects. Microarrays can measure in vitro the binding intensity of a protein to thousands of probes. Synthetic enhancer sequences inserted into an organisms genome allow us to measure in vivo the effect of such sequences on the phenotype. In both applications, by using sequence probes that cover all k-mers, a comprehensive picture of the effect of all possible short sequences on gene regulation is obtained. The value of k that can be used in practice is, however, severely limited by cost and space considerations. A key challenge is, therefore, to cover all k-mers with a minimal number of probes. The standard way to do this uses the de Bruijn sequence of length . However, as probes are double stranded, when a k-mer is included in a probe, its reverse complement k-mer is accounted for as well.'),
(77760, 2013, 'bio', 'Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model', 'Bonzanni, Nicola|Garg, Abhishek|Feenstra, K Anton|Schütte, Judith|Kinston, Sarah|Miranda-Saavedra, Diego|Heringa, Jaap|Xenarios, Ioannis|Göttgens, Berthold', 'IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.|IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Animals|Cell Line|Erythrocytes|Gene Regulatory Networks|Genes, Regulator|Hematopoiesis|Hematopoietic Stem Cells|Mice|Models, Genetic|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt243', '23813012', 'Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.'),
(77761, 2013, 'bio', 'Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation', 'Le, Hai-Son|Bar-Joseph, Ziv', 'Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States', 32, 'Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Models, Statistical|Neoplasms|Ovarian Neoplasms|Protein Interaction Mapping|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt231', '23813013', 'MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions.'),
(77762, 2013, 'bio', 'A high-throughput framework to detect synapses in electron microscopy images', 'Navlakha, Saket|Suhan, Joseph|Barth, Alison L|Bar-Joseph, Ziv', 'Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 14, 'Algorithms|Animals|Artificial Intelligence|Mice|Microscopy, Electron|Somatosensory Cortex|Synapses', 'Journal Article', '10.1093/bioinformatics/btt222', '23813014', 'Synaptic connections underlie learning and memory in the brain and are dynamically formed and eliminated during development and in response to stimuli. Quantifying changes in overall density and strength of synapses is an important pre-requisite for studying connectivity and plasticity in these cases or in diseased conditions. Unfortunately, most techniques to detect such changes are either low-throughput (e.g. electrophysiology), prone to error and difficult to automate (e.g. standard electron microscopy) or too coarse (e.g. magnetic resonance imaging) to provide accurate and large-scale measurements.'),
(77763, 2013, 'bio', 'A framework for scalable parameter estimation of gene circuit models using structural information', 'Kuwahara, Hiroyuki|Fan, Ming|Wang, Suojin|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Gene Expression Regulation|Gene Regulatory Networks|Models, Genetic|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt232', '23813015', 'Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation.'),
(77764, 2013, 'bio', 'SbacHTS: spatial background noise correction for high-throughput RNAi screening', 'Zhong, Rui|Kim, Min Soo|White, Michael A|Xie, Yang|Xiao, Guanghua', 'Quantitative Biomedical Research Center, Department of Clinical Science, Harold C. Simmons Comprehensive Cancer Center and Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Science, Harold C. Simmons Comprehensive Cancer Center and Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Science, Harold C. Simmons Comprehensive Cancer Center and Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Science, Harold C. Simmons Comprehensive Cancer Center and Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Science, Harold C. Simmons Comprehensive Cancer Center and Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.', 'United States|United States|United States|United States|United States', 2, 'Genomics|High-Throughput Screening Assays|RNA Interference|Software', 'Journal Article', '10.1093/bioinformatics/btt358', '23814141', 'High-throughput cell-based phenotypic screening has become an increasingly important technology for discovering new drug targets and assigning gene functions. Such experiments use hundreds of 96-well or 384-well plates, to cover whole-genome RNAi collections and/or chemical compound files, and often collect measurements that are sensitive to spatial background noise whose patterns can vary across individual plates. Correcting these position effects can substantially improve measurement accuracy and screening success.'),
(77765, 2013, 'bio', 'BNFinder2: Faster Bayesian network learning and Bayesian classification', 'Dojer, Norbert|Bednarz, Pawel|Podsiadlo, Agnieszka|Wilczynski, Bartek', 'Institute of Informatics, University of Warsaw, Warsaw, Poland. dojer@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. dojer@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. dojer@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. dojer@mimuw.edu.pl', 'Poland|Poland|Poland|Poland', 18, 'Algorithms|Bayes Theorem|Models, Statistical|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btt323', '23818512', 'Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time, (ii) inclusion of an additional scoring function based on mutual information criteria, (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores.'),
(77766, 2013, 'bio', 'Data-based filtering for replicated high-throughput transcriptome sequencing experiments', 'Rau, Andrea|Gallopin, Mélina|Celeux, Gilles|Jaffrézic, Florence', 'INRA, UMR1313 Génétique animale et biologie intégrative, 78352 Jouy-en-Josas, France. andrea.rau@jouy.inra.fr|INRA, UMR1313 Génétique animale et biologie intégrative, 78352 Jouy-en-Josas, France. andrea.rau@jouy.inra.fr|INRA, UMR1313 Génétique animale et biologie intégrative, 78352 Jouy-en-Josas, France. andrea.rau@jouy.inra.fr|INRA, UMR1313 Génétique animale et biologie intégrative, 78352 Jouy-en-Josas, France. andrea.rau@jouy.inra.fr', 'France|France|France|France', 0, 'Animals|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt350', '23821648', 'RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses.'),
(77767, 2013, 'bio', 'The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations', 'Lillacci, Gabriele|Khammash, Mustafa', 'ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland.|ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland.', 'Switzerland|Switzerland', 32, 'Algorithms|Bayes Theorem|Computer Simulation|Flow Cytometry|Fluorescence|Gene Expression Regulation|Gene Regulatory Networks|Models, Genetic|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btt380', '23821649', 'In the noisy cellular environment, stochastic fluctuations at the molecular level manifest as cell-cell variability at the population level that is quantifiable using high-throughput single-cell measurements. Such variability is rich with information about the cells underlying gene regulatory networks, their architecture and the parameters of the biochemical reactions at their core.'),
(77768, 2013, 'bio', 'An integrative approach to understanding the combinatorial histone code at functional elements', 'Lai, William K M|Buck, Michael J', 'Department of Biochemistry, Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA.|Department of Biochemistry, Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA.', 'United States|United States', 5, 'Algorithms|Chromatin|Gene Expression|Genomics|Histone Code|Nucleosomes|Saccharomyces cerevisiae|Sequence Alignment|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt382', '23821650', 'The rapid advancement of genomic technology has revealed the enormous complexity and combinatorial nature of chromatin modifications. To facilitate interpretation of the combinatorial nature of chromatin, we have developed a novel method to integrate all chromatin datasets into distinct nucleosome types (nucleosome alphabet). We have applied this approach to Saccharomyces cerevisiae, generating a nucleosome alphabet, which forms chromatin motifs when mapped back to the genome. By applying novel chromatin alignment and global word search approaches, we have defined distinctive chromatin motifs for introns, origins of replication, tRNAs, antisense transcripts, double-strand-break hotspots and DNase hypersensitive sites, and can distinguish genes by expression level. We have also uncovered strong associations between transcription factor binding and specific types of nucleosomes. Our results demonstrate the uses and functionality of defining a chromatin alphabet and provide a unique and novel framework for exploring chromatin architecture.'),
(77769, 2013, 'bio', 'TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference', 'Nariai, Naoki|Hirose, Osamu|Kojima, Kaname|Nagasaki, Masao', 'Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nariai@megabank.tohoku.ac.jp|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nariai@megabank.tohoku.ac.jp|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nariai@megabank.tohoku.ac.jp|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nariai@megabank.tohoku.ac.jp', 'Japan|Japan|Japan|Japan', 21, 'Algorithms|Alternative Splicing|Bayes Theorem|Cell Line|Gene Expression Profiling|Humans|RNA Isoforms|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt381', '23821651', 'Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other.'),
(77770, 2013, 'bio', 'CellMix: a comprehensive toolbox for gene expression deconvolution', 'Gaujoux, Renaud|Seoighe, Cathal', 'Computational Biology Group, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, South Africa. renaud@cbio.uct.ac.za|Computational Biology Group, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, South Africa. renaud@cbio.uct.ac.za', 'South Africa|South Africa', 111, 'Cell Line, Transformed|Gene Expression Profiling|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btt351', '23825367', 'Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques and enables a more detailed analysis of the underlying biological processes at the cell-type level. To facilitate and popularize the application of such methods, we developed CellMix, an R package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples.'),
(77771, 2013, 'bio', 'MMuFLR: missense mutation and frameshift location reporter', 'Rathe, Susan K|Johnson, James E|Silverstein, Kevin A T|Erdmann, Jesse J|Watson, Adrienne L|Popescu, Flavia E|Ohlfest, John R|Largaespada, David A', 'Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu|Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA. rath0096@umn.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Frameshift Mutation|High-Throughput Nucleotide Sequencing|Humans|Mutation, Missense|Neoplasm Proteins|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt385', '23825368', 'Cancer researchers seeking immunotherapy targets in cancer cells need tools to locate highly expressed proteins unique to cancer cells. Missense mutation and frameshift location reporter (MMuFLR), a Galaxy-based workflow, analyzes next-generation sequencing paired read RNA-seq output to reliably identify small frameshift mutations and missense mutations in highly expressed protein-coding genes. MMuFLR ignores known SNPs, low quality reads and poly-A/T sequences. For each frameshift and missense mutation identified, MMuFLR provides the location and sequence of the amino acid substitutions in the novel protein candidates for direct input into epitope evaluation tools.'),
(77772, 2013, 'bio', 'mRMRe: an R package for parallelized mRMR ensemble feature selection', 'De Jay, Nicolas|Papillon-Cavanagh, Simon|Olsen, Catharina|El-Hachem, Nehme|Bontempi, Gianluca|Haibe-Kains, Benjamin', 'Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.|Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.|Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.|Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.|Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.|Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 63, 'Algorithms|Antineoplastic Agents, Phytogenic|Camptothecin|Drug Resistance, Neoplasm|Genomics|Irinotecan|Software', 'Journal Article', '10.1093/bioinformatics/btt383', '23825369', 'Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature space and build more robust predictors. To deal with the computational complexity of the ensemble approach, the main functions of the package are implemented and parallelized in C using the openMP Application Programming Interface.'),
(77773, 2013, 'bio', 'Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data', 'Yang, Wen-Yun|Hormozdiari, Farhad|Wang, Zhanyong|He, Dan|Pasaniuc, Bogdan|Eskin, Eleazar', 'Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Alleles|Genome, Human|Genotyping Techniques|HapMap Project|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt386', '23825370', 'Haplotypes, defined as the sequence of alleles on one chromosome, are crucial for many genetic analyses. As experimental determination of haplotypes is extremely expensive, haplotypes are traditionally inferred using computational approaches from genotype data, i.e. the mixture of the genetic information from both haplotypes. Best performing approaches for haplotype inference rely on Hidden Markov Models, with the underlying assumption that the haplotypes of a given individual can be represented as a mosaic of segments from other haplotypes in the same population. Such algorithms use this model to predict the most likely haplotypes that explain the observed genotype data conditional on reference panel of haplotypes. With rapid advances in short read sequencing technologies, sequencing is quickly establishing as a powerful approach for collecting genetic variation information. As opposed to traditional genotyping-array technologies that independently call genotypes at polymorphic sites, short read sequencing often collects haplotypic information, a read spanning more than one polymorphic locus (multi-single nucleotide polymorphic read) contains information on the haplotype from which the read originates. However, this information is generally ignored in existing approaches for haplotype phasing and genotype-calling from short read data.'),
(77774, 2013, 'bio', 'Statistical agglomeration: peak summarization for direct infusion lipidomics', 'Smith, Rob|Anthonymuthu, Tamil S|Ventura, Dan|Prince, John T', 'Department of Computer Science and Department of Chemistry, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science and Department of Chemistry, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science and Department of Chemistry, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science and Department of Chemistry, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States', 4, 'Algorithms|Genomics|Internet|Lipids|Mass Spectrometry|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btt376', '23825371', 'Quantification of lipids is a primary goal in lipidomics. In direct infusion/injection (or shotgun) lipidomics, accurate downstream identification and quantitation requires accurate summarization of repetitive peak measurements. Imprecise peak summarization multiplies downstream error by propagating into species identification and intensity estimation. To our knowledge, this is the first analysis of direct infusion peak summarization in the literature.'),
(77775, 2013, 'bio', 'Scalable metagenomic taxonomy classification using a reference genome database', 'Ames, Sasha K|Hysom, David A|Gardner, Shea N|Lloyd, G Scott|Gokhale, Maya B|Allen, Jonathan E', 'Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.|Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.|Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.|Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.|Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.|Center for Applied Scientific Computing, Lawrence Livermore National Laboratory and Global Security Directorate, P. O. Box 808, Livermore, CA 94551, USA.', 'United States|United States|United States|United States|United States|United States', 94, 'Algorithms|Classification|Databases, Nucleic Acid|Genome|High-Throughput Nucleotide Sequencing|Metagenomics|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btt389', '23828782', 'Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge.'),
(77776, 2013, 'bio', 'Flux balance impact degree: a new definition of impact degree to properly treat reversible reactions in metabolic networks', 'Zhao, Yang|Tamura, Takeyuki|Akutsu, Tatsuya|Vert, Jean-Philippe', 'Bioinformatics Center, Kyoto University, Kyoto, Japan, Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honorxc3xa9, Fontainebleau, France.|Bioinformatics Center, Kyoto University, Kyoto, Japan, Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honorxc3xa9, Fontainebleau, France.|Bioinformatics Center, Kyoto University, Kyoto, Japan, Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honorxc3xa9, Fontainebleau, France.|Bioinformatics Center, Kyoto University, Kyoto, Japan, Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honorxc3xa9, Fontainebleau, France.', 'France|France|France|France', 6, 'Algorithms|Escherichia coli|Gene Deletion|Metabolic Networks and Pathways|Models, Biological|Phenotype', 'Journal Article', '10.1093/bioinformatics/btt364', '23828783', 'Metabolic pathways are complex systems of chemical reactions taking place in every living cell to degrade substrates and synthesize molecules needed for life. Modeling the robustness of these networks with respect to the dysfunction of one or several reactions is important to understand the basic principles of biological network organization, and to identify new drug targets. While several approaches have been proposed for that purpose, they are computationally too intensive to analyze large networks, and do not properly handle reversible reactions.'),
(77777, 2013, 'bio', 'Automated QuantMap for rapid quantitative molecular network topology analysis', 'Schaal, Wesley|Hammerling, Ulf|Gustafsson, Mats G|Spjuth, Ola', 'Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24 Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 6, 'Databases, Chemical|Pharmaceutical Preparations|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btt390', '23828784', 'The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment.'),
(77778, 2014, 'bio', 'Rapid similarity search of proteins using alignments of domain arrangements', 'Terrapon, Nicolas|Weiner, January|Grath, Sonja|Moore, Andrew D|Bornberg-Bauer, Erich', 'Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.|Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.|Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.|Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.|Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 22, 'Algorithms|Computational Biology|GTPase-Activating Proteins|Models, Statistical|Phosphotransferases (Phosphomutases)|Protein Interaction Domains and Motifs|Proteins|ROC Curve|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt379', '23828785', 'Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal.'),
(77779, 2013, 'bio', 'Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature', 'Xu, Rong|Li, Li|Wang, Quanqiu', 'Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, USA. rxx@case.edu|Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, USA. rxx@case.edu|Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, USA. rxx@case.edu', 'United States|United States|United States', 29, 'Data Mining|Disease|Drug Repositioning|Humans|Knowledge Bases|MEDLINE|Phenotype|Search Engine', 'Journal Article', '10.1093/bioinformatics/btt359', '23828786', 'Systems approaches to studying phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repurposing. Currently, systematic study of disease phenotypic relationships on a phenome-wide scale is limited because large-scale machine-understandable disease-phenotype relationship knowledge bases are often unavailable. Here, we present an automatic approach to extract disease-manifestation (D-M) pairs (one specific type of disease-phenotype relationship) from the wide body of published biomedical literature.'),
(77780, 2013, 'bio', 'PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data', 'Chen, Wei-Ming|Danziger, Samuel A|Chiang, Jung-Hsien|Aitchison, John D', 'Institute for Systems Biology, Seattle, WA 98109-5234, USA, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan and Seattle Biomedical Research Institute, Seattle, WA 98109-5219, USA.|Institute for Systems Biology, Seattle, WA 98109-5234, USA, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan and Seattle Biomedical Research Institute, Seattle, WA 98109-5219, USA.|Institute for Systems Biology, Seattle, WA 98109-5234, USA, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan and Seattle Biomedical Research Institute, Seattle, WA 98109-5219, USA.|Institute for Systems Biology, Seattle, WA 98109-5234, USA, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan and Seattle Biomedical Research Institute, Seattle, WA 98109-5219, USA.', 'United States|United States|United States|United States', 5, 'Algorithms|Amino Acid Sequence|Gene Expression Regulation, Enzymologic|Genome|MAP Kinase Signaling System|Molecular Sequence Data|Phosphoprotein Phosphatases|Phosphorylation|Protein Kinases|RNA, Messenger|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btt387', '23832245', 'Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed.'),
(77781, 2013, 'bio', 'Determining the subcellular location of new proteins from microscope images using local features', 'Coelho, Luis Pedro|Kangas, Joshua D|Naik, Armaghan W|Osuna-Highley, Elvira|Glory-Afshar, Estelle|Fuhrman, Margaret|Simha, Ramanuja|Berget, Peter B|Jarvik, Jonathan W|Murphy, Robert F', 'Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 37, 'HeLa Cells|Humans|Intracellular Space|Microscopy, Confocal|Microscopy, Fluorescence|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt392', '23836142', 'Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77782, 2013, 'bio', 'Allosite: a method for predicting allosteric sites', 'Huang, Wenkang|Lu, Shaoyong|Huang, Zhimin|Liu, Xinyi|Mou, Linkai|Luo, Yu|Zhao, Yanlong|Liu, Yaqin|Chen, Zhongjie|Hou, Tingjun|Zhang, Jian', 'Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China.', 'China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Allosteric Site|Drug Discovery|Ligands|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt399', '23842804', 'The use of allosteric modulators as preferred therapeutic agents against classic orthosteric ligands has colossal advantages, including higher specificity, fewer side effects and lower toxicity. Therefore, the computational prediction of allosteric sites in proteins is receiving increased attention in the field of drug discovery. Allosite is a newly developed automatic tool for the prediction of allosteric sites in proteins of interest and is now available through a web server.'),
(77783, 2013, 'bio', 'A computational method to preclude multistationarity in networks of interacting species', 'Feliu, Elisenda|Wiuf, Carsten', 'Department of Mathematical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark.|Department of Mathematical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark.', 'Denmark|Denmark', 21, 'Algorithms|Computational Biology|Models, Biological|Phosphorylation|RNA Interference|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt400', '23842805', 'Modeling and analysis of complex systems are important aspects of understanding systemic behavior. In the lack of detailed knowledge about a system, we often choose modeling equations out of convenience and search the (high-dimensional) parameter space randomly to learn about model properties. Qualitative modeling sidesteps the issue of choosing specific modeling equations and frees the inference from specific properties of the equations. We consider classes of ordinary differential equation (ODE) models arising from interactions of species/entities, such as (bio)chemical reaction networks or ecosystems. A class is defined by imposing mild assumptions on the interaction rates. In this framework, we investigate whether there can be multiple positive steady states in some ODE models in a given class.'),
(77784, 2013, 'bio', 'BioSmalltalk: a pure object system and library for bioinformatics', 'Morales, Hernán F|Giovambattista, Guillermo', 'Instituto de Genética Veterinaria IGEVET, CONICET La Plata-Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata B1900AVW, CC 296 Argentina. hernan.morales@gmail.com|Instituto de Genética Veterinaria IGEVET, CONICET La Plata-Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata B1900AVW, CC 296 Argentina. hernan.morales@gmail.com', 'Argentina|Argentina', 0, 'Computational Biology|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btt398', '23842806', 'We have developed BioSmalltalk, a new environment system for pure object-oriented bioinformatics programming. Adaptive end-user programming systems tend to become more important for discovering biological knowledge, as is demonstrated by the emergence of open-source programming toolkits for bioinformatics in the past years. Our software is intended to bridge the gap between bioscientists and rapid software prototyping while preserving the possibility of scaling to whole-system biology applications. BioSmalltalk performs better in terms of execution time and memory usage than Biopython and BioPerl for some classical situations.'),
(77785, 2013, 'bio', 'iLoops: a protein-protein interaction prediction server based on structural features', 'Planas-Iglesias, Joan|Marin-Lopez, Manuel A|Bonet, Jaume|Garcia-Garcia, Javier|Oliva, Baldo', 'Structural Bioinformatics Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Structural Bioinformatics Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Structural Bioinformatics Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Structural Bioinformatics Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.|Structural Bioinformatics Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Humans|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt401', '23842807', 'Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier.'),
(77786, 2013, 'bio', 'Naive binning improves phylogenomic analyses', 'Bayzid, Md Shamsuzzoha|Warnow, Tandy', 'Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA.', 'United States|United States', 82, 'Genes|Genomics|Phylogeny|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btt394', '23842808', 'Species tree estimation in the presence of incomplete lineage sorting (ILS) is a major challenge for phylogenomic analysis. Although many methods have been developed for this problem, little is understood about the relative performance of these methods when estimated gene trees are poorly estimated, owing to inadequate phylogenetic signal.'),
(77787, 2013, 'bio', 'nhmmer: DNA homology search with profile HMMs', 'Wheeler, Travis J|Eddy, Sean R', 'HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA.|HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA.', 'United States|United States', 0, 'Algorithms|DNA|DNA Transposable Elements|Genome, Human|Humans|Markov Chains|Probability|Sequence Alignment|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btt403', '23842809', 'Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome.'),
(77788, 2013, 'bio', 'A comparative analysis of algorithms for somatic SNV detection in cancer', 'Roberts, Nicola D|Kortschak, R Daniel|Parker, Wendy T|Schreiber, Andreas W|Branford, Susan|Scott, Hamish S|Glonek, Garique|Adelson, David L', 'School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.|School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 57, 'Algorithms|Exome|Genotyping Techniques|Humans|Mutation|Neoplasms|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btt375', '23842810', 'With the advent of relatively affordable high-throughput technologies, DNA sequencing of cancers is now common practice in cancer research projects and will be increasingly used in clinical practice to inform diagnosis and treatment. Somatic (cancer-only) single nucleotide variants (SNVs) are the simplest class of mutation, yet their identification in DNA sequencing data is confounded by germline polymorphisms, tumour heterogeneity and sequencing and analysis errors. Four recently published algorithms for the detection of somatic SNV sites in matched cancer-normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer-normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterized and compared to investigate the strengths and weaknesses of each SNV calling algorithm.'),
(77789, 2013, 'bio', 'MIG: Multi-Image Genome viewer', 'McGowan, Simon J|Hughes, Jim R|Han, Zong-Pei|Taylor, Stephen', 'Computational Biology Research Group and Molecular Haemotology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DS, UK.|Computational Biology Research Group and Molecular Haemotology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DS, UK.|Computational Biology Research Group and Molecular Haemotology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DS, UK.|Computational Biology Research Group and Molecular Haemotology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Base Sequence|Genome|Humans|Internet|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt406', '23842811', 'Multi-Image Genome (MIG) viewer is a web-based application for visualizing, querying and filtering many thousands of genome browser regions as well as for exporting the data in a variety of formats. This methodology has been used successfully to analyze ChIP-Seq data and RNA-Seq data and to detect somatic mutations in genome resequencing projects.'),
(77790, 2013, 'bio', 'A genomic random interval model for statistical analysis of genomic lesion data', 'Pounds, Stan|Cheng, Cheng|Li, Shaoyu|Liu, Zhifa|Zhang, Jinghui|Mullighan, Charles', 'Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org', 'United States|United States|United States|United States|United States|United States', 0, 'DNA Copy Number Variations|Genetic Loci|Genetic Variation|Genomics|Humans|Models, Statistical|Neoplasms|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Precursor T-Cell Lymphoblastic Leukemia-Lymphoma', 'Journal Article', '10.1093/bioinformatics/btt372', '23842812', 'Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all involved in a single specific biological process or function.'),
(77791, 2013, 'bio', 'Topologically inferring risk-active pathways toward precise cancer classification by directed random walk', 'Liu, Wei|Li, Chunquan|Xu, Yanjun|Yang, Haixiu|Yao, Qianlan|Han, Junwei|Shang, Desi|Zhang, Chunlong|Su, Fei|Li, Xiaoxi|Xiao, Yun|Zhang, Fan|Dai, Meng|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China', 41, 'Biomarkers, Tumor|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Risk|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btt373', '23842813', 'The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity.'),
(77792, 2013, 'bio', 'A new framework for computational protein design through cost function network optimization', 'Traoré, Seydou|Allouche, David|André, Isabelle|de Givry, Simon|Katsirelos, George|Schiex, Thomas|Barbe, Sophie', 'Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.|Université de Toulouse, 135 Avenue de Rangueil, Toulouse, France.', 'France|France|France|France|France|France|France', 0, 'Algorithms|Models, Molecular|Protein Conformation|Protein Engineering|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt374', '23842814', 'The main challenge for structure-based computational protein design (CPD) remains the combinatorial nature of the search space. Even in its simplest fixed-backbone formulation, CPD encompasses a computationally difficult NP-hard problem that prevents the exact exploration of complex systems defining large sequence-conformation spaces.'),
(77793, 2013, 'bio', 'pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling', 'Gil, Víctor A|Guallar, Víctor', 'Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, 08034 Barcelona, Spain.|Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, 08034 Barcelona, Spain.', 'Spain|Spain', 0, 'Algorithms|Data Interpretation, Statistical|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btt402', '23846743', 'We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.'),
(77794, 2013, 'bio', 'An integrated toolkit for accurate prediction and analysis of cis-regulatory motifs at a genome scale', 'Ma, Qin|Liu, Bingqiang|Zhou, Chuan|Yin, Yanbin|Li, Guojun|Xu, Ying', 'Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 22, 'Algorithms|Cluster Analysis|DNA|Escherichia coli K12|Genome|Genomics|Humans|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt397', '23846744', 'We present an integrated toolkit, BoBro2.0, for prediction and analysis of cis-regulatory motifs. This toolkit can (i) reliably identify statistically significant cis-regulatory motifs at a genome scale, (ii) accurately scan for all motif instances of a query motif in specified genomic regions using a novel method for P-value estimation, (iii) provide highly reliable comparisons and clustering of identified motifs, which takes into consideration the weak signals from the flanking regions of the motifs, and (iv) analyze co-occurring motifs in the regulatory regions.'),
(77795, 2013, 'bio', 'SICOP: identifying significant co-interaction patterns', 'Spitz, Andreas|Zweig, Katharina A|Horvát, Emoke-Ágnes', 'Graph Theory and Network Analysis Group, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, 69115 Heidelberg, Germany and Department of Computer Science, University of Science and Technology Kaiserslautern, 67663 Kaiserslautern, Germany.|Graph Theory and Network Analysis Group, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, 69115 Heidelberg, Germany and Department of Computer Science, University of Science and Technology Kaiserslautern, 67663 Kaiserslautern, Germany.|Graph Theory and Network Analysis Group, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, 69115 Heidelberg, Germany and Department of Computer Science, University of Science and Technology Kaiserslautern, 67663 Kaiserslautern, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|DNA|Models, Statistical|RNA|Random Allocation|Software Design', 'Journal Article', '10.1093/bioinformatics/btt408', '23846745', 'Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements of two different types such as the influence of different experimental conditions on cellular variables or the direct interaction between receptors and their activators/inhibitors. One of the major challenges in the analysis of such noisy datasets is the statistical evaluation of the relationship between any two elements of the same type. Here, we present SICOP (significant co-interaction patterns), an implementation of a method that provides such an evaluation based on the number of their common interaction partners, their so-called co-interaction. This general network analytic method, proved successful in diverse fields, provides a framework for assessing the significance of this relationship by comparison with the expected co-interaction in a suitable null model of the same bipartite graph. SICOP takes into consideration up to two distinct types of interactions such as up- or downregulation. The tool is written in Java and accepts several common input formats and supports different output formats, facilitating further analysis and visualization. Its key features include a user-friendly interface, easy installation and platform independence.'),
(77796, 2013, 'bio', 'Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs', 'LeGault, Laura H|Dewey, Colin N', 'Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.', 'United States|United States', 10, 'Algorithms|Alternative Splicing|Animals|Cell Line|Drosophila|Gene Expression Profiling|Humans|Models, Statistical|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt396', '23846746', 'Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.'),
(77797, 2013, 'bio', 'CoDNaS: a database of conformational diversity in the native state of proteins', 'Monzon, Alexander Miguel|Juritz, Ezequiel|Fornasari, María Silvina|Parisi, Gustavo', 'Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, B1876BXD, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, B1876BXD, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, B1876BXD, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, B1876BXD, Buenos Aires, Argentina.', 'Argentina|Argentina|Argentina|Argentina', 0, 'Databases, Protein|Internet|Ligands|Models, Molecular|Protein Processing, Post-Translational|Protein Structure, Tertiary|Proteins|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btt405', '23846747', 'Conformational diversity is a key concept in the understanding of different issues related with protein function such as the study of catalytic processes in enzymes, protein-protein recognition, protein evolution and the origins of new biological functions. Here, we present a database of proteins with different degrees of conformational diversity. Conformational Diversity of Native State (CoDNaS) is a redundant collection of three-dimensional structures for the same protein derived from protein data bank. Structures for the same protein obtained under different crystallographic conditions have been associated with snapshots of protein dynamism and consequently could characterize protein conformers. CoDNaS allows the user to explore global and local structural differences among conformers as a function of different parameters such as presence of ligand, post-translational modifications, changes in oligomeric states and differences in pH and temperature. Additionally, CoDNaS contains information about protein taxonomy and function, disorder level and structural classification offering useful information to explore the underlying mechanism of conformational diversity and its close relationship with protein function. Currently, CoDNaS has 122 122 structures integrating 12 684 entries, with an average of 9.63 conformers per protein.'),
(77798, 2013, 'bio', 'Modeling nucleosome position distributions from experimental nucleosome positioning maps', 'Schöpflin, Robert|Teif, Vladimir B|Müller, Oliver|Weinberg, Christin|Rippe, Karsten|Wedemann, Gero', 'Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.|Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.|Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.|Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.|Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.|Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Animals|Binding Sites|Cell Differentiation|Mice|Monte Carlo Method|Nucleosomes|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt404', '23846748', 'Recent experimental advancements allow determining positions of nucleosomes for complete genomes. However, the resulting nucleosome occupancy maps are averages of heterogeneous cell populations. Accordingly, they represent a snapshot of a dynamic ensemble at a single time point with an overlay of many configurations from different cells. To study the organization of nucleosomes along the genome and to understand the mechanisms of nucleosome translocation, it is necessary to retrieve features of specific conformations from the population average.'),
(77799, 2013, 'bio', 'Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming', 'Guziolowski, Carito|Videla, Santiago|Eduati, Federica|Thiele, Sven|Cokelaer, Thomas|Siegel, Anne|Saez-Rodriguez, Julio', 'École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.|École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.', 'France|France|France|France|France|France|France', 0, 'Cell Line, Tumor|Humans|Logic|Proteomics|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btt393', '23853063', 'Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.'),
(77800, 2013, 'bio', 'RACER: Rapid and accurate correction of errors in reads', 'Ilie, Lucian|Molnar, Michael', 'Department of Computer Science, University of Western Ontario, N6A 5B7 London, ON, Canada.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, ON, Canada.', 'Canada|Canada', 48, 'Animals|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btt407', '23853064', 'High-throughput next-generation sequencing technologies enable increasingly fast and affordable sequencing of genomes and transcriptomes, with a broad range of applications. The quality of the sequencing data is crucial for all applications. A significant portion of the data produced contains errors, and ever more efficient error correction programs are needed.'),
(77801, 2013, 'bio', 'Assessing association between protein truncating variants and quantitative traits', 'Rivas, Manuel A|Pirinen, Matti|Neville, Matthew J|Gaulton, Kyle J|Moutsianas, Loukas|GoT2D Consortium|Lindgren, Cecilia M|Karpe, Fredrik|McCarthy, Mark I|Donnelly, Peter', 'Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 4, 'Apolipoprotein C-III|Bayes Theorem|Diabetes Mellitus, Type 2|Exome|Genome, Human|Genotype|Humans|Internet|Models, Genetic|Mutation|Phenotype|Quantitative Trait Loci|Software Design|Triglycerides', 'Journal Article', '10.1093/bioinformatics/btt409', '23860716', 'In sequencing studies of common diseases and quantitative traits, power to test rare and low frequency variants individually is weak. To improve power, a common approach is to combine statistical evidence from several genetic variants in a region. Major challenges are how to do the combining and which statistical framework to use. General approaches for testing association between rare variants and quantitative traits include aggregating genotypes and trait values, referred to as collapsing, or using a score-based variance component test. However, little attention has been paid to alternative models tailored for protein truncating variants. Recent studies have highlighted the important role that protein truncating variants, commonly referred to as loss of function variants, may have on disease susceptibility and quantitative levels of biomarkers. We propose a Bayesian modelling framework for the analysis of protein truncating variants and quantitative traits.'),
(77802, 2013, 'bio', 'SW#-GPU-enabled exact alignments on genome scale', 'Korpar, Matija|Šikic, Mile', 'Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR 10000 Zagreb, Croatia and Bioinformatics Institute, A*STAR, #07-01 Matrix, 138671 Singapore.|Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR 10000 Zagreb, Croatia and Bioinformatics Institute, A*STAR, #07-01 Matrix, 138671 Singapore.', 'Croatia|Croatia', 0, 'Algorithms|Base Sequence|Genome|Internet|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt410', '23864730', 'We propose SW#, a new CUDA graphical processor unit-enabled and memory-efficient implementation of dynamic programming algorithm, for local alignment. It can be used as either a stand-alone application or a library. Although there are other graphical processor unit implementations of the Smith-Waterman algorithm, SW# is the only one publicly available that can produce sequence alignments on genome-wide scale. For long sequences, it is at least a few hundred times faster than a CPU version of the same algorithm.'),
(77803, 2013, 'bio', 'deltaGseg: macrostate estimation via molecular dynamics simulations and multiscale time series analysis', 'Low, Diana H P|Motakis, Efthymios', 'Institute of Molecular and Cell Biology, Epigenetics, Chromatin and Differentiation, 61 Biopolis Street, Proteos #03-06, Singapore 138673 and Bioinformatics Institute, Genome and Gene Expression Data Analysis, 30 Biopolis Street, Matrix #07-01, Singapore 138671.|Institute of Molecular and Cell Biology, Epigenetics, Chromatin and Differentiation, 61 Biopolis Street, Proteos #03-06, Singapore 138673 and Bioinformatics Institute, Genome and Gene Expression Data Analysis, 30 Biopolis Street, Matrix #07-01, Singapore 138671.', 'Singapore|Singapore', 0, 'Cluster Analysis|Humans|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btt413', '23864731', 'Binding free energy calculations obtained through molecular dynamics simulations reflect intermolecular interaction states through a series of independent snapshots. Typically, the free energies of multiple simulated series (each with slightly different starting conditions) need to be estimated. Previous approaches carry out this task by moving averages at certain decorrelation times, assuming that the system comes from a single conformation description of binding events. Here, we discuss a more general approach that uses statistical modeling, wavelets denoising and hierarchical clustering to estimate the significance of multiple statistically distinct subpopulations, reflecting potential macrostates of the system. We present the deltaGseg R package that performs macrostate estimation from multiple replicated series and allows molecular biologists/chemists to gain physical insight into the molecular details that are not easily accessible by experimental techniques.'),
(77804, 2013, 'bio', 'Twist-DNA: computing base-pair and bubble opening probabilities in genomic superhelical DNA', 'Jost, Daniel', 'Laboratoire de Physique, Ecole Normale Supérieure de Lyon, CNRS UMR 5672, 69007 Lyon, France.', 'France', 3, 'Base Pairing|DNA, Bacterial|DNA, Superhelical|Escherichia coli|Genomics|Probability|Software Design|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btt415', '23864732', 'Local opening of the DNA double helix is required in many fundamental biological processes and is, in part, controlled by the degree of superhelicity imposed in vivo by the protein machinery. In particular, positions of superhelically destabilized regions correlate with regulatory sites along the genome. Based on a self-consistent linearization of a thermodynamic model of superhelical DNA introduced by Benham, we have developed a program that predicts the locations of these regions by efficiently computing base-pair and bubble opening probabilities in genomic DNA. The program allows visualization of results in standard genome browsers to compare DNA opening properties with other available datasets.'),
(77805, 2013, 'bio', 'SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules', 'Maier, Josef|Adzhubei, Alexei A|Egge-Jacobsen, Wolfgang', 'IStLS, 78727 Oberndorf, Germany, Engelhardt Institute of Molecular Biology, Moscow 119991, Russia, Department of Molecular Biosciences, Glyconor Mass Spectrometry, University of Oslo, 0316 Oslo and Norbrain Mass Spectrometry Facility, Unit for Genome Dynamics, Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway.|IStLS, 78727 Oberndorf, Germany, Engelhardt Institute of Molecular Biology, Moscow 119991, Russia, Department of Molecular Biosciences, Glyconor Mass Spectrometry, University of Oslo, 0316 Oslo and Norbrain Mass Spectrometry Facility, Unit for Genome Dynamics, Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway.|IStLS, 78727 Oberndorf, Germany, Engelhardt Institute of Molecular Biology, Moscow 119991, Russia, Department of Molecular Biosciences, Glyconor Mass Spectrometry, University of Oslo, 0316 Oslo and Norbrain Mass Spectrometry Facility, Unit for Genome Dynamics, Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway.', 'Germany|Germany|Germany', 0, 'Amino Acid Sequence|Amino Acids|Internet|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt411', '23868956', 'Functional modules within protein sequences are often extracted by consensus sequence patterns representing a linear motif, however, other functional regions may only be described by combined features such as amino acid composition, profiles of amino acid properties and randomly distributed short sequence motifs. If only a small number of functional examples are well characterized, the researcher needs a tool to extract similar sequences for further investigation.'),
(77806, 2013, 'bio', 'Tiki, at the head of a new superfamily of enzymes', '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', 0, 'Amino Acid Sequence|Biocatalysis|Metalloproteases|Models, Molecular|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt412', '23868957', 'Tiki proteins appear to antagonize Wnt signalling pathway by acting as Wnt proteases, thereby affecting Wnt solubility by its amino-terminal cleavage. Tiki1 protease activity was shown to be metal ion-dependent and was inhibited by chelating agents and thus was tentatively proposed to be a metalloprotease. Nevertheless, Tiki proteins exhibit no detectable sequence similarity to previously described metalloproteases, but instead have been reported as being homologues of TraB proteins (Pfam ID: PF01963), a widely distributed family of unknown function and structure. Here, we show that Tiki proteins are members of a new superfamily of domains contained not just in TraB proteins, but also in erythromycin esterase (Pfam ID: PF05139), DUF399 (domain of unknown function 399, Pfam ID: PF04187) and MARTX toxins that contribute to host invasion and pathogenesis by bacteria. We establish the core fold of this enzymatic domain and its catalytic residues.'),
(77807, 2013, 'bio', 'Testing for presence of known and unknown molecules in imaging mass spectrometry', 'Alexandrov, Theodore|Bartels, Andreas', 'Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany. theodore@uni-bremen.de|Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany. theodore@uni-bremen.de', 'Germany|Germany', 20, 'Animals|Brain Chemistry|Image Processing, Computer-Assisted|Peptides|Rats|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btt388', '23873892', 'Imaging mass spectrometry has emerged in the past decade as a label-free, spatially resolved and multi-purpose bioanalytical technique for direct analysis of biological samples. However, solving two everyday data analysis problems still requires expert judgment: (i) the detection of unknown molecules and (ii) the testing for presence of known molecules.'),
(77808, 2013, 'bio', 'OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes', 'Tamborero, David|Gonzalez-Perez, Abel|Lopez-Bigas, Nuria', 'Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats ICREA, Passeig Lluis Companys, 23, 08010 Barcelona, Spain.|Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats ICREA, Passeig Lluis Companys, 23, 08010 Barcelona, Spain.|Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats ICREA, Passeig Lluis Companys, 23, 08010 Barcelona, Spain.', 'Spain|Spain|Spain', 0, 'Cluster Analysis|Genes, Neoplasm|Genomics|Humans|Mutation|Neoplasm Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt395', '23884480', 'Gain-of-function mutations often cluster in specific protein regions, a signal that those mutations provide an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours. We sought to determine the overall extent of this feature in cancer and the possibility to use this feature to identify drivers.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77809, 2013, 'bio', 'GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks', 'Camilo, Esther|Bovolenta, Luiz A|Acencio, Marcio L|Rybarczyk-Filho, José L|Castro, Mauro A A|Moreira, José C F|Lemke, Ney', 'Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.|Department of Physics and Biophysics, Institute of Biosciences, UNESP Univ Estadual Paulista, Botucatu, São Paulo, Brazil and Institute of Basic Science and Health, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Computational Biology|Gene Expression Regulation|Lung|Lung Neoplasms|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btt377', '23894138', 'Network-level visualization of functional data is a key aspect of both analysis and understanding of biological systems. In a continuing effort to create clear and integrated visualizations that facilitate the gathering of novel biological insights despite the overwhelming complexity of data, we present here the GrAph LANdscape VisualizaTion (GALANT), a Cytoscape plugin that builds functional landscapes onto biological networks. By using GALANT, it is possible to project any type of numerical data onto a network to create a smoothed data map resembling the network layout. As a Cytoscape plugin, GALANT is further improved by the functionalities of Cytoscape, the popular bioinformatics package for biological network visualization and data integration.'),
(77810, 2013, 'bio', 'mpMoRFsDB: a database of molecular recognition features in membrane proteins', 'Gypas, Foivos|Tsaousis, Georgios N|Hamodrakas, Stavros J', 'Faculty of Biology, Department of Cell Biology and Biophysics, University of Athens, Panepistimiopolis, Athens 157 01, Greece.|Faculty of Biology, Department of Cell Biology and Biophysics, University of Athens, Panepistimiopolis, Athens 157 01, Greece.|Faculty of Biology, Department of Cell Biology and Biophysics, University of Athens, Panepistimiopolis, Athens 157 01, Greece.', 'Greece|Greece|Greece', 6, 'Databases, Genetic|Internet|Membrane Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt427', '23894139', 'Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein-protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute xe2x88xbc30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a proteins topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein-protein interactions in membrane proteins.'),
(77811, 2013, 'bio', 'MaGnET: Malaria Genome Exploration Tool', 'Sharman, Joanna L|Gerloff, Dietlind L', 'The University/BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK. joanna.sharman@ed.ac.uk|The University/BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK. joanna.sharman@ed.ac.uk', 'United Kingdom|United Kingdom', 0, 'Genome, Protozoan|Genomics|Plasmodium falciparum|Protozoan Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt384', '23894142', 'The Malaria Genome Exploration Tool (MaGnET) is a software tool enabling intuitive exploration-style visualization of functional genomics data relating to the malaria parasite, Plasmodium falciparum. MaGnET provides innovative integrated graphic displays for different datasets, including genomic location of genes, mRNA expression data, protein-protein interactions and more. Any selection of genes to explore made by the user is easily carried over between the different viewers for different datasets, and can be changed interactively at any point (without returning to a search).'),
(77812, 2013, 'bio', 'Indel and Carryforward Correction (ICC): a new analysis approach for processing 454 pyrosequencing data', 'Deng, Wenjie|Maust, Brandon S|Westfall, Dylan H|Chen, Lennie|Zhao, Hong|Larsen, Brendan B|Iyer, Shyamala|Liu, Yi|Mullins, James I', 'Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 14, 'Algorithms|Base Sequence|Cluster Analysis|HIV Infections|HIV-1|Humans|Mutation|Polymerase Chain Reaction|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt434', '23900188', 'Pyrosequencing technology provides an important new approach to more extensively characterize diverse sequence populations and detect low frequency variants. However, the promise of this technology has been difficult to realize, as careful correction of sequencing errors is crucial to distinguish rare variants (xe2x88xbc1%) in an infected host with high sensitivity and specificity.'),
(77813, 2013, 'bio', 'Near-optimal experimental design for model selection in systems biology', 'Busetto, Alberto Giovanni|Hauser, Alain|Krummenacher, Gabriel|Sunnåker, Mikael|Dimopoulos, Sotiris|Ong, Cheng Soon|Stelling, Jörg|Buhmann, Joachim M', 'Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.|Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Animals|Models, Theoretical|Probability|Research Design|Signal Transduction|Software|Systems Biology|TOR Serine-Threonine Kinases', 'Journal Article', '10.1093/bioinformatics/btt436', '23900189', 'Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points.'),
(77814, 2013, 'bio', 'Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets', 'Wang, Jijie|Lam, Henry', 'Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.|Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.', 'Hong Kong|Hong Kong', 6, 'Algorithms|Cell Line, Tumor|Chromatography, Liquid|Databases, Protein|Humans|Mass Spectrometry|Proteome|Proteomics|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btt435', '23904508', 'Liquid chromatography coupled to mass spectrometry (LC-MS) is the dominant technological platform for proteomics. An LC-MS analysis of a complex biological sample can be visualized as a map of which the positional coordinates are the mass-to-charge ratio (m/z) and chromatographic retention time (RT) of the chemical species profiled. Label-free quantitative proteomics requires the alignment and comparison of multiple LC-MS maps to ascertain the reproducibility of experiments or reveal proteome changes under different conditions. The main challenge in this task lies in correcting inevitable RT shifts. Similar, but not identical, LC instruments and settings can cause peptides to elute at very different times and sometimes in a different order, violating the assumptions of many state-of-the-art alignment tools. To meet this challenge, we developed LWBMatch, a new algorithm based on weighted bipartite matching. Unlike existing tools, which search for accurate warping functions to correct RT shifts, we directly seek a peak-to-peak mapping by maximizing a global similarity function between two LC-MS maps. For alignment tasks with large RT shifts (>500 s), an approximate warping function is determined by locally weighted scatterplot smoothing of potential matched features, detected using a novel voting scheme based on co-elution. For validation, we defined the ground truth for alignment success based on tandem mass spectrometry identifications from sequence searching. We showed that our method outperforms several existing tools in terms of precision and recall, and is capable of aligning maps from different instruments and settings.'),
(77815, 2013, 'bio', 'Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily', 'Goldberg, Jonathan M|Griggs, Allison D|Smith, Janet L|Haas, Brian J|Wortman, Jennifer R|Zeng, Qiandong', 'Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.|Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.|Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.|Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.|Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.|Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States|United States|United States', 22, 'Algorithms|Eukaryotic Cells|Genome|Internet|Position-Specific Scoring Matrices|Protein Kinases|Proteome|Software Design', 'Journal Article', '10.1093/bioinformatics/btt419', '23904509', 'Kinases of the eukaryotic protein kinase superfamily are key regulators of most aspects eukaryotic cellular behavior and have provided several drug targets including kinases dysregulated in cancers. The rapid increase in the number of genomic sequences has created an acute need to identify and classify members of this important class of enzymes efficiently and accurately.'),
(77816, 2013, 'bio', 'SPNConverter: a new link between static and dynamic complex network analysis', 'Dent, Jennifer E|Yang, Xinyi|Nardini, Christine', 'Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, PR China and Division of Community Health Sciences, St. Georges University of London, Cranmer Terrace, London, SW17 0RE, UK.|Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, PR China and Division of Community Health Sciences, St. Georges University of London, Cranmer Terrace, London, SW17 0RE, UK.|Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, PR China and Division of Community Health Sciences, St. Georges University of London, Cranmer Terrace, London, SW17 0RE, UK.', 'China|China|China', 0, 'Automation|Internet|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt421', '23908262', 'The signaling Petri net (SPN) simulator, designed to provide insights into the trends of molecules activity levels in response to an external stimulus, contributes to the systems biology necessity of analyzing the dynamics of large-scale cellular networks. Implemented into the freely available software, BioLayout Express(3D), the simulator is publicly available and easy to use, provided the input files are prepared in the GraphML format, typically using the network editing software, yEd, and standards specific to the software. However, analysis of complex networks represented using other systems biology formatting languages (on which popular software, such as CellDesigner and Cytoscape, are based) requires manual manipulation, a step that is prone to error and limits the use of the SPN simulator in BioLayout Express(3D). To overcome this, we present a Cytoscape plug-in that enables users to automatically convert networks for analysis with the SPN simulator from the standard systems biology markup language. The automation of this step opens the SPN simulator to a far larger user group than has previously been possible.'),
(77817, 2013, 'bio', 'MeltDB 20-advances of the metabolomics software system', 'Kessler, Nikolas|Neuweger, Heiko|Bonte, Anja|Langenkämper, Georg|Niehaus, Karsten|Nattkemper, Tim W|Goesmann, Alexander', 'Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.|Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 46, 'Cluster Analysis|Data Mining|Databases, Genetic|Internet|Metabolomics|Software Design', 'Journal Article', '10.1093/bioinformatics/btt414', '23918246', 'The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization.'),
(77818, 2013, 'bio', 'CellMissy: a tool for management, storage and analysis of cell migration data produced in wound healing-like assays', 'Masuzzo, Paola|Hulstaert, Niels|Huyck, Lynn|Ampe, Christophe|Van Troys, Marleen|Martens, Lennart', 'Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB and Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent 9000, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 10, 'Cell Movement|Databases, Chemical|Image Processing, Computer-Assisted|Programming Languages|Software|Wound Healing', 'Journal Article', '10.1093/bioinformatics/btt437', '23918247', 'Automated image processing has allowed cell migration research to evolve to a high-throughput research field. As a consequence, there is now an unmet need for data management in this domain. The absence of a generic management system for the quantitative data generated in cell migration assays results in each dataset being treated in isolation, making data comparison across experiments difficult. Moreover, by integrating quality control and analysis capabilities into such a data management system, the common practice of having to manually transfer data across different downstream analysis tools will be markedly sped up and made more robust. In addition, access to a data management solution creates gateways for data standardization, meta-analysis and structured public data dissemination. We here present CellMissy, a cross-platform data management system for cell migration data with a focus on wound healing data. CellMissy simplifies and automates data management, storage and analysis from the initial experimental set-up to data exploration.'),
(77819, 2013, 'bio', 'Pclust: protein network visualization highlighting experimental data', 'Li, Wenlin|Kinch, Lisa N|Grishin, Nick V', 'Departments of Biophysics and Biochemistry and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Departments of Biophysics and Biochemistry and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Departments of Biophysics and Biochemistry and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.', 'United States|United States|United States', 0, 'Animals|Databases, Genetic|Internet|Mice|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt451', '23918248', 'One approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. reference proteins. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such reference proteins and providing access to their full information in source databases, e.g. articles in PubMed. The identification of reference proteins and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest.'),
(77820, 2013, 'bio', 'The BioPAX Validator', 'Rodchenkov, Igor|Demir, Emek|Sander, Chris|Bader, Gary D', 'The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA.|The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA.|The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA.|The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA.', 'Canada|Canada|Canada|Canada', 5, 'Documentation|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt452', '23918249', 'BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation-detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs >100 classes of consistency checks.'),
(77821, 2013, 'bio', 'Scaffold network generator: a tool for mining molecular structures', 'Matlock, Matt K|Zaretzki, Jed M|Swamidass, S Joshua', 'Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 1097, Whitaker Hall, St. Louis, MO 63130, USA.|Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 1097, Whitaker Hall, St. Louis, MO 63130, USA.|Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 1097, Whitaker Hall, St. Louis, MO 63130, USA.', 'United States|United States|United States', 13, 'Data Mining|Drug Discovery|High-Throughput Screening Assays|Molecular Structure|Small Molecule Libraries|Software', 'Journal Article', '10.1093/bioinformatics/btt448', '23918250', 'Scaffold network generator (SNG) is an open-source command-line utility that computes the hierarchical network of scaffolds that define a large set of input molecules. Scaffold networks are useful for visualizing, analysing and understanding the chemical data that is increasingly available through large public repositories like PubChem. For example, some groups have used scaffold networks to identify missed-actives in high-throughput screens of small molecules with bioassays. Substantially improving on existing software, SNG is robust enough to work on millions of molecules at a time with a simple command-line interface.'),
(77822, 2013, 'bio', 'Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities', 'Taghavi, Zeinab|Movahedi, Narjes S|Draghici, Sorin|Chitsaz, Hamidreza', 'Department of Computer Science and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA.|Department of Computer Science and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA.|Department of Computer Science and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA.|Department of Computer Science and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA.', 'United States|United States|United States|United States', 6, 'Algorithms|Base Sequence|Genome, Microbial|Humans|Intestines|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btt420', '23918251', 'Identification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells.'),
(77823, 2013, 'bio', 'A distance-based test of association between paired heterogeneous genomic data', 'Minas, Christopher|Curry, Edward|Montana, Giovanni', 'Department of Imaging Sciences, Institute of Clinical Sciences, Hammersmith Campus, Statistics Section, Department of Mathematics, South Kensington Campus and Department of Surgery and Cancer, Ovarian Cancer Action Research Centre, Hammersmith Campus, Imperial College London, London W12 0NN, UK.|Department of Imaging Sciences, Institute of Clinical Sciences, Hammersmith Campus, Statistics Section, Department of Mathematics, South Kensington Campus and Department of Surgery and Cancer, Ovarian Cancer Action Research Centre, Hammersmith Campus, Imperial College London, London W12 0NN, UK.|Department of Imaging Sciences, Institute of Clinical Sciences, Hammersmith Campus, Statistics Section, Department of Mathematics, South Kensington Campus and Department of Surgery and Cancer, Ovarian Cancer Action Research Centre, Hammersmith Campus, Imperial College London, London W12 0NN, UK.', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Biometry|Female|Genomics|Humans|Monte Carlo Method|Mutation|Ovarian Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt450', '23918252', 'Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at different scales or represented by different data structures.'),
(77824, 2014, 'bio', 'Decoy-free protein-level false discovery rate estimation', 'Teng, Ben|Huang, Ting|He, Zengyou', 'School of Software, Dalian University of Technology, Dalian 116621, China.|School of Software, Dalian University of Technology, Dalian 116621, China.|School of Software, Dalian University of Technology, Dalian 116621, China.', 'China|China|China', 6, 'Algorithms|Humans|Linear Models|Peptides|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/btt431', '23926225', 'Statistical validation of protein identifications is an important issue in shotgun proteomics. The false discovery rate (FDR) is a powerful statistical tool for evaluating the protein identification result. Several research efforts have been made for FDR estimation at the protein level. However, there are still certain drawbacks in the existing FDR estimation methods based on the target-decoy strategy.'),
(77825, 2013, 'bio', 'nCal: an R package for non-linear calibration', 'Fong, Youyi|Sebestyen, Krisztian|Yu, Xuesong|Gilbert, Peter|Self, Steve', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.', 'United States|United States|United States|United States|United States', 10, 'Bayes Theorem|Calibration|Nonlinear Dynamics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btt456', '23926226', 'Non-linear calibration is a widely used method for quantifying biomarkers wherein concentration-response curves estimated using samples of known concentrations are used to predict the biomarker concentrations in the samples of interest. The R package nCal fills an important gap in the open source, stand-alone software for performing non-linear calibration. For curve fitting, nCal provides a new implementation of a robust, Bayesian hierarchical five-parameter logistic model. nCal supports a simple graphical user interface that can be used by laboratory scientists, and contains functionality for importing data from the multiplex bead array assay instrumentation.'),
(77826, 2013, 'bio', 'OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes', 'Yau, Christopher', 'Department of Mathematics, South Kensington Campus, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom', 0, 'DNA Copy Number Variations|Genome|Genomics|Humans|Loss of Heterozygosity|Neoplasms|Ploidies|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1093/bioinformatics/btt416', '23926227', 'Recent major cancer genome sequencing studies have used whole-genome sequencing to detect various types of genomic variation. However, a number of these studies have continued to rely on SNP array information to provide additional results for copy number and loss-of-heterozygosity estimation and assessing tumour purity. OncoSNP-SEQ is a statistical model-based approach for inferring copy number profiles directly from high-coverage whole genome sequencing data that is able to account for unknown tumour purity and ploidy.'),
(77827, 2013, 'bio', 'Exploring the role of human miRNAs in virus-host interactions using systematic overlap analysis', 'Li, Zhenpeng|Cui, Xiuliang|Li, Fei|Li, Peng|Ni, Ming|Wang, Shengqi|Bo, Xiaochen', 'Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.', 'China|China|China|China|China|China|China', 6, 'Gene Expression Regulation|Host-Pathogen Interactions|Humans|MicroRNAs|Transcription Factors|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/btt391', '23926228', 'Human miRNAs have recently been found to have important roles in viral replication. Understanding the patterns and details of human miRNA interactions during virus-host interactions may help uncover novel antiviral therapies. Based on the abundance of knowledge available regarding protein-protein interactions (PPI), virus-host protein interactions, experimentally validated human miRNA-target pairs and transcriptional regulation of human miRNAs, it is possible to explore the complex regulatory network that exists between viral proteins and human miRNAs at the system level.'),
(77828, 2013, 'bio', 'WebRASP: a server for computing energy scores to assess the accuracy and stability of RNA 3D structures', 'Norambuena, Tomas|Cares, Jorge F|Capriotti, Emidio|Melo, Francisco', 'Facultad de Ciencias Biologicas, Departamento de Genetica Molecular y Microbiologia, Pontificia Universidad Catolica de Chile, Alameda 340, Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chile and Division of Informatics, Department of Pathology, University of Alabama at Birmingham, 619 19 st. south, Birmingham, AL 35249, USA.|Facultad de Ciencias Biologicas, Departamento de Genetica Molecular y Microbiologia, Pontificia Universidad Catolica de Chile, Alameda 340, Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chile and Division of Informatics, Department of Pathology, University of Alabama at Birmingham, 619 19 st. south, Birmingham, AL 35249, USA.|Facultad de Ciencias Biologicas, Departamento de Genetica Molecular y Microbiologia, Pontificia Universidad Catolica de Chile, Alameda 340, Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chile and Division of Informatics, Department of Pathology, University of Alabama at Birmingham, 619 19 st. south, Birmingham, AL 35249, USA.|Facultad de Ciencias Biologicas, Departamento de Genetica Molecular y Microbiologia, Pontificia Universidad Catolica de Chile, Alameda 340, Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chile and Division of Informatics, Department of Pathology, University of Alabama at Birmingham, 619 19 st. south, Birmingham, AL 35249, USA.', 'Chile|Chile|Chile|Chile', 6, 'Internet|Models, Molecular|Nucleic Acid Conformation|RNA|RNA Stability|Software', 'Journal Article', '10.1093/bioinformatics/btt441', '23929030', 'The understanding of the biological role of RNA molecules has changed. Although it is widely accepted that RNAs play important regulatory roles without necessarily coding for proteins, the functions of many of these non-coding RNAs are unknown. Thus, determining or modeling the 3D structure of RNA molecules as well as assessing their accuracy and stability has become of great importance for characterizing their functional activity. Here, we introduce a new web application, WebRASP, that uses knowledge-based potentials for scoring RNA structures based on distance-dependent pairwise atomic interactions. This web server allows the users to upload a structure in PDB format, select several options to visualize the structure and calculate the energy profile. The server contains online help, tutorials and links to other related resources. We believe this server will be a useful tool for predicting and assessing the quality of RNA 3D structures.'),
(77829, 2013, 'bio', 'PARSEC: PAtteRn SEarch and Contextualization', 'Allot, Alexis|Anno, Yannick-Noël|Poidevin, Laetitia|Ripp, Raymond|Poch, Olivier|Lecompte, Odile', 'Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.|Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.|Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.|Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.|Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.|Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS/INSERM/UDS, 67404, Illkirch, France.', 'France|France|France|France|France|France', 0, 'Algorithms|Genome|Genomics|Humans|Internet|Nonlinear Dynamics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btt455', '23929031', 'We present PARSEC (PAtteRn Search and Contextualization), a new open source platform for guided discovery, allowing localization and biological characterization of short genomic sites in entire eukaryotic genomes. PARSEC can search for a sequence or a degenerated pattern. The retrieved set of genomic sites can be characterized in terms of (i) conservation in model organisms, (ii) genomic context (proximity to genes) and (iii) function of neighboring genes. These modules allow the user to explore, visualize, filter and extract biological knowledge from a set of short genomic regions such as transcription factor binding sites.'),
(77830, 2013, 'bio', 'ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq', 'Kucukural, Alper|Özadam, Hakan|Singh, Guramrit|Moore, Melissa J|Cenik, Can', 'Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605 and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605 and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605 and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605 and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Biochemistry and Molecular Pharmacology, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605 and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States', 25, 'Algorithms|Internet|RNA-Binding Proteins|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt428', '23929032', 'Unlike DNA, RNA abundances can vary over several orders of magnitude. Thus, identification of RNA-protein binding sites from high-throughput sequencing data presents unique challenges. Although peak identification in ChIP-Seq data has been extensively explored, there are few bioinformatics tools tailored for peak calling on analogous datasets for RNA-binding proteins. Here we describe ASPeak (abundance sensitive peak detection algorithm), an implementation of an algorithm that we previously applied to detect peaks in exon junction complex RNA immunoprecipitation in tandem experiments. Our peak detection algorithm yields stringent and robust target sets enabling sensitive motif finding and downstream functional analyses.'),
(77831, 2013, 'bio', 'On representative and illustrative comparisons with real data in bioinformatics: response to the letter to the editor by Smith et al', 'Boulesteix, Anne-Laure', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, 81377 Munich, Germany.', 'Germany', 17, 'Algorithms', 'Letter', '10.1093/bioinformatics/btt458', '23929033', '-'),
(77832, 2013, 'bio', 'TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors', 'Chawla, Konika|Tripathi, Sushil|Thommesen, Liv|Lægreid, Astrid|Kuiper, Martin', 'Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway and Department of Technology, Sør-Trøndelag, University College, N-7004 Trondheim, Norway.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway and Department of Technology, Sør-Trøndelag, University College, N-7004 Trondheim, Norway.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway and Department of Technology, Sør-Trøndelag, University College, N-7004 Trondheim, Norway.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway and Department of Technology, Sør-Trøndelag, University College, N-7004 Trondheim, Norway.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway and Department of Technology, Sør-Trøndelag, University College, N-7004 Trondheim, Norway.', 'Norway|Norway|Norway|Norway|Norway', 34, 'Animals|DNA|Databases, Genetic|Humans|Internet|Protein Binding|RNA Polymerase II|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt432', '23933972', 'Gene regulatory network assembly and analysis requires high-quality knowledge sources that cover functional aspects of the various components of the gene regulatory machinery. A multiplicity of resources exists with information about mammalian transcription factors (TFs), yet, only few of these provide sufficiently accurate classifications of the functional roles of individual TFs, or standardized evidence that would justify the information on which these functional classifications are based. We compiled the list of all putative TFs from nine different resources, ignored factors such as general TFs, mediator complexes and chromatin modifiers, and for the remaining factors checked the available literature for references that support their function as a true sequence-specific DNA-binding RNA polymerase II TF (DbTF). The results are available in the TFcheckpoint database, an exhaustive collection of TFs annotated according to experimental and other evidence on their function as true DbTFs. TFcheckpoint.org provides a high-quality and comprehensive knowledge source for genome-scale regulatory network studies.'),
(77833, 2013, 'bio', 'Analysis of base-pairing probabilities of RNA molecules involved in protein-RNA interactions', 'Iwakiri, Junichi|Kameda, Tomoshi|Asai, Kiyoshi|Hamada, Michiaki', 'Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.', 'Japan|Japan|Japan|Japan', 5, 'Amino Acids|Base Pairing|Hydrogen Bonding|Probability|Proteins|RNA', 'Journal Article', '10.1093/bioinformatics/btt453', '23933973', 'Understanding the details of protein-RNA interactions is important to reveal the functions of both the RNAs and the proteins. In these interactions, the secondary structures of the RNAs play an important role. Because RNA secondary structures in protein-RNA complexes are variable, considering the ensemble of RNA secondary structures is a useful approach. In particular, recent studies have supported the idea that, in the analysis of RNA secondary structures, the base-pairing probabilities (BPPs) of RNAs (i.e. the probabilities of forming a base pair in the ensemble of RNA secondary structures) provide richer and more robust information about the structures than a single RNA secondary structure, for example, the minimum free energy structure or a snapshot of structures in the Protein Data Bank. However, there has been no investigation of the BPPs in protein-RNA interactions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77834, 2013, 'bio', 'Badger--an accessible genome exploration environment', 'Elsworth, Ben|Jones, Martin|Blaxter, Mark', 'Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.|Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.|Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Genes|Genome|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt466', '23940251', 'High-quality draft genomes are now easy to generate, as sequencing and assembly costs have dropped dramatically. However, building a user-friendly searchable Web site and database for a newly annotated genome is not straightforward. Here we present Badger, a lightweight and easy-to-install genome exploration environment designed for next generation non-model organism genomes.'),
(77835, 2013, 'bio', 'ROBNCA: robust network component analysis for recovering transcription factor activities', 'Noor, Amina|Ahmad, Aitzaz|Serpedin, Erchin|Nounou, Mohamed|Nounou, Hazem', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.', 'Qatar|Qatar|Qatar|Qatar|Qatar', 13, 'Algorithms|Cell Cycle|Escherichia coli|Gene Expression|Neural Networks (Computer)|Nonlinear Dynamics|Reproducibility of Results|Saccharomyces cerevisiae|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt433', '23940252', 'Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications.'),
(77836, 2013, 'bio', 'BLAD: a comprehensive database of widely circulated beta-lactamases', 'Danishuddin, Mohd|Hassan Baig, Mohd|Kaushal, Lalima|Khan, Asad U', 'Interdisciplinary Biotechnology Unit, A. M. U., Aligarh 202002, India.|Interdisciplinary Biotechnology Unit, A. M. U., Aligarh 202002, India.|Interdisciplinary Biotechnology Unit, A. M. U., Aligarh 202002, India.|Interdisciplinary Biotechnology Unit, A. M. U., Aligarh 202002, India.', 'India|India|India|India', 12, 'Databases, Nucleic Acid|Internet|Software|beta-Lactamases', 'Journal Article', '10.1093/bioinformatics/btt417', '23943635', 'Beta-lactamases confer resistance to a broad range of antibiotics and inhibitors by accumulating mutations. The number of beta-lactamases and their variants is steadily increasing. The horizontal gene transfer likely plays a major role in dissemination of these markers to new environments and hosts. Moreover, information about the beta-lactamase classes and their variants was scattered. Categorizing all these classes and their associated variants along with their epidemiology and resistance pattern information on one platform could be helpful to the researcher working on multidrug-resistant bacteria. Thus, the beta-lactamase database (BLAD) has been developed to provide comprehensive information (epidemiology and resistance pattern) on beta-lactamases. Beta-lactamase gene sequences in BLAD are linked with structural data, phenotypic data (i.e. antibiotic resistance) and literature references to experimental studies. In summary, BLAD integrates information that may provide insight into the epidemiology of multidrug resistance and enable the designing of novel drug candidates.'),
(77837, 2013, 'bio', 'DRAW+SneakPeek: analysis workflow and quality metric management for DNA-seq experiments', 'Lin, Chiao-Feng|Valladares, Otto|Childress, D Micah|Klevak, Egor|Geller, Evan T|Hwang, Yih-Chii|Tsai, Ellen A|Schellenberg, Gerard D|Wang, Li-San', 'Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, Department of Physics, University of Washington, Seattle, WA 98105, USA, Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Pathology and Laboratory Medicine, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Biometry|DNA|Internet|Programming Languages|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1093/bioinformatics/btt422', '23943636', 'We report our new DRAW+SneakPeek software for DNA-seq analysis. DNA resequencing analysis workflow (DRAW) automates the workflow of processing raw sequence reads including quality control, read alignment and variant calling on high-performance computing facilities such as Amazon elastic compute cloud. SneakPeek provides an effective interface for reviewing dozens of quality metrics reported by DRAW, so users can assess the quality of data and diagnose problems in their sequencing procedures. Both DRAW and SneakPeek are freely available under the MIT license, and are available as Amazon machine images to be used directly on Amazon cloud with minimal installation.'),
(77838, 2013, 'bio', 'Joint haplotype phasing and genotype calling of multiple individuals using haplotype informative reads', 'Zhang, Kui|Zhi, Degui', 'Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.', 'United States|United States', 5, 'Algorithms|Alleles|Genome, Human|Genotype|Haplotypes|Humans|Markov Chains|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt418', '23943637', 'Hidden Markov model, based on Li and Stephens model that takes into account chromosome sharing of multiple individuals, results in mainstream haplotype phasing algorithms for genotyping arrays and next-generation sequencing (NGS) data. However, existing methods based on this model assume that the allele count data are independently observed at individual sites and do not consider haplotype informative reads, i.e. reads that cover multiple heterozygous sites, which carry useful haplotype information. In our previous work, we developed a new hidden Markov model to incorporate a two-site joint emission term that captures the haplotype information across two adjacent sites. Although our model improves the accuracy of genotype calling and haplotype phasing, haplotype information in reads covering non-adjacent sites and/or more than two adjacent sites is not used because of the severe computational burden.'),
(77839, 2013, 'bio', 'isomiRID: a framework to identify microRNA isoforms', 'de Oliveira, Luiz Felipe Valter|Christoff, Ana Paula|Margis, Rogerio', 'Genetics and Molecular Biology Graduation Program and Department of Biophysics, Center of Biotechnology, Universidade Federal do Rio Grande do Sul - UFRGS, Avenida Bento Goncalves 9500, Predio 43431, Sala 213, Porto Alegre, Brasil, CEP 91501-970.|Genetics and Molecular Biology Graduation Program and Department of Biophysics, Center of Biotechnology, Universidade Federal do Rio Grande do Sul - UFRGS, Avenida Bento Goncalves 9500, Predio 43431, Sala 213, Porto Alegre, Brasil, CEP 91501-970.|Genetics and Molecular Biology Graduation Program and Department of Biophysics, Center of Biotechnology, Universidade Federal do Rio Grande do Sul - UFRGS, Avenida Bento Goncalves 9500, Predio 43431, Sala 213, Porto Alegre, Brasil, CEP 91501-970.', '-|-|-', 24, 'Base Sequence|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Protein Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt424', '23946501', 'MicroRNAs (miRNAs) have been extensively studied owing to their important regulatory roles in genic expression. An increasingly number of reports are performing extensive data mining in small RNA sequencing libraries to detect miRNAs isoforms and also 5 and 3 post-transcriptional nucleotide additions, as well as edited miRNAs sequences. A ready to use pipeline, isomiRID, was developed to standardize and automatize the search for miRNAs isoforms in high-throughput small RNA sequencing libraries.'),
(77840, 2013, 'bio', 'High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling', 'Yang, Jing|Jang, Richard|Zhang, Yang|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.', 'China|China|China|China', 38, 'Amino Acid Sequence|Databases, Protein|Humans|Internet|Models, Molecular|Protein Structure, Secondary|Protein Structure, Tertiary|Receptors, G-Protein-Coupled|Software', 'Journal Article', '10.1093/bioinformatics/btt440', '23946502', 'Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize.'),
(77841, 2013, 'bio', 'omiRas: a Web server for differential expression analysis of miRNAs derived from small RNA-Seq data', 'Müller, Sören|Rycak, Lukas|Winter, Peter|Kahl, Günter|Koch, Ina|Rotter, Björn', 'Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.|Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.|Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.|Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.|Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.|Plant Molecular Biology, Institute of Molecular BioSciences, University of Frankfurt am Main, Marie-Curie-Str.9, 60439 Frankfurt am Main, GenXPro GmbH, Frankfurt Innovation Center Biotechnology, Altenhöferallee 3, 60438 Frankfurt am Main and Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence, Frankfurt Macromolecular Complexes, Robert-Mayer-Strasse 11-15, 60325 Frankfurt am Main, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 51, 'Animals|Gene Library|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|RNA, Small Nucleolar|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt457', '23946503', 'Small RNA deep sequencing is widely used to characterize non-coding RNAs (ncRNAs) differentially expressed between two conditions, e.g. healthy and diseased individuals and to reveal insights into molecular mechanisms underlying condition-specific phenotypic traits. The ncRNAome is composed of a multitude of RNAs, such as transfer RNA, small nucleolar RNA and microRNA (miRNA), to name few. Here we present omiRas, a Web server for the annotation, comparison and visualization of interaction networks of ncRNAs derived from next-generation sequencing experiments of two different conditions. The Web tool allows the user to submit raw sequencing data and results are presented as: (i) static annotation results including length distribution, mapping statistics, alignments and quantification tables for each library as well as lists of differentially expressed ncRNAs between conditions and (ii) an interactive network visualization of user-selected miRNAs and their target genes based on the combination of several miRNA-mRNA interaction databases.'),
(77842, 2013, 'bio', 'SubNet: a Java application for subnetwork extraction', 'Lemetre, Christophe|Zhang, Quanwei|Zhang, Zhengdong D', 'Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA.', 'United States|United States|United States', 12, 'Algorithms|DNA|DNA Damage|Databases, Protein|Humans|Internet|Proteins|Software Design', 'Journal Article', '10.1093/bioinformatics/btt430', '23946504', 'The extraction of targeted subnetworks is a powerful way to identify functional modules and pathways within complex networks. Here, we present SubNet, a Java-based stand-alone program for extracting subnetworks, given a basal network and a set of selected nodes. Designed with a graphical user-friendly interface, SubNet combines four different extraction methods, which offer the possibility to interrogate a biological network according to the question investigated. Of note, we developed a method based on the highly successful Google PageRank algorithm to extract the subnetwork using the node centrality metric, to which possible node weights of the selected genes can be incorporated.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77843, 2013, 'bio', 'Imputation of coding variants in African Americans: better performance using data from the exome sequencing project', 'Duan, Qing|Liu, Eric Yi|Auer, Paul L|Zhang, Guosheng|Lange, Ethan M|Jun, Goo|Bizon, Chris|Jiao, Shuo|Buyske, Steven|Franceschini, Nora|Carlson, Chris S|Hsu, Li|Reiner, Alex P|Peters, Ulrike|Haessler, Jeffrey|Curtis, Keith|Wassel, Christina L|Robinson, Jennifer G|Martin, Lisa W|Haiman, Christopher A|Le Marchand, Loic|Matise, Tara C|Hindorff, Lucia A|Crawford, Dana C|Assimes, Themistocles L|Kang, Hyun Min|Heiss, Gerardo|Jackson, Rebecca D|Kooperberg, Charles|Wilson, James G|Abecasis, Gonçalo R|North, Kari E|Nickerson, Deborah A|Lange, Leslie A|Li, Yun', 'Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, US', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 0, 'African Americans|Exome|Gene Frequency|Genetic Variation|Genome, Human|Genome-Wide Association Study|Haplotypes|Humans|Phenotype|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt477', '23956302', 'Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2s two-panel combination.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77844, 2013, 'bio', 'SPOCS: software for predicting and visualizing orthology/paralogy relationships among genomes', 'Curtis, Darren S|Phillips, Aaron R|Callister, Stephen J|Conlan, Sean|McCue, Lee Ann', 'Computational & Statistical Analytics Division, Pacific Northwest National Laboratory, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA, Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA and Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational & Statistical Analytics Division, Pacific Northwest National Laboratory, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA, Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA and Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational & Statistical Analytics Division, Pacific Northwest National Laboratory, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA, Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA and Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational & Statistical Analytics Division, Pacific Northwest National Laboratory, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA, Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA and Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational & Statistical Analytics Division, Pacific Northwest National Laboratory, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA, Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA and Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States', 11, 'Genome|Genomics|Internet|Programming Languages|Proteins|Software Design', 'Journal Article', '10.1093/bioinformatics/btt454', '23956303', 'At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid.'),
(77845, 2013, 'bio', 'Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions', 'Shugay, Mikhail|Ortiz de Mendíbil, Iñigo|Vizmanos, José L|Novo, Francisco J', 'Department of Genetics, University of Navarra. 31008 Pamplona, Spain.|Department of Genetics, University of Navarra. 31008 Pamplona, Spain.|Department of Genetics, University of Navarra. 31008 Pamplona, Spain.|Department of Genetics, University of Navarra. 31008 Pamplona, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Bayes Theorem|Databases, Genetic|Gene Expression Profiling|Gene Fusion|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Oncogenes', 'Journal Article', '10.1093/bioinformatics/btt445', '23956304', 'Gene fusions resulting from chromosomal aberrations are an important cause of cancer. The complexity of genomic changes in certain cancer types has hampered the identification of gene fusions by molecular cytogenetic methods, especially in carcinomas. This is changing with the advent of next-generation sequencing, which is detecting a substantial number of new fusion transcripts in individual cancer genomes. However, this poses the challenge of identifying those fusions with greater oncogenic potential amid a background of passenger fusion sequences.'),
(77846, 2013, 'bio', 'A Turing test for artificial expression data', 'Maier, Robert|Zimmer, Ralf|Kxc3xbcffner, Robert', 'Department of Informatics, Ludwig-Maximilians Universitxc3xa4t, 80333 Mxc3xbcnchen, Germany.|Department of Informatics, Ludwig-Maximilians Universitxc3xa4t, 80333 Mxc3xbcnchen, Germany.|Department of Informatics, Ludwig-Maximilians Universitxc3xa4t, 80333 Mxc3xbcnchen, Germany.', 'Germany|Germany|Germany', 3, 'Cluster Analysis|Escherichia coli|Gene Expression|Gene Expression Profiling|Humans|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btt438', '23956305', 'The lack of reliable, comprehensive gold standards complicates the development of many bioinformatics tools, particularly for the analysis of expression data and biological networks. Simulation approaches can provide provisional gold standards, such as regulatory networks, for the assessment of network inference methods. However, this just defers the problem, as it is difficult to assess how closely simulators emulate the properties of real data.'),
(77847, 2013, 'bio', 'Incorporating prior knowledge into Gene Network Study', 'Wang, Zixing|Xu, Wenlong|San Lucas, F Anthony|Liu, Yin', 'Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Department of Epidemiology, University of Texas MD Anderson Center and University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.|Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Department of Epidemiology, University of Texas MD Anderson Center and University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.|Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Department of Epidemiology, University of Texas MD Anderson Center and University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.|Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Department of Epidemiology, University of Texas MD Anderson Center and University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.', 'United States|United States|United States|United States', 41, 'Bayes Theorem|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Models, Genetic|Ovarian Neoplasms|Software Design', 'Journal Article', '10.1093/bioinformatics/btt443', '23956306', 'A major goal in genomic research is to identify genes that may jointly influence a biological response. From many years of intensive biomedical research, a large body of biological knowledge, or pathway information, has accumulated in available databases. There is a strong interest in leveraging these pathways to improve the statistical power and interpretability in studying gene networks associated with complex phenotypes. This prior information is a valuable complement to large-scale genomic data such as gene expression data generated from microarrays. However, it is a non-trivial task to effectively integrate available biological knowledge into gene expression data when reconstructing gene networks.'),
(77848, 2013, 'bio', 'A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis', 'Reese, Sarah E|Archer, Kellie J|Therneau, Terry M|Atkinson, Elizabeth J|Vachon, Celine M|de Andrade, Mariza|Kocher, Jean-Pierre A|Eckel-Passow, Jeanette E', 'Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.|Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'DNA Copy Number Variations|Data Interpretation, Statistical|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Principal Component Analysis', 'Journal Article', '10.1093/bioinformatics/btt480', '23958724', 'Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data.'),
(77849, 2013, 'bio', 'PRAP: an ab initio software package for automated genome-wide analysis of DNA repeats for prokaryotes', 'Chen, Gwo-Liang|Chang, Yun-Juan|Hsueh, Chun-Hway', 'Department of Materials Science and Engineering, National Taiwan University, Taipei 10617, Taiwan and High-Performance Biological Computing, Roy J. Carver Biotechnology Center, The University of Illinois, Urbana, IL 61801, USA.|Department of Materials Science and Engineering, National Taiwan University, Taipei 10617, Taiwan and High-Performance Biological Computing, Roy J. Carver Biotechnology Center, The University of Illinois, Urbana, IL 61801, USA.|Department of Materials Science and Engineering, National Taiwan University, Taipei 10617, Taiwan and High-Performance Biological Computing, Roy J. Carver Biotechnology Center, The University of Illinois, Urbana, IL 61801, USA.', 'United States|United States|United States', 5, 'DNA, Archaeal|DNA, Bacterial|Databases, Nucleic Acid|Genome, Archaeal|Genome, Bacterial|Genomics|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt482', '23958725', 'Prokaryotic genome annotation has been focused mainly on identifying all genes and their protein functions. However, <30% of the prokaryotic genomes submitted to GenBank contain partial repeat features of specific types and none of the genomes contain complete repeat annotations. Deciphering all repeats in DNA sequences is an important and open task in genome annotation and bioinformatics. Hence, there is an immediate need of a tool capable of identifying full spectrum repeats in the whole genome.'),
(77850, 2013, 'bio', 'RDAVIDWebService: a versatile R interface to DAVID', 'Fresno, Cristóbal|Fernández, Elmer A', 'CONICET, Buenos Aires and BioScience Data Mining Group, Catholic University of Córdoba, Córdoba, Argentina.|CONICET, Buenos Aires and BioScience Data Mining Group, Catholic University of Córdoba, Córdoba, Argentina.', 'Argentina|Argentina', 0, 'Databases, Genetic|Gene Ontology|Humans|Internet|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btt487', '23958726', 'The RDAVIDWebService package provides a class-based interface from R programs/scripts to fully access/control the database for annotation, visualization and integrated discovery, without the need for human interaction on its Web site (http://david.abcc.ncifcrf.gov). The library enhances the database for annotation, visualization and integrated discovery capabilities for Gene Ontology analysis by means of GOstats-based direct acyclic graph conversion methods, in addition to the usual many-genes-to-many-terms visualization.'),
(77851, 2013, 'bio', 'Efficient inference of local ancestry', 'Yang, James J|Li, Jia|Buu, Anne|Williams, L K', 'Public Health Sciences, Henry Ford Health System, Detroit, Department of Psychiatry, University of Michigan, Ann Arbor and Center for Health Services Research, Henry Ford Health System, Detroit, MI, USA.|Public Health Sciences, Henry Ford Health System, Detroit, Department of Psychiatry, University of Michigan, Ann Arbor and Center for Health Services Research, Henry Ford Health System, Detroit, MI, USA.|Public Health Sciences, Henry Ford Health System, Detroit, Department of Psychiatry, University of Michigan, Ann Arbor and Center for Health Services Research, Henry Ford Health System, Detroit, MI, USA.|Public Health Sciences, Henry Ford Health System, Detroit, Department of Psychiatry, University of Michigan, Ann Arbor and Center for Health Services Research, Henry Ford Health System, Detroit, MI, USA.', 'United States|United States|United States|United States', 0, 'Data Interpretation, Statistical|Genetic Loci|Genetics, Population|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide|Regression Analysis', 'Evaluation Studies', '10.1093/bioinformatics/btt488', '23958727', 'The inference of local ancestry of admixed individuals at every locus provides the basis for admixture mapping. Local ancestry information has been used to identify genetic susceptibility loci.'),
(77852, 2013, 'bio', 'A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs', 'Abrishami, V|Zaldívar-Peraza, A|de la Rosa-Trevín, J M|Vargas, J|Otón, J|Marabini, R|Shkolnisky, Y|Carazo, J M|Sorzano, C O S', 'Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.|Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 40, 'Adenoviridae|Algorithms|Automation, Laboratory|DNA Helicases|Image Processing, Computer-Assisted|Macromolecular Substances|Microscopy, Electron|Particle Size', 'Journal Article', '10.1093/bioinformatics/btt429', '23958728', 'Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives.'),
(77853, 2013, 'bio', 'iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data', 'Imholte, Greg C|Scott-Boyer, Marie-Pier|Labbe, Aurélie|Deschepper, Christian F|Gottardo, Raphael', 'Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.', 'Canada|Canada|Canada|Canada|Canada', 8, 'Algorithms|Animals|Bayes Theorem|Gene Expression|Markov Chains|Mice|Monte Carlo Method|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btt485', '23958729', 'Recently, mapping studies of expression quantitative loci (eQTL) (where gene expression levels are viewed as quantitative traits) have provided insight into the biology of gene regulation. Bayesian methods provide natural modeling frameworks for analyzing eQTL studies, where information shared across markers and/or genes can increase the power to detect eQTLs. Bayesian approaches tend to be computationally demanding and require specialized software. As a result, most eQTL studies use univariate methods treating each gene independently, leading to suboptimal results.'),
(77854, 2013, 'bio', 'HippDB: a database of readily targeted helical protein-protein interactions', 'Bergey, Christina M|Watkins, Andrew M|Arora, Paramjit S', 'Department of Anthropology and Department of Chemistry, New York University, New York, NY 10003, USA.|Department of Anthropology and Department of Chemistry, New York University, New York, NY 10003, USA.|Department of Anthropology and Department of Chemistry, New York University, New York, NY 10003, USA.', 'United States|United States|United States', 0, 'Databases, Protein|Drug Discovery|Internet|Multiprotein Complexes|Protein Interaction Mapping|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/btt483', '23958730', 'HippDB catalogs every protein-protein interaction whose structure is available in the Protein Data Bank and which exhibits one or more helices at the interface. The Web site accepts queries on variables such as helix length and sequence, and it provides computational alanine scanning and change in solvent-accessible surface area values for every interfacial residue. HippDB is intended to serve as a starting point for structure-based small molecule and peptidomimetic drug development.'),
(77855, 2013, 'bio', 'A fast Peptide Match service for UniProt Knowledgebase', 'Chen, Chuming|Li, Zhiwen|Huang, Hongzhan|Suzek, Baris E|Wu, Cathy H|UniProt Consortium', 'Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.|Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 49, 'Databases, Protein|Internet|Knowledge Bases|Peptides|Proteomics|Search Engine|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt484', '23958731', 'We have developed a new web application for peptide matching using Apache Lucene-based search engine. The Peptide Match service is designed to quickly retrieve all occurrences of a given query peptide from UniProt Knowledgebase (UniProtKB) with isoforms. The matched proteins are shown in summary tables with rich annotations, including matched sequence region(s) and links to corresponding proteins in a number of proteomic/peptide spectral databases. The results are grouped by taxonomy and can be browsed by organism, taxonomic group or taxonomy tree. The service supports queries where isobaric leucine and isoleucine are treated equivalent, and an option for searching UniRef100 representative sequences, as well as dynamic queries to major proteomic databases. In addition to the web interface, we also provide RESTful web services. The underlying data are updated every 4 weeks in accordance with the UniProt releases.'),
(77856, 2013, 'bio', 'Determination of genomic copy number alteration emphasizing a restriction site-based strategy of genome re-sequencing', 'Zheng, Caihong|Miao, Xuexia|Li, Yanen|Huang, Ying|Ruan, Jue|Ma, Xi|Wang, Li|Wu, Chung-I|Cai, Jun', 'Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.|Laboratory of Disease Genomics and Personalized Medicine & Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China, University of Chinese Academy of Sciences, Beijing 100049, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Regeneron Pharmaceuticals, Inc., Tarrytown, NY 10591, USA, Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China and Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.', 'China|China|China|China|China|China|China|China|China', 6, 'Adult|Carcinoma, Hepatocellular|DNA Copy Number Variations|DNA Restriction Enzymes|Exome|Female|Gene Frequency|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Liver Neoplasms|Mutation|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt481', '23962614', 'Copy number abbreviation (CNA) is one type of genomic aberration that is often induced by genome instability and is associated with diseases such as cancer. Determination of the genome-wide CNA profile is an important step in identifying the underlying mutation mechanisms. Genomic data based on next-generation sequencing technology are particularly suitable for determination of high-quality CNA profile. Now is an important time to reevaluate the use of sequencing techniques for CNA analysis, especially with the rapid growth of the different targeted genome and whole-genome sequencing strategies.'),
(77857, 2013, 'bio', 'fmcsR: mismatch tolerant maximum common substructure searching in R', 'Wang, Yan|Backman, Tyler W H|Horan, Kevin|Girke, Thomas', 'Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Drug Discovery|Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btt475', '23962615', 'The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches.'),
(77858, 2013, 'bio', 'ProteoStats--a library for estimating false discovery rates in proteomics pipelines', 'Yadav, Amit Kumar|Kadimi, Puneet Kumar|Kumar, Dhirendra|Dash, Debasis', 'G.N.R. Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, New Delhi-110020, India.|G.N.R. Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, New Delhi-110020, India.|G.N.R. Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, New Delhi-110020, India.|G.N.R. Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, New Delhi-110020, India.', 'India|India|India|India', 0, 'Algorithms|Data Interpretation, Statistical|Peptides|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt490', '23962616', 'Statistical validation of peptide assignments from a large-scale shotgun proteomics experiment is a critical step, and various methods for evaluating significance based on decoy database search are in practice. False discovery rate (FDR) estimation of peptide assignments assesses global significance and corrects for multiple comparisons. Various approaches have been proposed for FDR estimation but unavailability of standard tools or libraries leads to development of many in-house scripts followed by manual steps that are error-prone and low-throughput. The ProteoStats library provides an open-source framework for developers with many FDR estimation and visualization features for several popular search algorithms. It also provides accurate q-values, which can be easily integrated in any proteomics pipeline to provide automated, accurate, high-throughput statistical validation and minimize manual errors.'),
(77859, 2013, 'bio', 'Accurate identification of polyadenylation sites from 3 end deep sequencing using a naive Bayes classifier', 'Sheppard, Sarah|Lawson, Nathan D|Zhu, Lihua Julie', 'Program in Gene Function and Expression and Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation St, Worcester, MA 01605, USA.|Program in Gene Function and Expression and Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation St, Worcester, MA 01605, USA.|Program in Gene Function and Expression and Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation St, Worcester, MA 01605, USA.', 'United States|United States|United States', 0, '3 Flanking Region|Algorithms|Animals|Base Sequence|Bayes Theorem|DNA|High-Throughput Nucleotide Sequencing|Humans|Polyadenylation', 'Journal Article', '10.1093/bioinformatics/btt446', '23962617', '3 end processing is important for transcription termination, mRNA stability and regulation of gene expression. To identify 3 ends, most techniques use an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Although heuristic filters have been applied in these cases, they typically result in a high proportion of both false-positive and -negative classifications. Therefore, there is a need to develop improved algorithms to better identify mis-priming events in oligo-dT primed sequences.');
