INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78040, 2014, 'bio', 'PhosphoNetworks: a database for human phosphorylation networks', 'Hu, Jianfei|Rho, Hee-Sool|Newman, Robert H|Zhang, Jin|Zhu, Heng|Qian, Jiang', 'Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Department of Pharmacology and Molecular Sciences, Center for High-Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA, Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.', 'United States|United States|United States|United States|United States|United States', 57, 'Databases, Protein|Humans|Phosphorylation|Phosphotransferases|Protein Array Analysis|Proteins|Signal Transduction|Substrate Specificity|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt627', '24227675', 'Phosphorylation plays an important role in cellular signal transduction. Current phosphorylation-related databases often focus on the phosphorylation sites, which are mainly determined by mass spectrometry. Here, we present PhosphoNetworks, a phosphorylation database built on a high-resolution map of phosphorylation networks. This high-resolution map of phosphorylation networks provides not only the kinase-substrate relationships (KSRs), but also the specific phosphorylation sites on which the kinases act on the substrates. The database contains the most comprehensive dataset for KSRs, including the relationships from a recent high-throughput project for identification of KSRs using protein microarrays, as well as known KSRs curated from the literature. In addition, the database also includes several analytical tools for dissecting phosphorylation networks. PhosphoNetworks is expected to play a prominent role in proteomics and phosphorylation-related disease research.'),
(78041, 2014, 'bio', 'Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser', 'Raney, Brian J|Dreszer, Timothy R|Barber, Galt P|Clawson, Hiram|Fujita, Pauline A|Wang, Ting|Nguyen, Ngan|Paten, Benedict|Zweig, Ann S|Karolchik, Donna|Kent, W James', 'Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA and Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 162, 'Databases, Genetic|Genome|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt637', '24227676', 'Track data hubs provide an efficient mechanism for visualizing remotely hosted Internet-accessible collections of genome annotations. Hub datasets can be organized, configured and fully integrated into the University of California Santa Cruz (UCSC) Genome Browser and accessed through the familiar browser interface. For the first time, individuals can use the complete browser feature set to view custom datasets without the overhead of setting up and maintaining a mirror.'),
(78042, 2014, 'bio', 'featureCounts: an efficient general purpose program for assigning sequence reads to genomic features', 'Liao, Yang|Smyth, Gordon K|Shi, Wei', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Department of Computing and Information Systems and Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Department of Computing and Information Systems and Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Department of Computing and Information Systems and Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Genome|Genomics|High-Throughput Nucleotide Sequencing|Histones|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt656', '24227677', 'Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature.'),
(78043, 2014, 'bio', 'CheNER: chemical named entity recognizer', 'Usié, Anabel|Alves, Rui|Solsona, Francesc|Vázquez, Miguel|Valencia, Alfonso', 'Department of Basic Medical Science (CMB), University of Lleida & IRBLleida, Department of Computers an Industrial Engineering (DIEI), University of Lleida, Lleida and Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|Department of Basic Medical Science (CMB), University of Lleida & IRBLleida, Department of Computers an Industrial Engineering (DIEI), University of Lleida, Lleida and Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|Department of Basic Medical Science (CMB), University of Lleida & IRBLleida, Department of Computers an Industrial Engineering (DIEI), University of Lleida, Lleida and Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|Department of Basic Medical Science (CMB), University of Lleida & IRBLleida, Department of Computers an Industrial Engineering (DIEI), University of Lleida, Lleida and Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|Department of Basic Medical Science (CMB), University of Lleida & IRBLleida, Department of Computers an Industrial Engineering (DIEI), University of Lleida, Lleida and Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain', 10, 'Databases, Chemical|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btt639', '24227678', 'Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemical compounds, which due to the complex morphology of IUPAC names requires more advanced techniques than that of brand names.'),
(78044, 2014, 'bio', 'ClockstaR: choosing the number of relaxed-clock models in molecular phylogenetic analysis', 'Duchêne, Sebastián|Molak, Martyna|Ho, Simon Y W', 'School of Biological Sciences, University of Sydney, Sydney, NSW 2006, Australia.|School of Biological Sciences, University of Sydney, Sydney, NSW 2006, Australia.|School of Biological Sciences, University of Sydney, Sydney, NSW 2006, Australia.', 'Australia|Australia|Australia', 38, 'Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Models, Genetic|Phylogeny|Software Design', 'Journal Article', '10.1093/bioinformatics/btt665', '24234002', 'Relaxed molecular clocks allow the phylogenetic estimation of evolutionary timescales even when substitution rates vary among branches. In analyses of large multigene datasets, it is often appropriate to use multiple relaxed-clock models to accommodate differing patterns of rate variation among genes. We present ClockstaR, a method for selecting the number of relaxed clocks for multigene datasets.'),
(78045, 2014, 'bio', 'footprintDB: a database of transcription factors with annotated cis elements and binding interfaces', 'Sebastian, Alvaro|Contreras-Moreira, Bruno', 'Laboratory of Computational Biology, Department of Genetics and Plant Production, Estación Experimental de Aula Dei/CSIC, Av. Montañana 1005, Zaragoza (http://www.eead.csic.es/compbio) and Fundación ARAID, Paseo María Agustín 36, Zaragoza, Spain.|Laboratory of Computational Biology, Department of Genetics and Plant Production, Estación Experimental de Aula Dei/CSIC, Av. Montañana 1005, Zaragoza (http://www.eead.csic.es/compbio) and Fundación ARAID, Paseo María Agustín 36, Zaragoza, Spain.', 'Spain|Spain', 31, 'Animals|Arabidopsis|Bacillus subtilis|Binding Sites|Databases, Genetic|Drosophila melanogaster|Escherichia coli K12|Gene Expression Regulation|Humans|Molecular Sequence Annotation|Nucleotide Motifs|Protein Binding|Proteome|Regulatory Sequences, Nucleic Acid|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt663', '24234003', 'Traditional and high-throughput techniques for determining transcription factor (TF) binding specificities are generating large volumes of data of uneven quality, which are scattered across individual databases.'),
(78046, 2013, 'bio', 'ISCB: past-present perspective for the International Society for Computational Biology', 'Rost, Burkhard', 'TUM, Department of Informatics, Bioinformatics and Computational Biology - i12, TU Munich, Boltzmannstr. 3, 85748 Garching/Munich, Germany and International Society for Computational Biology, 9500 Gilman Drive, MC 0505 UCSD/SDSC, La Jolla, CA 92093-0505, USA.', 'Germany', 0, 'Computational Biology|Genomics|International Agencies|Proteome|Societies, Scientific|Software', 'Journal Article', '10.1093/bioinformatics/btt629', '24234004', 'Since its establishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly toward advancing the understanding of living systems through computation. The ISCB represents nearly 3000 members working in >70 countries. It has doubled the number of members since 2007. At the same time, the number of meetings organized by the ISCB has increased from two in 2007 to eight in 2013, and the society has cemented many lasting alliances with regional societies and specialist groups. ISCB is ready to grow into a challenging and promising future. The progress over the past 7 years has resulted from the vision, and possibly more importantly, the passion and hard working dedication of many individuals. '),
(78047, 2014, 'bio', 'SeqDepot: streamlined database of biological sequences and precomputed features', 'Ulrich, Luke E|Zhulin, Igor B', 'Agile Genomics, LLC, Mount Pleasant, SC 29466, USA, Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA and Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Agile Genomics, LLC, Mount Pleasant, SC 29466, USA, Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA and Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States', 7, 'Computational Biology|Databases, Genetic|Humans|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt658', '24234005', 'Assembling and/or producing integrated knowledge of sequence features continues to be an onerous and redundant task despite a large number of existing resources. We have developed SeqDepot-a novel database that focuses solely on two primary goals: (i) assimilating known primary sequences with predicted feature data and (ii) providing the most simple and straightforward means to procure and readily use this information. Access to >28.5 million sequences and 300 million features is provided through a well-documented and flexible RESTful interface that supports fetching specific data subsets, bulk queries, visualization and searching by MD5 digests or external database identifiers. We have also developed an HTML5/JavaScript web application exemplifying how to interact with SeqDepot and Perl/Python scripts for use with local processing pipelines.'),
(78048, 2014, 'bio', 'ASSIST: a fast versatile local structural comparison tool', 'Caprari, Silvia|Toti, Daniele|Viet Hung, Le|Di Stefano, Maurizio|Polticelli, Fabio', 'Department of Sciences, University of Roma Tre, 00146 Rome, Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy, Artificial Solutions, 08026 Barcelona, Spain and National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy, Artificial Solutions, 08026 Barcelona, Spain and National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy, Artificial Solutions, 08026 Barcelona, Spain and National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy, Artificial Solutions, 08026 Barcelona, Spain and National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy, Artificial Solutions, 08026 Barcelona, Spain and National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.', 'Spain|Spain|Spain|Spain|Spain', 9, 'Algorithms|Imaging, Three-Dimensional|Protein Conformation|Proteins|Software|Time Factors', 'Comparative Study', '10.1093/bioinformatics/btt664', '24243934', 'Structural genomics initiatives are increasingly leading to the determination of the 3D structure of target proteins whose catalytic function is not known. The aim of this work was that of developing a novel versatile tool for searching structural similarity, which allows to predict the catalytic function, if any, of these proteins.'),
(78049, 2014, 'bio', 'Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data', 'Waszak, Sebastian M|Kilpinen, Helena|Gschwind, Andreas R|Orioli, Andrea|Raghav, Sunil K|Witwicki, Robert M|Migliavacca, Eugenia|Yurovsky, Alisa|Lappalainen, Tuuli|Hernandez, Nouria|Reymond, Alexandre|Dermitzakis, Emmanouil T|Deplancke, Bart', 'Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.|Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland, Department of Genetic Medicine and Development, University of Geneva Medical School, Institute of Genetics and Genomics in Geneva, University of Geneva, 1211, Geneva, Switzerland and Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1011, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 5, 'Alleles|Binding Sites|Chromatin Immunoprecipitation|Female|Gene Library|Genome, Human|Humans|Lymphocytes|Male|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|RNA Polymerase II', 'Journal Article', '10.1093/bioinformatics/btt667', '24255646', 'High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts.'),
(78050, 2014, 'bio', 'Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes', 'De Santis, Marianna|Rinaldi, Francesco|Falcone, Emmanuela|Lucidi, Stefano|Piaggio, Giulia|Gurtner, Aymone|Farina, Lorenzo', 'Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy and Istituto Nazionale dei Tumori Regina Elena, Dipartimento di Oncologia Sperimentale, Laboratorio di Oncogenesi Molecolare, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 4, 'Algorithms|Artificial Intelligence|Cell Cycle|Cells, Cultured|Fibroblasts|Flow Cytometry|Gene Expression Profiling|Genes, cdc|Genome, Human|Humans|RNA, Messenger|Real-Time Polymerase Chain Reaction|Reverse Transcriptase Polymerase Chain Reaction|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt671', '24255647', 'The identification of cell cycle-regulated genes through the cyclicity of messenger RNAs in genome-wide studies is a difficult task due to the presence of internal and external noise in microarray data. Moreover, the analysis is also complicated by the loss of synchrony occurring in cell cycle experiments, which often results in additional background noise.'),
(78051, 2014, 'bio', 'FastPros: screening of reaction knockout strategies for metabolic engineering', 'Ohno, Satoshi|Shimizu, Hiroshi|Furusawa, Chikara', 'Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871 and Quantitative Biology Center (QBiC), RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871 and Quantitative Biology Center (QBiC), RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871 and Quantitative Biology Center (QBiC), RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan.', 'Japan|Japan|Japan', 23, 'Algorithms|Cluster Analysis|Escherichia coli|Genome, Bacterial|Metabolic Engineering|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btt672', '24257186', 'Although constraint-based flux analysis of knockout strains has facilitated the production of desirable metabolites in microbes, current screening methods have placed a limitation on the number knockouts that can be simultaneously analyzed.'),
(78052, 2014, 'bio', 'Effect of separate sampling on classification accuracy', 'Shahrokh Esfahani, Mohammad|Dougherty, Edward R', 'Department of Electrical and Computer Engineering and Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering and Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 39, 'Algorithms|Bayes Theorem|Breast Neoplasms|Child|Discriminant Analysis|Female|Gene Expression Profiling|Humans|Leukemia, Myeloid, Acute|Multiple Myeloma|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Sample Size|Selection Bias', 'Journal Article', '10.1093/bioinformatics/btt662', '24257187', 'Measurements are commonly taken from two phenotypes to build a classifier, where the number of data points from each class is predetermined, not random. In this separate sampling scenario, the data cannot be used to estimate the class prior probabilities. Moreover, predetermined class sizes can severely degrade classifier performance, even for large samples.'),
(78053, 2014, 'bio', 'A divergent calponin homology (NN-CH) domain defines a novel family: implications for evolution of ciliary IFT complex B proteins', 'Schou, Kenneth B|Andersen, Jens S|Pedersen, Lotte B', 'Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark and Department of Biology, University of Copenhagen, Universitetsparken 13, DK-2100 Copenhagen OE, Denmark.|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark and Department of Biology, University of Copenhagen, Universitetsparken 13, DK-2100 Copenhagen OE, Denmark.|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark and Department of Biology, University of Copenhagen, Universitetsparken 13, DK-2100 Copenhagen OE, Denmark.', 'Denmark|Denmark|Denmark', 25, 'Amino Acid Sequence|Animals|Calcium-Binding Proteins|Evolution, Molecular|Humans|Kinetochores|Microfilament Proteins|Molecular Sequence Data|Nuclear Proteins|Protein Binding|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt661', '24257188', 'Microtubules are dynamic polymers of tubulin dimers that undergo continuous assembly and disassembly. A mounting number of microtubule-associated proteins (MAPs) regulate the dynamic behavior of microtubules and hence the assembly and disassembly of disparate microtubule structures within the cell. Despite recent advances in identification and functional characterization of MAPs, a substantial number of microtubule accessory factors have not been functionally annotated. Here, using profile-to-profile comparisons and structure modeling, we show that the yeast outer kinetochore components NDC80 and NUF2 share evolutionary ancestry with a novel protein family in mammals comprising, besides NDC80/HEC1 and NUF2, three Intraflagellar Transport (IFT) complex B subunits (IFT81, IFT57, CLUAP1) as well as six proteins with poorly defined function (FAM98A-C, CCDC22, CCDC93 and C14orf166). We show that these proteins consist of a divergent N-terminal calponin homology (CH)-like domain adjoined to an array of C-terminal heptad repeats predicted to form a coiled-coil arrangement. We have named the divergent CH-like domain NN-CH after the founding members NDC80 and NUF2.'),
(78054, 2014, 'bio', 'A novel splicing outcome reveals more than 2000 new mammalian protein isoforms', 'Wilson, Laurence O W|Spriggs, Andrew|Taylor, Jennifer M|Fahrer, Aude M', 'Research School of Biology, Australian National University, Canberra, ACT 0200 and CSIRO Plant Industry, Black Mountain Laboratories, Canberra, ACT 2601, Australia.|Research School of Biology, Australian National University, Canberra, ACT 0200 and CSIRO Plant Industry, Black Mountain Laboratories, Canberra, ACT 2601, Australia.|Research School of Biology, Australian National University, Canberra, ACT 0200 and CSIRO Plant Industry, Black Mountain Laboratories, Canberra, ACT 2601, Australia.|Research School of Biology, Australian National University, Canberra, ACT 0200 and CSIRO Plant Industry, Black Mountain Laboratories, Canberra, ACT 2601, Australia.', 'Australia|Australia|Australia|Australia', 8, 'Alternative Splicing|Amino Acid Sequence|Animals|Base Sequence|Cell Cycle Proteins|Computational Biology|Gene Expression Regulation|Genome|Humans|Mice|Molecular Sequence Data|Nuclear Proteins|Protein Isoforms|Proteins|RNA Splice Sites', 'Comparative Study', '10.1093/bioinformatics/btt668', '24262213', 'We have recently characterized an instance of alternative splicing that differs from the canonical gene transcript by deletion of a length of sequence not divisible by three, but where translation can be rescued by an alternative start codon. This results in a predicted protein in which the amino terminus differs markedly in sequence from the known protein product(s), as it is translated from an alternative reading frame. Automated pipelines have annotated thousands of splice variants but have overlooked these protein isoforms, leading to them being underrepresented in current databases.'),
(78055, 2014, 'bio', 'myChEMBL: a virtual machine implementation of open data and cheminformatics tools', 'Ochoa, Rodrigo|Davies, Mark|Papadatos, George|Atkinson, Francis|Overington, John P', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Computational Biology|Databases, Chemical|Drug Discovery|Internet|Small Molecule Libraries|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btt666', '24262214', 'myChEMBL is a completely open platform, which combines public domain bioactivity data with open source database and cheminformatics technologies. myChEMBL consists of a Linux (Ubuntu) Virtual Machine featuring a PostgreSQL schema with the latest version of the ChEMBL database, as well as the latest RDKit cheminformatics libraries. In addition, a self-contained web interface is available, which can be modified and improved according to user specifications.'),
(78056, 2014, 'bio', 'On the simultaneous association analysis of large genomic regions: a massive multi-locus association test', 'Qiao, Dandi|Cho, Michael H|Fier, Heide|Bakke, Per S|Gulsvik, Amund|Silverman, Edwin K|Lange, Christoph', 'Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 20115, USA, Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA, Department of Genomic Mathematics, University of Bonn, 53113 Bonn, Germany and Department of Thoracic Medicine, Haukeland University Hospital and Section for Respiratory Medicine Institute of Medicine, University of Bergen, 5006 Bergen, Norway.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Case-Control Studies|Chromosomes, Human|Cluster Analysis|Computer Simulation|Genetic Loci|Genetic Markers|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Genomics|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Pulmonary Disease, Chronic Obstructive', 'Comparative Study', '10.1093/bioinformatics/btt654', '24262215', 'For samples of unrelated individuals, we propose a general analysis framework in which hundred thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multilocus analysis, which has focused on the dimension reduction of the data, our multilocus association-clustering test profits from the availability of large numbers of genetic loci by detecting clusters of loci that are associated with the phenotype.'),
(78057, 2014, 'bio', 'Identification of transcription factors for drug-associated gene modules and biomedical implications', 'Xiong, Min|Li, Bin|Zhu, Qiang|Wang, Yun-Xing|Zhang, Hong-Yu', 'National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China.', 'China|China|China|China|China', 0, 'Cell Line|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Lipid Metabolism|NF-E2 Transcription Factor|Peroxisome Proliferator-Activated Receptors|Polyphenols|Protein Interaction Mapping|Receptors, Estrogen|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btt683', '24262216', 'One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown.'),
(78058, 2014, 'bio', 'MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program', 'Ghahremanpour, Mohammad Mehdi|Arab, Seyed Shahriar|Aghazadeh, Saman Biook|Zhang, Jin|van der Spoel, David', 'Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran, School of Mathematics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Computer Science, Faculty of Engineering, University of Tehran, Tehran, Iran, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden and Department of Chemistry, Zhejiang University, Hangzhou 310027, China.|Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran, School of Mathematics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Computer Science, Faculty of Engineering, University of Tehran, Tehran, Iran, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden and Department of Chemistry, Zhejiang University, Hangzhou 310027, China.|Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran, School of Mathematics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Computer Science, Faculty of Engineering, University of Tehran, Tehran, Iran, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden and Department of Chemistry, Zhejiang University, Hangzhou 310027, China.|Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran, School of Mathematics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Computer Science, Faculty of Engineering, University of Tehran, Tehran, Iran, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden and Department of Chemistry, Zhejiang University, Hangzhou 310027, China.|Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran, School of Mathematics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, Department of Computer Science, Faculty of Engineering, University of Tehran, Tehran, Iran, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden and Department of Chemistry, Zhejiang University, Hangzhou 310027, China.', 'China|China|China|China|China', 34, 'Cell Membrane|Computer Graphics|Internet|Lipid Bilayers|Models, Biological|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btt680', '24273238', 'Molecular dynamics (MD) simulations have had a profound impact on studies of membrane proteins during past two decades, but the accuracy of MD simulations of membranes is limited by the quality of membrane models and the applied force fields. Membrane models used in MD simulations mostly contain one kind of lipid molecule. This is far from reality, for biological membranes always contain more than one kind of lipid molecule. Moreover, the lipid composition and their distribution are functionally important. As a result, there is a necessity to prepare more realistic lipid membranes containing different types of lipids at physiological concentrations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78059, 2014, 'bio', 'Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins', 'May, Ali|Pool, René|van Dijk, Erik|Bijlard, Jochem|Abeln, Sanne|Heringa, Jaap|Feenstra, K Anton', 'Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Molecular Dynamics Simulation|Multiprotein Complexes|Mutation|Protein Interaction Mapping|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btt675', '24273239', 'To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure.'),
(78060, 2014, 'bio', 'In silico site of metabolism prediction for human UGT-catalyzed reactions', 'Peng, Jianlong|Lu, Jing|Shen, Qiancheng|Zheng, Mingyue|Luo, Xiaomin|Zhu, Weiliang|Jiang, Hualiang|Chen, Kaixian', 'Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.', 'China|China|China|China|China|China|China|China', 0, 'Biocatalysis|Computer Simulation|Glucuronosyltransferase|Humans|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt681', '24273240', 'The human uridine diphosphate-glucuronosyltransferase enzyme family catalyzes the glucuronidation of the glycosyl group of a nucleotide sugar to an acceptor compound (substrate), which is the most common conjugation pathway that serves to protect the organism from the potential toxicity of xenobiotics. Moreover, it could affect the pharmacological profile of a drug. Therefore, it is important to identify the metabolically labile sites for glucuronidation.'),
(78061, 2014, 'bio', 'Pathway Commons at virtual cell: use of pathway data for mathematical modeling', 'Blinov, Michael L|Schaff, James C|Ruebenacker, Oliver|Wei, Xintao|Vasilescu, Dan|Gao, Fei|Morgan, Frank|Ye, Li|Lakshminarayana, Anuradha|Moraru, Ion I|Loew, Leslie M', 'Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Cell Physiological Phenomena|Computational Biology|Databases, Factual|Gene Regulatory Networks|Information Storage and Retrieval|Models, Theoretical|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btt660', '24273241', 'Pathway Commons is a resource permitting simultaneous queries of multiple pathway databases. However, there is no standard mechanism for using these data (stored in BioPAX format) to annotate and build quantitative mathematical models. Therefore, we developed a new module within the virtual cell modeling and simulation software. It provides pathway data retrieval and visualization and enables automatic creation of executable network models directly from qualitative connections between pathway nodes.'),
(78062, 2014, 'bio', 'jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework', 'Lxc3xb3pez-Camacho, Esteban|Garcxc3xada Godoy, Marxc3xada Jesxc3xbas|Nebro, Antonio J|Aldana-Montes, Josxc3xa9 F', 'Lenguajes y Ciencias de la Computacixc3xb3n, Universidad de Mxc3xa1laga, Bulevar Louis Pasteur 35, 29071, Mxc3xa1laga, Spain.|Lenguajes y Ciencias de la Computacixc3xb3n, Universidad de Mxc3xa1laga, Bulevar Louis Pasteur 35, 29071, Mxc3xa1laga, Spain.|Lenguajes y Ciencias de la Computacixc3xb3n, Universidad de Mxc3xa1laga, Bulevar Louis Pasteur 35, 29071, Mxc3xa1laga, Spain.|Lenguajes y Ciencias de la Computacixc3xb3n, Universidad de Mxc3xa1laga, Bulevar Louis Pasteur 35, 29071, Mxc3xa1laga, Spain.', 'Spain|Spain|Spain|Spain', 18, 'Algorithms|Drug Design|Humans|Ligands|Molecular Docking Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt679', '24273242', 'Molecular docking is a method for structure-based drug design and structural molecular biology, which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is AutoDock, which incorporates three metaheuristic techniques. We propose the integration of AutoDock with jMetalCpp, an optimization framework, thereby providing both single- and multi-objective algorithms that can be used to effectively solve docking problems.'),
(78063, 2014, 'bio', 'Protein-driven inference of miRNA-disease associations', 'Mørk, Søren|Pletscher-Frankild, Sune|Palleja Caro, Albert|Gorodkin, Jan|Jensen, Lars Juhl', 'Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.|Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.|Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.|Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.|Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 105, 'Diabetes Mellitus|Disease|Humans|MicroRNAs|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt677', '24273243', 'MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer.'),
(78064, 2014, 'bio', 'Refining carbon flux paths using atomic trace data', 'Pey, Jon|Planes, Francisco J|Beasley, John E', 'CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK.|CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK.|CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK.', 'Spain|Spain|Spain', 0, 'Carbon|Carbon Cycle|Escherichia coli|Genome, Bacterial|Metabolic Networks and Pathways|Pyruvate Kinase', 'Journal Article', '10.1093/bioinformatics/btt653', '24273244', 'Pathway analysis tools are a powerful strategy to analyze omics data in the field of systems biology. From a metabolic perspective, several pathway definitions can be found in the literature, each one appropriate for a particular study. Recently, a novel pathway concept termed carbon flux paths (CFPs) was introduced and benchmarked against existing approaches, showing a clear advantage for finding linear pathways from a given source to target metabolite. CFPs are simple paths in a metabolite-metabolite graph that satisfy typical constraints in stoichiometric models: mass balancing and thermodynamics (irreversibility). In addition, CFPs guarantee carbon exchange in each of their intermediate steps, but not between the source and the target metabolites and consequently false positive solutions may arise. These pathways often lack biological interest, particularly when studying biosynthetic or degradation routes of a metabolite. To overcome this issue, we amend the formulation in CFP, so as to account for atomic fate information. This approach is termed atomic CFP (aCFP).'),
(78065, 2014, 'bio', 'A classification approach for DNA methylation profiling with bisulfite next-generation sequencing data', 'Cheng, Longjie|Zhu, Yu', 'Department of Statistics, Purdue University, West Lafayette, IN 47906, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47906, USA.', 'United States|United States', 0, 'Algorithms|Biometry|Computer Simulation|DNA Methylation|Embryonic Stem Cells|False Positive Reactions|Gene Expression Profiling|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sulfites', 'Journal Article', '10.1093/bioinformatics/btt674', '24273245', 'With the advent of high-throughput sequencing technology, bisulfite-sequencing-based DNA methylation profiling methods have emerged as the most promising approaches due to their single-base resolution and genome-wide coverage. However, statistical analysis methods for analyzing this type of methylation data are not well developed. Although the most widely used proportion-based estimation method is simple and intuitive, it is not statistically adequate in dealing with the various sources of noise in bisulfite-sequencing data. Furthermore, it is not biologically satisfactory in applications that require binary methylation status calls.'),
(78066, 2014, 'bio', 'Sequence alignment visualization in HTML5 without Java', 'Gille, Christoph|Birgit, Weyand|Gille, Andreas', 'Universitätsmedizin Berlin, Charité, Institut für Biochemie, 10117 Berlin, Germany, Department of Plastic, Hannover Medical School, Hand and Reconstructive Surgery, 30625 Hannover, Germany and Universität Heidelberg, Institut für Experimentelle und Klinische Pharmakologie und Toxikologie, 68169 Mannheim, Germany.|Universitätsmedizin Berlin, Charité, Institut für Biochemie, 10117 Berlin, Germany, Department of Plastic, Hannover Medical School, Hand and Reconstructive Surgery, 30625 Hannover, Germany and Universität Heidelberg, Institut für Experimentelle und Klinische Pharmakologie und Toxikologie, 68169 Mannheim, Germany.|Universitätsmedizin Berlin, Charité, Institut für Biochemie, 10117 Berlin, Germany, Department of Plastic, Hannover Medical School, Hand and Reconstructive Surgery, 30625 Hannover, Germany and Universität Heidelberg, Institut für Experimentelle und Klinische Pharmakologie und Toxikologie, 68169 Mannheim, Germany.', 'Germany|Germany|Germany', 0, 'Amino Acid Sequence|Internet|Models, Molecular|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt614', '24273246', 'Java has been extensively used for the visualization of biological data in the web. However, the Java runtime environment is an additional layer of software with an own set of technical problems and security risks. HTML in its new version 5 provides features that for some tasks may render Java unnecessary.'),
(78067, 2014, 'bio', 'DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data', 'Kovacheva, Violeta N|Khan, Adnan M|Khan, Michael|Epstein, David B A|Rajpoot, Nasir M', 'Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar.|Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar.|Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar.|Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar.|Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar.', 'Qatar|Qatar|Qatar|Qatar|Qatar', 0, 'Algorithms|Colonic Neoplasms|Humans|Image Processing, Computer-Assisted|Phenotype|Protein Interaction Mapping|Proteomics', 'Journal Article', '10.1093/bioinformatics/btt676', '24273247', 'New bioimaging techniques have recently been proposed to visualize the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyze the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level.'),
(78068, 2014, 'bio', 'Revisiting amino acid substitution matrices for identifying distantly related proteins', 'Yamada, Kazunori|Tomii, Kentaro', 'Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.', 'Japan|Japan', 19, 'Amino Acid Substitution|Principal Component Analysis|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btt694', '24281694', 'Although many amino acid substitution matrices have been developed, it has not been well understood which is the best for similarity searches, especially for remote homology detection. Therefore, we collected information related to existing matrices, condensed it and derived a novel matrix that can detect more remote homology than ever.'),
(78069, 2014, 'bio', 'Flexible analysis of RNA-seq data using mixed effects models', 'Turro, Ernest|Astle, William J|Tavaré, Simon', 'Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK, Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge CB2 0PT, UK and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal QC H3A 1A2, Canada.|Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK, Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge CB2 0PT, UK and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal QC H3A 1A2, Canada.|Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK, Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge CB2 0PT, UK and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal QC H3A 1A2, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Animals|Bayes Theorem|Gene Expression Profiling|Gene Expression Regulation|Genomic Imprinting|High-Throughput Nucleotide Sequencing|Mice|Mice, Inbred C57BL|Models, Statistical|Protein Isoforms', 'Journal Article', '10.1093/bioinformatics/btt624', '24281695', 'Most methods for estimating differential expression from RNA-seq are based on statistics that compare normalized read counts between treatment classes. Unfortunately, reads are in general too short to be mapped unambiguously to features of interest, such as genes, isoforms or haplotype-specific isoforms. There are methods for estimating expression levels that account for this source of ambiguity. However, the uncertainty is not generally accounted for in downstream analysis of gene expression experiments. Moreover, at the individual transcript level, it can sometimes be too large to allow useful comparisons between treatment groups.'),
(78070, 2014, 'bio', 'mCSM: predicting the effects of mutations in proteins using graph-based signatures', 'Pires, Douglas E V|Ascher, David B|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK and ACRF Rational Drug Discovery Centre and Biota Structural Biology Laboratory, St Vincents Institute of Medical Research, Fitzroy, VIC, 3065, Australia.|Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK and ACRF Rational Drug Discovery Centre and Biota Structural Biology Laboratory, St Vincents Institute of Medical Research, Fitzroy, VIC, 3065, Australia.|Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK and ACRF Rational Drug Discovery Centre and Biota Structural Biology Laboratory, St Vincents Institute of Medical Research, Fitzroy, VIC, 3065, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Amino Acids|Computer Graphics|DNA|DNA-Binding Proteins|Multiprotein Complexes|Mutation, Missense|Protein Stability|Proteins|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btt691', '24281696', 'Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein-protein and protein-nucleic acid interactions.'),
(78071, 2014, 'bio', 'DDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment', 'Yalamanchili, Hari Krishna|Yan, Bin|Li, Mulin Jun|Qin, Jing|Zhao, Zhongying|Chin, Francis Y L|Wang, Junwen', 'Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Department of Biology, Hong Kong Baptist University, Kowloon, Department of Computer Science, Faculty of Engineering and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Animals|Caenorhabditis elegans|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Transcription Factors|Yeasts', 'Journal Article', '10.1093/bioinformatics/btt692', '24285602', 'Inferring gene-regulatory networks is very crucial in decoding various complex mechanisms in biological systems. Synthesis of a fully functional transcriptional factor/protein from DNA involves series of reactions, leading to a delay in gene regulation. The complexity increases with the dynamic delay induced by other small molecules involved in gene regulation, and noisy cellular environment. The dynamic delay in gene regulation is quite evident in high-temporal live cell lineage-imaging data. Although a number of gene-network-inference methods are proposed, most of them ignore the associated dynamic time delay.'),
(78072, 2014, 'bio', 'RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments', 'Soldatov, Ruslan A|Vinogradova, Svetlana V|Mironov, Andrey A', 'Institute for Information Transmission Problems (the Kharkevich Institute), Russian Academy of Sciences, 19 Bolshoy Karetny per., Moscow 127994 and Department of Bioengineering and Bioinformatics, Moscow State University, 1-73 Vorobyevy Gory, Moscow 119991, Russia.|Institute for Information Transmission Problems (the Kharkevich Institute), Russian Academy of Sciences, 19 Bolshoy Karetny per., Moscow 127994 and Department of Bioengineering and Bioinformatics, Moscow State University, 1-73 Vorobyevy Gory, Moscow 119991, Russia.|Institute for Information Transmission Problems (the Kharkevich Institute), Russian Academy of Sciences, 19 Bolshoy Karetny per., Moscow 127994 and Department of Bioengineering and Bioinformatics, Moscow State University, 1-73 Vorobyevy Gory, Moscow 119991, Russia.', '-|-|-', 10, 'Algorithms|Bacillus subtilis|Computer Simulation|Genome, Bacterial|Genomics|Nucleic Acid Conformation|RNA, Untranslated|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt701', '24292360', 'During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts.'),
(78073, 2014, 'bio', 'ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect', 'Di Camillo, Barbara|Sambo, Francesco|Toffolo, Gianna|Cobelli, Claudio', 'Department of Information Engineering, University of Padova, via Gradenigo 6B, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6B, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6B, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6B, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Data Interpretation, Statistical|Gene Frequency|Genome-Wide Association Study|Genotype|Genotyping Techniques|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btt697', '24292361', 'In the past years, both sequencing and microarray have been widely used to search for relations between genetic variations and predisposition to complex pathologies such as diabetes or neurological disorders. These studies, however, have been able to explain only a small fraction of disease heritability, possibly because complex pathologies cannot be referred to few dysfunctional genes, but are rather heterogeneous and multicausal, as a result of a combination of rare and common variants possibly impairing multiple regulatory pathways. Rare variants, though, are difficult to detect, especially when the effects of causal variants are in different directions, i.e. with protective and detrimental effects.'),
(78074, 2014, 'bio', 'Finding consistent disease subnetworks using PFSNet', 'Lim, Kevin|Wong, Limsoon', 'School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417.|School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417.', 'Singapore|Singapore', 32, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genome, Human|Humans|Leukemia|Models, Statistical|Muscular Dystrophy, Duchenne|Oligonucleotide Array Sequence Analysis|Phenotype|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btt625', '24292362', 'Microarray data analysis is often applied to characterize disease populations by identifying individual genes linked to the disease. In recent years, efforts have shifted to focus on sets of genes known to perform related biological functions (i.e. in the same pathways). Evaluating gene sets reduces the need to correct for false positives in multiple hypothesis testing. However, pathways are often large, and genes in the same pathway that do not contribute to the disease can cause a method to miss the pathway. In addition, large pathways may not give much insight to the cause of the disease. Moreover, when such a method is applied independently to two datasets of the same disease phenotypes, the two resulting lists of significant pathways often have low agreement.'),
(78075, 2014, 'bio', 'Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets', 'Rahmatallah, Yasir|Emmert-Streib, Frank|Glazko, Galina', 'Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA and Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK.|Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA and Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK.|Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA and Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Cell Line, Tumor|Gene Expression Profiling|Gene Regulatory Networks|Genes, p53|Humans|Multivariate Analysis|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btt687', '24292935', 'To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.'),
(78076, 2014, 'bio', 'Accessibility of microRNA binding sites in metastable RNA secondary structures in the presence of SNPs', 'Day, Luke|Abdelhadi Ep Souki, Ouala|Albrecht, Andreas A|Steinhöfel, Kathleen', 'Department of Informatics, Kings College London, London WC2R 2LS and Middlesex University London, School of Science and Technology, London NW4 4BT, UK.|Department of Informatics, Kings College London, London WC2R 2LS and Middlesex University London, School of Science and Technology, London NW4 4BT, UK.|Department of Informatics, Kings College London, London WC2R 2LS and Middlesex University London, School of Science and Technology, London NW4 4BT, UK.|Department of Informatics, Kings College London, London WC2R 2LS and Middlesex University London, School of Science and Technology, London NW4 4BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, '3 Untranslated Regions|Alleles|Binding Sites|MicroRNAs|Nucleic Acid Conformation|Polymorphism, Single Nucleotide|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btt695', '24292936', 'We study microRNA (miRNA) bindings to metastable RNA secondary structures close to minimum free energy conformations in the context of single nucleotide polymorphisms (SNPs) and messenger RNA (mRNA) concentration levels, i.e. whether features of miRNA bindings to metastable conformations could provide additional information supporting the differences in expression levels of the two sequences defined by a SNP. In our study, the instances [mRNA/3UTR, SNP, miRNA] were selected based on strong expression level analyses, SNP locations within binding regions and the computationally feasible identification of metastable conformations.'),
(78077, 2014, 'bio', 'Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction', 'Dümcke, Sebastian|Bräuer, Johannes|Anchang, Benedict|Spang, Rainer|Beerenwinkel, Niko|Tresch, Achim', 'Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.|Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.|Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.|Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.|Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.|Institute for Genetics, University of Cologne, 50674 Cologne, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Gene Center, Department of Biochemistry, Ludwig-Maximilians University, 81379 Munich, Germany, Insitute for Functional Genomics, 93053 Regensburg, Germany, Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA and ETH Zürich, Department of Biosystems Science and Engineering, Mattenstrasse 26 4058 Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Animals|Cell Differentiation|Embryonic Stem Cells|Feedback, Physiological|Gene Expression|Mice|Probability|RNA Interference|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btt696', '24292937', 'For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network.'),
(78078, 2014, 'bio', 'A mathematical model describing the mechanical kinetics of kinesin stepping', 'Khataee, Hamidreza|Wee-Chung Liew, Alan', 'School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD 4222, Australia.|School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD 4222, Australia.', 'Australia|Australia', 10, 'Adenosine Triphosphate|Hydrolysis|Kinesin|Kinetics|Microtubules|Models, Biological|Probability', 'Journal Article', '10.1093/bioinformatics/btt698', '24292938', 'Kinesin is a smart motor protein that steps processively forward and backward along microtubules (MTs). The mechanical kinetics of kinesin affecting its stepping behavior is not fully understood. Here, we propose a mathematical model to study the mechanical kinetics of forward and backward stepping of kinesin motor based on the four-state discrete stochastic model of the motor.'),
(78079, 2014, 'bio', 'GPU linear and non-linear Poisson-Boltzmann solver module for DelPhi', 'Colmenares, José|Ortiz, Jesús|Rocchia, Walter', 'Drug Discovery and Development Department and Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova - Italy.|Drug Discovery and Development Department and Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova - Italy.|Drug Discovery and Development Department and Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova - Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Amidohydrolases|Computational Biology|Humans|Nonlinear Dynamics|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/btt699', '24292939', 'In this work, we present a CUDA-based GPU implementation of a Poisson-Boltzmann equation solver, in both the linear and non-linear versions, using double precision. A finite difference scheme is adopted and made suitable for the GPU architecture. The resulting code was interfaced with the electrostatics software for biomolecules DelPhi, which is widely used in the computational biology community. The algorithm has been implemented using CUDA and tested over a few representative cases of biological interest. Details of the implementation and performance test results are illustrated. A speedup of ~10 times was achieved both in the linear and non-linear cases.'),
(78080, 2014, 'bio', 'Fingerprinting protein structures effectively and efficiently', 'Cui, Xuefeng|Li, Shuai Cheng|He, Lin|Li, Ming', 'David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.', 'Canada|Canada|Canada|Canada', 3, 'False Positive Reactions|Peptide Mapping|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt659', '24292940', 'One common task in structural biology is to assess the similarities and differences among protein structures. A variety of structure alignment algorithms and programs has been designed and implemented for this purpose. A major drawback with existing structure alignment programs is that they require a large amount of computational time, rendering them infeasible for pairwise alignments on large collections of structures. To overcome this drawback, a fragment alphabet learned from known structures has been introduced. The method, however, considers local similarity only, and therefore occasionally assigns high scores to structures that are similar only in local fragments.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78081, 2014, 'bio', 'AISAIC: a software suite for accurate identification of significant aberrations in cancers', 'Zhang, Bai|Hou, Xuchu|Yuan, Xiguo|Shih, Ie-Ming|Zhang, Zhen|Clarke, Robert|Wang, Roger R|Fu, Yi|Madhavan, Subha|Wang, Yue|Yu, Guoqiang', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xian 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.', 'China|China|China|China|China|China|China|China|China|China|China', 6, 'Bayes Theorem|Computer Simulation|DNA Copy Number Variations|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt693', '24292941', 'Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results, (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test, (iii) it estimates the fraction of normal contamination for each sample, (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations, and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines.'),
(78082, 2014, 'bio', 'LipidGO: database for lipid-related GO terms and applications', 'Fan, Mengyuan|Low, Hong Sang|Zhou, Hufeng|Wenk, Markus R|Wong, Limsoon', 'Department of Computer Science, National University of Singapore, NUS Graduate School of Integrative Science and Engineering, National University of Singapore, Singapore, National Research Foundation, Singapore, Department of Biochemistry and Department of Pathology, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, NUS Graduate School of Integrative Science and Engineering, National University of Singapore, Singapore, National Research Foundation, Singapore, Department of Biochemistry and Department of Pathology, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, NUS Graduate School of Integrative Science and Engineering, National University of Singapore, Singapore, National Research Foundation, Singapore, Department of Biochemistry and Department of Pathology, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, NUS Graduate School of Integrative Science and Engineering, National University of Singapore, Singapore, National Research Foundation, Singapore, Department of Biochemistry and Department of Pathology, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, NUS Graduate School of Integrative Science and Engineering, National University of Singapore, Singapore, National Research Foundation, Singapore, Department of Biochemistry and Department of Pathology, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Databases, Genetic|Gene Expression|Lipid Metabolism|Lipids|Proteins', 'Journal Article', '10.1093/bioinformatics/btt689', '24297518', 'Lipid, an essential class of biomolecules, is receiving increasing attention in the research community, especially with the development of analytical technique like mass spectrometry. Gene Ontology (GO) is the de facto standard function annotation scheme for gene products. Identification of both explicit and implicit lipid-related GO terms will help lipid research in many ways, e.g. assigning lipid function in protein function prediction.'),
(78083, 2014, 'bio', 'Topological augmentation to infer hidden processes in biological systems', 'Sunnåker, Mikael|Zamora-Sillero, Elias|López García de Lomana, Adrián|Rudroff, Florian|Sauer, Uwe|Stelling, Joerg|Wagner, Andreas', 'Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.|Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 9, 'Algorithms|Amino Acid Transport Systems, Basic|Bayes Theorem|Gastrointestinal Tract|Glutamine|Humans|Models, Biological|Pharmaceutical Preparations|Pharmacokinetics|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt638', '24297519', 'A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data.'),
(78084, 2014, 'bio', 'NextClip: an analysis and read preparation tool for Nextera Long Mate Pair libraries', 'Leggett, Richard M|Clavijo, Bernardo J|Clissold, Leah|Clark, Matthew D|Caccamo, Mario', 'The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich NR4 7UH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Genomic Library|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt702', '24297520', 'Illuminas recently released Nextera Long Mate Pair (LMP) kit enables production of jumping libraries of up to 12 kb. The LMP libraries are an invaluable resource for carrying out complex assemblies and other downstream bioinformatics analyses such as the characterization of structural variants. However, LMP libraries are intrinsically noisy and to maximize their value, post-sequencing data analysis is required. Standardizing laboratory protocols and the selection of sequenced reads for downstream analysis are non-trivial tasks. NextClip is a tool for analyzing reads from LMP libraries, generating a comprehensive quality report and extracting good quality trimmed and deduplicated reads.'),
(78085, 2014, 'bio', 'ISRNA: an integrative online toolkit for short reads from high-throughput sequencing data', 'Luo, Guan-Zheng|Yang, Wei|Ma, Ying-Ke|Wang, Xiu-Jie', 'State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing 100101, China.|State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing 100101, China.|State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing 100101, China.|State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing 100101, China.', 'China|China|China|China', 6, 'Chromosome Mapping|Genomics|High-Throughput Nucleotide Sequencing|Internet|MicroRNAs|RNA, Small Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt678', '24300438', 'Integrative Short Reads NAvigator (ISRNA) is an online toolkit for analyzing high-throughput small RNA sequencing data. Besides the high-speed genome mapping function, ISRNA provides statistics for genomic location, length distribution and nucleotide composition bias analysis of sequence reads. Number of reads mapped to known microRNAs and other classes of short non-coding RNAs, coverage of short reads on genes, expression abundance of sequence reads as well as some other analysis functions are also supported. The versatile search functions enable users to select sequence reads according to their sub-sequences, expression abundance, genomic location, relationship to genes, etc. A specialized genome browser is integrated to visualize the genomic distribution of short reads. ISRNA also supports management and comparison among multiple datasets.'),
(78086, 2014, 'bio', 'Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression', 'Yuan, Lei|Pan, Cheng|Ji, Shuiwang|McCutchan, Michael|Zhou, Zhi-Hua|Newfeld, Stuart J|Kumar, Sudhir|Ye, Jieping', 'School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.', 'China|China|China|China|China|China|China|China', 11, 'Algorithms|Animals|Computational Biology|Drosophila Proteins|Drosophila melanogaster|Embryo, Nonmammalian|Embryonic Development|Gene Expression Profiling|Gene Expression Regulation, Developmental|Image Processing, Computer-Assisted|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btt648', '24300439', 'Drosophila melanogaster is a major model organism for investigating the function and interconnection of animal genes in the earliest stages of embryogenesis. Today, images capturing Drosophila gene expression patterns are being produced at a higher throughput than ever before. The analysis of spatial patterns of gene expression is most biologically meaningful when images from a similar time point during development are compared. Thus, the critical first step is to determine the developmental stage of an embryo. This information is also needed to observe and analyze expression changes over developmental time. Currently, developmental stages (time) of embryos in images capturing spatial expression pattern are annotated manually, which is time- and labor-intensive. Embryos are often designated into stage ranges, making the information on developmental time course. This makes downstream analyses inefficient and biological interpretations of similarities and differences in spatial expression patterns challenging, particularly when using automated tools for analyzing expression patterns of large number of images.'),
(78087, 2014, 'bio', 'HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors', 'Gao, Chuan|Tignor, Nicole L|Salit, Jacqueline|Strulovici-Barel, Yael|Hackett, Neil R|Crystal, Ronald G|Mezey, Jason G', 'Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.|Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10021, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Gene Expression|Gene Expression Profiling|Humans|Lung|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btt690', '24307700', 'Identification of expression Quantitative Trait Loci (eQTL), the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis.'),
(78088, 2014, 'bio', 'HomSI: a homozygous stretch identifier from next-generation sequencing data', 'Görmez, Zeliha|Bakir-Gungor, Burcu|Sagiroglu, Mahmut Samil', 'Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK-BILGEM), 41470 Gebze, Kocaeli, Turkey and Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039 Kayseri, Turkey.|Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK-BILGEM), 41470 Gebze, Kocaeli, Turkey and Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039 Kayseri, Turkey.|Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK-BILGEM), 41470 Gebze, Kocaeli, Turkey and Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039 Kayseri, Turkey.', 'Turkey|Turkey|Turkey', 0, 'Consanguinity|Genes, Recessive|Genotype|High-Throughput Nucleotide Sequencing|Homozygote|Humans|Internet|Mutation|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt686', '24307702', 'In consanguineous families, as a result of inheriting the same genomic segments through both parents, the individuals have stretches of their genomes that are homozygous. This situation leads to the prevalence of recessive diseases among the members of these families. Homozygosity mapping is based on this observation, and in consanguineous families, several recessive disease genes have been discovered with the help of this technique. The researchers typically use single nucleotide polymorphism arrays to determine the homozygous regions and then search for the disease gene by sequencing the genes within this candidate disease loci. Recently, the advent of next-generation sequencing enables the concurrent identification of homozygous regions and the detection of mutations relevant for diagnosis, using data from a single sequencing experiment. In this respect, we have developed a novel tool that identifies homozygous regions using deep sequence data. Using *.vcf (variant call format) files as an input file, our program identifies the majority of homozygous regions found by microarray single nucleotide polymorphism genotype data.'),
(78089, 2014, 'bio', 'Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network', 'Jung, Nicolas|Bertrand, Frédéric|Bahram, Seiamak|Vallat, Laurent|Maumy-Bertrand, Myriam', 'INSERM UMR S_1109, Labex Transplantex, FMTS, Hôpitaux and Faculté de Médecine, Université de Strasbourg, 67085 Strasbourg Cedex and IRMA, CNRS UMR 7501, Labex IRMIA, Université de Strasbourg, 67084 Strasbourg Cedex, France.|INSERM UMR S_1109, Labex Transplantex, FMTS, Hôpitaux and Faculté de Médecine, Université de Strasbourg, 67085 Strasbourg Cedex and IRMA, CNRS UMR 7501, Labex IRMIA, Université de Strasbourg, 67084 Strasbourg Cedex, France.|INSERM UMR S_1109, Labex Transplantex, FMTS, Hôpitaux and Faculté de Médecine, Université de Strasbourg, 67085 Strasbourg Cedex and IRMA, CNRS UMR 7501, Labex IRMIA, Université de Strasbourg, 67084 Strasbourg Cedex, France.|INSERM UMR S_1109, Labex Transplantex, FMTS, Hôpitaux and Faculté de Médecine, Université de Strasbourg, 67085 Strasbourg Cedex and IRMA, CNRS UMR 7501, Labex IRMIA, Université de Strasbourg, 67084 Strasbourg Cedex, France.|INSERM UMR S_1109, Labex Transplantex, FMTS, Hôpitaux and Faculté de Médecine, Université de Strasbourg, 67085 Strasbourg Cedex and IRMA, CNRS UMR 7501, Labex IRMIA, Université de Strasbourg, 67084 Strasbourg Cedex, France.', 'France|France|France|France|France', 2, 'Animals|Cells, Cultured|Computational Biology|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Lymphocytes|Mice|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btt705', '24307703', 'Temporal gene interactions, in response to environmental stress, form a complex system that can be efficiently described using gene regulatory networks. They allow highlighting the more influential genes and spotting some targets for biological intervention experiments. Despite that many reverse engineering tools have been designed, the Cascade package is an integrated solution adding several new and original key features such as the ability to predict changes in gene expressions after a biological perturbation in the network and graphical outputs that allow monitoring the spread of a signal through the network.'),
(78090, 2014, 'bio', 'Joint estimation of isoform expression and isoform-specific read distribution using multisample RNA-Seq data', 'Suo, Chen|Calza, Stefano|Salim, Agus|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Italy and Department of Mathematics and Statistics, La Trobe University, Australia.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Italy and Department of Mathematics and Statistics, La Trobe University, Australia.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Italy and Department of Mathematics and Statistics, La Trobe University, Australia.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Italy and Department of Mathematics and Statistics, La Trobe University, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Alternative Splicing|Animals|Brain|Computational Biology|Computer Simulation|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Liver|Mice|Models, Statistical|Muscle, Skeletal|Protein Isoforms|RNA, Messenger|Real-Time Polymerase Chain Reaction|Reverse Transcriptase Polymerase Chain Reaction|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt704', '24307704', 'RNA-sequencing technologies provide a powerful tool for expression analysis at gene and isoform level, but accurate estimation of isoform abundance is still a challenge. Standard assumption of uniform read intensity would yield biased estimates when the read intensity is in fact non-uniform. The problem is that, without strong assumptions, the read intensity pattern is not identifiable from data observed in a single sample.'),
(78091, 2014, 'bio', 'Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection', 'Liu, Bin|Zhang, Deyuan|Xu, Ruifeng|Xu, Jinghao|Wang, Xiaolong|Chen, Qingcai|Dong, Qiwen|Chou, Kuo-Chen', 'School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.|School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.', 'China|China|China|China|China|China|China|China', 248, 'Algorithms|Amino Acid Motifs|Cyclin A|Evolution, Molecular|Humans|Nerve Tissue Proteins|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt709', '24318998', 'Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.'),
(78092, 2014, 'bio', 'A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data', 'Lartillot, Nicolas', 'Laboratoire de Biométrie et Biologie Évolutive, Centre National de la Recherche Scientifique, UMR 5558. Université Lyon 1, F-69622 Villeurbanne, France and Centre Robert-Cedergren pour la Bioinformatique, Département de Biochimie, Université de Montréal, Québec, Canada.', 'Canada', 0, 'Algorithms|Archaea|Base Composition|Bayes Theorem|Biological Evolution|Data Interpretation, Statistical|Linear Models|Markov Chains|Models, Biological|Monte Carlo Method|Phenotype|Phylogeny|RNA, Ribosomal|Temperature', 'Journal Article', '10.1093/bioinformatics/btt707', '24318999', 'Correlation between life history or ecological traits and genomic features such as nucleotide or amino acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be seen as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters coevolving along the phylogeny.'),
(78093, 2014, 'bio', 'Chemical structure informing statistical hypothesis testing in metabolomics', 'Zhu, Hongjie|Luo, Man', 'Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA.|Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA.', 'United States|United States', 1, 'Alzheimer Disease|Biomarkers|Computer Simulation|Data Interpretation, Statistical|Gas Chromatography-Mass Spectrometry|Humans|Metabolomics|Molecular Structure|Regression Analysis', 'Comparative Study', '10.1093/bioinformatics/btt708', '24319000', 'Metabolomics has been shown as an effective tool to study various biological and biomedical phenotypes, whereas interrogating the inherently noisy metabolite concentration data with limited sample size remains a major challenge. Accumulating evidence suggests that metabolites structures are relevant to their bioactivities.'),
(78094, 2014, 'bio', 'ChromoHub V2: cancer genomics', 'Shah, Muhammad A|Denton, Emily L|Liu, Lihua|Schapira, Matthieu', 'Structural Genomics Consortium, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada.|Structural Genomics Consortium, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada.|Structural Genomics Consortium, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada.|Structural Genomics Consortium, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Chromatin|DNA Methylation|Epigenesis, Genetic|Gene Expression Profiling|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Histones|Humans|Mutation|Neoplasms|Promoter Regions, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btt710', '24319001', 'Cancer genomics data produced by next-generation sequencing support the notion that epigenetic mechanisms play a central role in cancer. We have previously developed Chromohub, an open access online interface where users can map chemical, structural and biological data from public repositories on phylogenetic trees of protein families involved in chromatin mediated-signaling. Here, we describe a cancer genomics interface that was recently added to Chromohub, the frequency of mutation, amplification and change in expression of chromatin factors across large cohorts of cancer patients is regularly extracted from The Cancer Genome Atlas and the International Cancer Genome Consortium and can now be mapped on phylogenetic trees of epigenetic protein families. Explorators of chromatin signaling can now easily navigate the cancer genomics landscape of writers, readers and erasers of histone marks, chromatin remodeling complexes, histones and their chaperones.'),
(78095, 2014, 'bio', 'RNA-seq differential expression studies: more sequence or more replication?', 'Liu, Yuwen|Zhou, Jie|White, Kevin P', 'Institute of Genomics and Systems Biology, Committee on Development, Regeneration, and Stem Cell Biology and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.|Institute of Genomics and Systems Biology, Committee on Development, Regeneration, and Stem Cell Biology and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.|Institute of Genomics and Systems Biology, Committee on Development, Regeneration, and Stem Cell Biology and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.', 'United States|United States|United States', 0, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|MCF-7 Cells|Reproducibility of Results|Sample Size|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt688', '24319002', 'RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources.'),
(78096, 2014, 'bio', 'forqs: forward-in-time simulation of recombination, quantitative traits and selection', 'Kessner, Darren|Novembre, John', 'Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095 and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095 and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.', 'United States|United States', 23, 'Algorithms|Computer Simulation|Evolution, Molecular|Genetics, Population|Haplotypes|Humans|Quantitative Trait Loci|Recombination, Genetic|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btt712', '24336146', 'forqs is a forward-in-time simulation of recombination, quantitative traits and selection. It was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits.'),
(78097, 2014, 'bio', 'A combinatorial approach to graphlet counting', 'Hoxc4x8devar, Tomaxc5xbe|Demxc5xa1ar, Janez', 'Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia.', 'Slovenia|Slovenia', 0, 'Algorithms|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Humans|Models, Biological|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btt717', '24336411', 'Small-induced subgraphs called graphlets are emerging as a possible tool for exploration of global and local structure of networks and for analysis of roles of individual nodes. One of the obstacles to their wider use is the computational complexity of algorithms for their discovery and counting.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78098, 2014, 'bio', 'slaMEM: efficient retrieval of maximal exact matches using a sampled LCP array', 'Fernandes, Francisco|Freitas, Ana T', 'Knowledge Discovery and Bioinformatics Group (KDBIO), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol, 9, 1000-029 Lisbon and Department of Computer Science and Engineering, Instituto Superior Técnico (IST) - Universidade de Lisboa, Avenida Rovisco Pais, 1, 1049-001 Lisbon, Portugal.|Knowledge Discovery and Bioinformatics Group (KDBIO), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol, 9, 1000-029 Lisbon and Department of Computer Science and Engineering, Instituto Superior Técnico (IST) - Universidade de Lisboa, Avenida Rovisco Pais, 1, 1049-001 Lisbon, Portugal.', 'Portugal|Portugal', 8, 'Algorithms|Animals|Drosophila|Genome|Genomics|Humans|Mice|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt706', '24336412', 'Maximal exact matches, or just MEMs, are a powerful tool in the context of multiple sequence alignment and approximate string matching. The most efficient algorithms to collect them are based on compressed indexes that rely on longest common prefix array-centered data structures. However, their space-efficient representations make use of encoding techniques that are expensive from a computational point of view. With the deluge of data generated by high-throughput sequencing, new approaches need to be developed to deal with larger genomic sequences.'),
(78099, 2014, 'bio', 'HAMMER: automated operation of mass frontier to construct in silico mass spectral fragmentation libraries', 'Zhou, Jiarui|Weber, Ralf J M|Allwood, J William|Mistrik, Robert|Zhu, Zexuan|Ji, Zhen|Chen, Siping|Dunn, Warwick B|He, Shan|Viant, Mark R', 'College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.|College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK, HighChem, Ltd., Leskova 11, 81104 Bratislava, Slovakia, Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China and School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.', 'China|China|China|China|China|China|China|China|China|China', 27, 'Computer Simulation|Mass Spectrometry|Metabolomics|Pattern Recognition, Automated|Pharmaceutical Preparations|Phenylalanine|Software', 'Journal Article', '10.1093/bioinformatics/btt711', '24336413', 'Experimental MS(n) mass spectral libraries currently do not adequately cover chemical space. This limits the robust annotation of metabolites in metabolomics studies of complex biological samples. In silico fragmentation libraries would improve the identification of compounds from experimental multistage fragmentation data when experimental reference data are unavailable. Here, we present a freely available software package to automatically control Mass Frontier software to construct in silico mass spectral libraries and to perform spectral matching. Based on two case studies, we have demonstrated that high-throughput automation of Mass Frontier allows researchers to generate in silico mass spectral libraries in an automated and high-throughput fashion with little or no human intervention required.'),
(78100, 2014, 'bio', 'BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks', 'Alkan, Ferhat|Erten, Cesim', 'Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.', 'Turkey|Turkey', 0, 'Algorithms|Animals|Humans|Models, Biological|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt713', '24336414', 'Global many-to-many alignment of biological networks has been a central problem in comparative biological network studies. Given a set of biological interaction networks, the informal goal is to group together related nodes. For the case of protein-protein interaction networks, such groups are expected to form clusters of functionally orthologous proteins. Construction of such clusters for networks from different species may prove useful in determining evolutionary relationships, in predicting the functions of proteins with unknown functions and in verifying those with estimated functions.'),
(78101, 2014, 'bio', 'ChAMP: 450k Chip Analysis Methylation Pipeline', 'Morris, Tiffany J|Butcher, Lee M|Feber, Andrew|Teschendorff, Andrew E|Chakravarthy, Ankur R|Wojdacz, Tomasz K|Beck, Stephan', 'Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.|Medical Genomics, and Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai 200031, China and Institute of Environmental Medicine, Karolinska Instytutet, 17177 Stockholm, Sweden.', 'China|China|China|China|China|China|China', 0, 'DNA Copy Number Variations|DNA Methylation|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btt684', '24336642', 'The Illumina Infinium HumanMethylation450 BeadChip is a new platform for high-throughput DNA methylation analysis. Several methods for normalization and processing of these data have been published recently. Here we present an integrated analysis pipeline offering a choice of the most popular normalization methods while also introducing new methods for calling differentially methylated regions and detecting copy number aberrations.'),
(78102, 2014, 'bio', 'BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing', 'Yan, Song|Li, Yun', 'Department of Biostatistics, University of North Carolina, 3101 McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Biostatistics, University of North Carolina, 3101 McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.', 'United States|United States', 0, 'Anxiety|Case-Control Studies|Computer Simulation|Data Interpretation, Statistical|Genetic Association Studies|Genetic Variation|Genotype|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btt719', '24336643', 'Despite its great capability to detect rare variant associations, next-generation sequencing is still prohibitively expensive when applied to large samples. In case-control studies, it is thus appealing to sequence only a subset of cases to discover variants and genotype the identified variants in controls and the remaining cases under the reasonable assumption that causal variants are usually enriched among cases. However, this approach leads to inflated type-I error if analyzed naively for rare variant association. Several methods have been proposed in recent literature to control type-I error at the cost of either excluding some sequenced cases or correcting the genotypes of discovered rare variants. All of these approaches thus suffer from certain extent of information loss and thus are underpowered. We propose a novel method (BETASEQ), which corrects inflation of type-I error by supplementing pseudo-variants while keeps the original sequence and genotype data intact. Extensive simulations and real data analysis demonstrate that, in most practical situations, BETASEQ leads to higher testing powers than existing approaches with guaranteed (controlled or conservative) type-I error.'),
(78103, 2014, 'bio', 'VIRAPOPS: a forward simulator dedicated to rapidly evolved viral populations', 'Petitjean, Michel|Vanet, Anne', 'Department of Biology, Univ Paris Diderot, Sorbonne Paris Cité, MTI, INSERM UMR-S 973, CNRS, UMR7592, Institut Jacques Monod, F-75013 Paris and Atelier de Bio Informatique, F-75005 Paris, France.|Department of Biology, Univ Paris Diderot, Sorbonne Paris Cité, MTI, INSERM UMR-S 973, CNRS, UMR7592, Institut Jacques Monod, F-75013 Paris and Atelier de Bio Informatique, F-75005 Paris, France.', 'France|France', 4, 'Antiviral Agents|Computational Biology|Computer Simulation|Evolution, Molecular|Genetic Variation|Genome, Viral|Genomics|HIV|HIV Infections|Humans|RNA Viruses|Selection, Genetic|Software|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btt724', '24336644', 'Daily, mutability and recombination of RNA viruses result in the production of million variants. All these rapid genomic changes directly influence the functional sites of the protein, its 3D structure or its drug resistances. Therefore, it is important to simulate these drastic switches to determine their effects on virus populations. Many computer programs are able to simulate specific variations in DNA genomes, but are generally non-adapted to RNA viruses. They simulate site-specific selection pressures, but rarely pressures on covariant or on higher order correlated sites and no at all on synthetic lethal groups. That is why we felt it important to create VIRAPOPS, a forward simulator that models specific RNA virus functions. It was designed for computational biologists, biologists and virologists.'),
(78104, 2014, 'bio', 'PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants', 'Li, Biao|Wang, Gao|Leal, Suzanne M', 'Center for Statistical Genetics, Department of Molecular and Human Genetics, One Baylor Plaza 700D, Baylor College of Medicine, Houston, TX 77030, USA.|Center for Statistical Genetics, Department of Molecular and Human Genetics, One Baylor Plaza 700D, Baylor College of Medicine, Houston, TX 77030, USA.|Center for Statistical Genetics, Department of Molecular and Human Genetics, One Baylor Plaza 700D, Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States', 2, 'Genetic Association Studies|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Phenotype|Quality Control|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btt682', '24336645', 'Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership, control for missing covariate data, remove related individuals, duplicate samples and individuals with incorrect sex specification, recode primary traits and covariates, transform data, remove or winsorize outliers, select covariates for analysis, and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data.'),
(78105, 2014, 'bio', 'Sequence-based Gaussian network model for protein dynamics', 'Zhang, Hua|Kurgan, Lukasz', 'School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, P.R. China and Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada.|School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, P.R. China and Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada.', 'Canada|Canada', 3, 'Algorithms|Animals|Arthropod Proteins|Bacterial Proteins|Cattle|Computer Simulation|Lactoglobulins|Linear Models|Models, Molecular|Protein Conformation|Proteins|Quorum Sensing|Rhipicephalus|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt716', '24336646', 'Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.'),
(78106, 2014, 'bio', 'Genes related to differentiation are correlated with the gene regulatory network structure', 'Bodaker, Matan|Meshorer, Eran|Mitrani, Eduardo|Louzoun, Yoram', 'Department of Cell and Developmental Biology and Department of Genetics, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel and Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel.|Department of Cell and Developmental Biology and Department of Genetics, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel and Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel.|Department of Cell and Developmental Biology and Department of Genetics, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel and Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel.|Department of Cell and Developmental Biology and Department of Genetics, The Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel and Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel.', 'Israel|Israel|Israel|Israel', 2, 'Algorithms|Cell Differentiation|Gene Regulatory Networks|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btt685', '24336647', 'Many secondary messengers, receptors and transcription factors are related to cell differentiation. Their role in cell differentiation can be affected by their position in the gene regulatory network. Here, we test whether the properties of the gene regulatory network can highlight which genes and proteins are associated with cell differentiation. We use a previously developed purely theoretical algorithm built to detect nodes that can induce a state change in Boolean gene regulatory networks, and show that most genes predicted to participate in differentiation in the theoretical framework are also experimentally known to be associated with such differentiation. These results show that genes related to differentiation are associated with specific features of the genetic regulatory network. The proposed algorithm produces a better classification than simple network measures such as the nodes degree or centrality. Boolean networks were used in many previous theoretical models. Here, we show a direct application of such networks to the detection of genes and subnetworks related to differentiation. The subnetwork emerging from the genes and edges that are predicted to be associated with differentiation are the most active molecular pathways experimentally described to be involved in cell differentiation.'),
(78107, 2014, 'bio', 'DIVE: a data intensive visualization engine', 'Bromley, Dennis|Rysavy, Steven J|Su, Robert|Toofanny, Rudesh D|Schmidlin, Tom|Daggett, Valerie', 'Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States|United States', 8, 'Computational Biology|Computer Graphics|Computer Simulation|Documentation|Humans|Mutant Proteins|Mutation|Software|Superoxide Dismutase|Superoxide Dismutase-1|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btt721', '24336804', 'Modern scientific investigation is generating increasingly larger datasets, yet analyzing these data with current tools is challenging. DIVE is a software framework intended to facilitate big data analysis and reduce the time to scientific insight. Here, we present features of the framework and demonstrate DIVEs application to the Dynameomics project, looking specifically at two proteins.'),
(78108, 2014, 'bio', 'Causal analysis approaches in Ingenuity Pathway Analysis', 'Krämer, Andreas|Green, Jeff|Pollard, Jack|Tugendreich, Stuart', 'Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.|Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.|Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.|Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.', 'United States|United States|United States|United States', 1420, 'Algorithms|Breast Neoplasms|Causality|Computational Biology|Female|Gene Expression Profiling|Gene Regulatory Networks|Human Umbilical Vein Endothelial Cells|Humans|Knowledge Bases|MCF-7 Cells', 'Journal Article', '10.1093/bioinformatics/btt703', '24336805', 'Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.'),
(78109, 2014, 'bio', 'NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks', 'Hu, Jialu|Kehr, Birte|Reinert, Knut', 'Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany and Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany and Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany and Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.', 'Germany|Germany|Germany', 36, 'Algorithms|Animals|Bacteria|Computational Biology|Databases, Protein|Gene Regulatory Networks|Humans|Models, Biological|Protein Interaction Mapping|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt715', '24336806', 'Owing to recent advancements in high-throughput technologies, protein-protein interaction networks of more and more species become available in public databases. The question of how to identify functionally conserved proteins across species attracts a lot of attention in computational biology. Network alignments provide a systematic way to solve this problem. However, most existing alignment tools encounter limitations in tackling this problem. Therefore, the demand for faster and more efficient alignment tools is growing.'),
(78110, 2014, 'bio', 'ORCA: a COBRA toolbox extension for model-driven discovery and analysis', 'Mao, Longfei|Verwoerd, Wynand S', 'Department of Molecular Biosciences, Centre for Advanced Computational Solutions, Lincoln University, Lincoln 7647, New Zealand.|Department of Molecular Biosciences, Centre for Advanced Computational Solutions, Lincoln University, Lincoln 7647, New Zealand.', 'New Zealand|New Zealand', 10, 'Biomass|Bioreactors|Computational Biology|Computer Simulation|Enzymes|Metabolic Networks and Pathways|Metabolomics|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btt723', '24336807', 'Over past decades, constraint-based modelling has emerged as an important approach to obtain referential information about mechanisms behind biological phenotypes and identify physiological and perturbed metabolic states at genome-scale. However, application of this novel approach to systems biology in biotechnology is still hindered by the functionalities of the existing modelling software. To augment the usability of the constraint-based approach for various use scenarios, we present ORCA, a Matlab package, which extends the scope of established Constraint-Based Reconstruction and Analysis metabolic modelling and includes three unique functionalities: (i) a framework method integrating three analyses of multi-objective optimization, robustness analysis and fractional benefit analysis, (ii) metabolic pathways identification with futile loop elimination and (iii) a dynamic flux balance analysis framework incorporating kinetic constraints.'),
(78111, 2014, 'bio', 'ProbeSelect: selecting differentially expressed probes in transcriptional profile data', 'Hosur, Raghavendra|Szak, Suzanne|Thai, Alice|Allaire, Norm|Bienkowska, Jadwiga', 'Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.|Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.|Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.|Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.|Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States', 1, 'Algorithms|Case-Control Studies|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Multiple Sclerosis, Chronic Progressive|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Software', 'Comparative Study', '10.1093/bioinformatics/btt720', '24336808', 'Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a simple statistical technique that identifies differentially expressed probes in heterogeneous populations as compared with controls.'),
(78112, 2014, 'bio', 'NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes', 'Ofer, Dan|Linial, Michal', 'Department of Biological Chemistry, Institute of Life Sciences, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Givat Ram 91904, Israel.|Department of Biological Chemistry, Institute of Life Sciences, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Givat Ram 91904, Israel.', 'Israel|Israel', 14, 'Amino Acid Sequence|Animals|Artificial Intelligence|Databases, Genetic|Insecta|Molecular Sequence Data|Neuropeptides|Protein Precursors|Proteome|Sequence Alignment|Sequence Homology, Amino Acid|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt725', '24336809', 'The evolution of multicellular organisms is associated with increasing variability of molecules governing behavioral and physiological states. This is often achieved by neuropeptides (NPs) that are produced in neurons from a longer protein, named neuropeptide precursor (NPP). The maturation of NPs occurs through a sequence of proteolytic cleavages. The difficulty in identifying NPPs is a consequence of their diversity and the lack of applicable sequence similarity among the short functionally related NPs.'),
(78113, 2014, 'bio', 'A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data', 'Zhou, Cong|Walker, Michael J|Williamson, Andrew J K|Pierce, Andrew|Berzuini, Carlo|Dive, Caroline|Whetton, Anthony D', 'Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK, Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, University of Manchester, Manchester M20 3LJ, UK and Centre for Biostatistics, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Animals|Cells, Cultured|Escherichia coli Proteins|Mice|Models, Statistical|Peptides|Precursor Cells, B-Lymphoid|Proteins|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Staining and Labeling', 'Journal Article', '10.1093/bioinformatics/btt722', '24344193', 'Isobaric tag for relative and absolute quantitation (iTRAQ) is a widely used method in quantitative proteomics. A robust data analysis strategy is required to determine protein quantification reliability, i.e. changes due to biological regulation rather than technical variation, so that proteins that are differentially expressed can be identified.'),
(78114, 2014, 'bio', 'SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods', 'Chang, Cheng|Zhang, Jiyang|Han, Mingfei|Ma, Jie|Zhang, Wei|Wu, Songfeng|Liu, Kehui|Xie, Hongwei|He, Fuchu|Zhu, Yunping', 'Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.|Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.', 'China|China|China|China|China|China|China|China|China|China', 16, 'Chromatography, Liquid|Isotope Labeling|Mass Spectrometry|Peptides|Proteome|Proteomics|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btt726', '24344194', 'With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses.'),
(78115, 2014, 'bio', 'Wigwams: identifying gene modules co-regulated across multiple biological conditions', 'Polanski, Krzysztof|Rhodes, Johanna|Hill, Claire|Zhang, Peijun|Jenkins, Dafyd J|Kiddle, Steven J|Jironkin, Aleksey|Beynon, Jim|Buchanan-Wollaston, Vicky|Ott, Sascha|Denby, Katherine J', 'Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 18, 'Arabidopsis|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Plant|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btt728', '24351708', 'Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.'),
(78116, 2014, 'bio', 'CrossMap: a versatile tool for coordinate conversion between genome assemblies', 'Zhao, Hao|Sun, Zhifu|Wang, Jing|Huang, Haojie|Kocher, Jean-Pierre|Wang, Liguo', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA and Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.', 'United States|United States|United States|United States|United States|United States', 116, 'Animals|Base Sequence|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt730', '24351709', 'Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc.'),
(78117, 2014, 'bio', 'Site-heterogeneous mutation-selection models within the PhyloBayes-MPI package', 'Rodrigue, Nicolas|Lartillot, Nicolas', 'Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary AB T2N 1N4, Canada and UMR CNRS 5558 - LBBE, Université Lyon 1, Villeurbanne Cedex, France.|Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary AB T2N 1N4, Canada and UMR CNRS 5558 - LBBE, Université Lyon 1, Villeurbanne Cedex, France.', 'Canada|Canada', 31, 'Bayes Theorem|Codon|Models, Genetic|Mutation|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btt729', '24351710', 'In recent years, there has been an increasing interest in the potential of codon substitution models for a variety of applications. However, the computational demands of these models have sometimes lead to the adoption of oversimplified assumptions, questionable statistical methods or a limited focus on small data sets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78118, 2014, 'bio', 'ISCB: past-present perspective for the International Society for Computational Biology', 'Rost, Burkhard', 'TUM, Department of Informatics, Bioinformatics and Computational Biology - i12, TU Munich, Boltzmannstr. 3, 85748 Garching/Munich, Germany and International Society for Computational Biology, 9500 Gilman Drive, MC 0505 UCSD/SDSC, La Jolla, CA 92093-0505, USA.', 'Germany', 1, 'Biometry|Computational Biology|Congresses as Topic', 'Journal Article', '10.1093/bioinformatics/btt669', '24352412', 'Since its establishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly toward advancing the understanding of living systems through computation. The ISCB represents nearly 3000 members working in >70 countries. It has doubled the number of members since 2007. At the same time, the number of meetings organized by the ISCB has increased from two in 2007 to eight in 2013, and the society has cemented many lasting alliances with regional societies and specialist groups. ISCB is ready to grow into a challenging and promising future. The progress over the past 7 years has resulted from the vision, and possibly more importantly, the passion and hard working dedication of many individuals. '),
(78119, 2014, 'bio', 'Efficient clustering of identity-by-descent between multiple individuals', 'Qian, Yu|Browning, Brian L|Browning, Sharon R', 'Bioinformatics Research Center, Aarhus Universitet, 8000C Aarhus, Denmark, Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.|Bioinformatics Research Center, Aarhus Universitet, 8000C Aarhus, Denmark, Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.|Bioinformatics Research Center, Aarhus Universitet, 8000C Aarhus, Denmark, Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.', 'Denmark|Denmark|Denmark', 1, 'Algorithms|Cholesterol, LDL|Cluster Analysis|Cohort Studies|Genomics|Haplotypes|Humans|Polymorphism, Single Nucleotide|Proprotein Convertase 9|Proprotein Convertases|Serine Endopeptidases', 'Journal Article', '10.1093/bioinformatics/btt734', '24363374', 'Most existing identity-by-descent (IBD) detection methods only consider haplotype pairs, less attention has been paid to considering multiple haplotypes simultaneously, even though IBD is an equivalence relation on haplotypes that partitions a set of haplotypes into IBD clusters. Multiple-haplotype IBD clusters may have advantages over pairwise IBD in some applications, such as IBD mapping. Existing methods for detecting multiple-haplotype IBD clusters are often computationally expensive and unable to handle large samples with thousands of haplotypes.'),
(78120, 2014, 'bio', 'GPU-Meta-Storms: computing the structure similarities among massive amount of microbial community samples using GPU', 'Su, Xiaoquan|Wang, Xuetao|Jing, Gongchao|Ning, Kang', 'Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioenergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, P. R. China.|Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioenergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, P. R. China.|Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioenergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, P. R. China.|Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioenergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, P. R. China.', 'China|China|China|China', 10, 'Algorithms|Bacteria|Data Mining|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btt736', '24363375', 'The number of microbial community samples is increasing with exponential speed. Data-mining among microbial community samples could facilitate the discovery of valuable biological information that is still hidden in the massive data. However, current methods for the comparison among microbial communities are limited by their ability to process large amount of samples each with complex community structure.'),
(78121, 2014, 'bio', 'JEPETTO: a Cytoscape plugin for gene set enrichment and topological analysis based on interaction networks', 'Winterhalter, Charles|Widera, Pawexc5x82|Krasnogor, Natalio', 'Institute of Systems & Synthetic Biology, University of Evry Val-dEssonne, 91000 Evry, France and School of Computing Science, Newcastle University, Newcastle NE1 7RU, UK.|Institute of Systems & Synthetic Biology, University of Evry Val-dEssonne, 91000 Evry, France and School of Computing Science, Newcastle University, Newcastle NE1 7RU, UK.|Institute of Systems & Synthetic Biology, University of Evry Val-dEssonne, 91000 Evry, France and School of Computing Science, Newcastle University, Newcastle NE1 7RU, UK.', 'France|France|France', 0, 'Alzheimer Disease|Gene Expression Regulation|Humans|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btt732', '24363376', 'JEPETTO (Java Enrichment of Pathways Extended To TOpology) is a Cytoscape 3.x plugin performing integrative human gene set analysis. It identifies functional associations between genes and known cellular pathways, and processes using protein interaction networks and topological analysis. The plugin integrates information from three separate web servers we published previously, specializing in enrichment analysis, pathways expansion and topological matching. This integration substantially simplifies the analysis of user gene sets and the interpretation of the results. We demonstrate the utility of the JEPETTO plugin on a set of misregulated genes associated with Alzheimers disease.'),
(78122, 2014, 'bio', 'WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis', 'Zerbino, Daniel R|Johnson, Nathan|Juettemann, Thomas|Wilder, Steven P|Flicek, Paul', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 19, 'Genome|Genomic Library|Genomics|High-Throughput Nucleotide Sequencing|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt737', '24363377', 'Using high-throughput sequencing, researchers are now generating hundreds of whole-genome assays to measure various features such as transcription factor binding, histone marks, DNA methylation or RNA transcription. Displaying so much data generally leads to a confusing accumulation of plots. We describe here a multithreaded library that computes statistics on large numbers of datasets (Wiggle, BigWig, Bed, BigBed and BAM), generating statistical summaries within minutes with limited memory requirements, whether on the whole genome or on selected regions.'),
(78123, 2014, 'bio', 'SplicePlot: a utility for visualizing splicing quantitative trait loci', 'Wu, Eric|Nance, Tracy|Montgomery, Stephen B', 'Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305-5324, USA.|Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305-5324, USA.|Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305-5324, USA.', 'United States|United States|United States', 0, 'Alternative Splicing|Genomics|Programming Languages|Quantitative Trait Loci|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt733', '24363378', 'RNA sequencing has provided unprecedented resolution of alternative splicing and splicing quantitative trait loci (sQTL). However, there are few tools available for visualizing the genotype-dependent effects of splicing at a population level. SplicePlot is a simple command line utility that produces intuitive visualization of sQTLs and their effects. SplicePlot takes mapped RNA sequencing reads in BAM format and genotype data in VCF format as input and outputs publication-quality Sashimi plots, hive plots and structure plots, enabling better investigation and understanding of the role of genetics on alternative splicing and transcript structure.'),
(78124, 2014, 'bio', 'Ondex Web: web-based visualization and exploration of heterogeneous biological networks', 'Taubert, Jan|Hassani-Pak, Keywan|Castells-Brooke, Nathalie|Rawlings, Christopher J', 'Rothamsted Research, Computational and Systems Biology, Harpenden, AL5 2JQ, UK.|Rothamsted Research, Computational and Systems Biology, Harpenden, AL5 2JQ, UK.|Rothamsted Research, Computational and Systems Biology, Harpenden, AL5 2JQ, UK.|Rothamsted Research, Computational and Systems Biology, Harpenden, AL5 2JQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Biology|Data Mining|Internet|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btt740', '24363379', 'Ondex Web is a new web-based implementation of the network visualization and exploration tools from the Ondex data integration platform. New features such as context-sensitive menus and annotation tools provide users with intuitive ways to explore and manipulate the appearance of heterogeneous biological networks. Ondex Web is open source, written in Java and can be easily embedded into Web sites as an applet. Ondex Web supports loading data from a variety of network formats, such as XGMML, NWB, Pajek and OXL.'),
(78125, 2014, 'bio', 'BiPACE 2D--graph-based multiple alignment for comprehensive 2D gas chromatography-mass spectrometry', 'Hoffmann, Nils|Wilhelm, Mathias|Doebbe, Anja|Niehaus, Karsten|Stoye, Jens', 'Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.|Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.|Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.|Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.|Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Automation, Laboratory|Chlamydomonas reinhardtii|Gas Chromatography-Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btt738', '24363380', 'Comprehensive 2D gas chromatography-mass spectrometry is an established method for the analysis of complex mixtures in analytical chemistry and metabolomics. It produces large amounts of data that require semiautomatic, but preferably automatic handling. This involves the location of significant signals (peaks) and their matching and alignment across different measurements. To date, there exist only a few openly available algorithms for the retention time alignment of peaks originating from such experiments that scale well with increasing sample and peak numbers, while providing reliable alignment results.'),
(78126, 2014, 'bio', 'SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization', 'Aldecoa, Rodrigo|Marín, Ignacio', 'Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas (IBV-CSIC), Valencia 46010, Spain.|Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas (IBV-CSIC), Valencia 46010, Spain.', 'Spain|Spain', 0, 'Algorithms|Neural Networks (Computer)|Programming Languages|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btt741', '24363381', 'Detecting communities and densely connected groups may contribute to unravel the underlying relationships among the units present in diverse biological networks (e.g. interactomes, coexpression networks, ecological networks). We recently showed that communities can be precisely characterized by maximizing Surprise, a global network parameter. Here, we present SurpriseMe, a tool that integrates the outputs of seven of the best algorithms available to estimate the maximum Surprise value. SurpriseMe also generates distance matrices that allow visualizing the relationships among the solutions generated by the algorithms. We show that the communities present in small- and medium-sized networks, with up to 10 000 nodes, can be easily characterized: on standard PC computers, these analyses take less than an hour. Also, four of the algorithms may rapidly analyze networks with up to 100 000 nodes, given enough memory resources. Because of its performance and simplicity, SurpriseMe is a reference tool for community structure characterization.'),
(78127, 2014, 'bio', 'Phylogeny-based classification of microbial communities', 'Tanaseichuk, Olga|Borneman, James|Jiang, Tao', 'Department of Computer Science and Engineering, Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521 USA and School of Information Science and Technology, Tsinghua University, Beijing 100084, China.|Department of Computer Science and Engineering, Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521 USA and School of Information Science and Technology, Tsinghua University, Beijing 100084, China.|Department of Computer Science and Engineering, Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521 USA and School of Information Science and Technology, Tsinghua University, Beijing 100084, China.', 'China|China|China', 0, 'Algorithms|Bacteria|Computational Biology|Computer Simulation|High-Throughput Nucleotide Sequencing|Metagenomics|Microbiota|Phylogeny|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bioinformatics/btt700', '24369151', 'Next-generation sequencing coupled with metagenomics has led to the rapid growth of sequence databases and enabled a new branch of microbiology called comparative metagenomics. Comparative metagenomic analysis studies compositional patterns within and between different environments providing a deep insight into the structure and function of complex microbial communities. It is a fast growing field that requires the development of novel supervised learning techniques for addressing challenges associated with metagenomic data, e.g. sensitivity to the choice of sequence similarity cutoff used to define operational taxonomic units (OTUs), high dimensionality and sparsity of the data and so forth. On the other hand, the natural properties of microbial community data may provide useful information about the structure of the data. For example, similarity between species encoded by a phylogenetic tree captures the relationship between OTUs and may be useful for the analysis of complex microbial datasets where the diversity patterns comprise features at multiple taxonomic levels. Even though some of the challenges have been addressed by learning algorithms in the literature, none of the available methods take advantage of the inherent properties of metagenomic data.'),
(78128, 2014, 'bio', 'Discriminative motif optimization based on perceptron training', 'Patel, Ronak Y|Stormo, Gary D', 'Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.', 'United States|United States', 18, 'Algorithms|Animals|Artificial Intelligence|Base Sequence|Binding Sites|DNA|Drosophila melanogaster|Humans|Protein Binding|Saccharomyces cerevisiae|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt748', '24369152', 'Generating accurate transcription factor (TF) binding site motifs from data generated using the next-generation sequencing, especially ChIP-seq, is challenging. The challenge arises because a typical experiment reports a large number of sequences bound by a TF, and the length of each sequence is relatively long. Most traditional motif finders are slow in handling such enormous amount of data. To overcome this limitation, tools have been developed that compromise accuracy with speed by using heuristic discrete search strategies or limited optimization of identified seed motifs. However, such strategies may not fully use the information in input sequences to generate motifs. Such motifs often form good seeds and can be further improved with appropriate scoring functions and rapid optimization.'),
(78129, 2014, 'bio', 'tasiRNAdb: a database of ta-siRNA regulatory pathways', 'Zhang, Changqing|Li, Guangping|Zhu, Shinong|Zhang, Shuo|Fang, Jinggui', 'College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China, College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China and College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China, College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China and College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China, College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China and College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China, College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China and College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China, College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China and College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.', 'China|China|China|China|China', 0, 'Databases, Nucleic Acid|Gene Expression Regulation|MicroRNAs|Plants|RNA, Small Interfering|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt746', '24371150', 'In plants, many trans-acting small interfering RNA (ta-siRNA) regulatory pathways have been identified as significant components of the gene networks involved in development, metabolism, responses to biotic and abiotic stresses and DNA methylation at the TAS locus. To obtain a more comprehensive understanding on the nature of ta-siRNA regulatory pathways, we developed a freely accessible resource, tasiRNAdb, to serve as a repository for the sequences of ta-siRNA regulatory pathway-related microRNAs, TASs, ta-siRNAs and ta-siRNA targets, and for the cascading relations among them. With 583 pathways from 18 species, tasiRNAdb is the largest resource for known ta-siRNA regulatory pathways currently available. tasiRNAdb also provides a tool named TasExpAnalysis that was developed to map user-submitted small RNA and degradome libraries to a stored/input TAS and to perform sRNA phasing analysis and TAS cleavage analysis.'),
(78130, 2014, 'bio', 'TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data', 'Jorjani, Hadi|Zavolan, Mihaela', 'Computational and Systems Biology, Biozentrum, University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland.|Computational and Systems Biology, Biozentrum, University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland.', 'Switzerland|Switzerland', 18, 'Automation, Laboratory|Base Sequence|Genome, Bacterial|Genomics|Sequence Analysis, RNA|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btt752', '24371151', 'Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually.'),
(78131, 2014, 'bio', 'Improving computational efficiency and tractability of protein design using a piecemeal approach A strategy for parallel and distributed protein design', 'Pitman, Derek J|Schenkelberg, Christian D|Huang, Yao-Ming|Teets, Frank D|DiTursi, Daniel|Bystroff, Christopher', 'Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, Department of Computer Science and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.', 'United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Amino Acids|Computational Biology|Models, Molecular|Protein Conformation|Protein Engineering|Proteins', 'Journal Article', '10.1093/bioinformatics/btt735', '24371152', 'Accuracy in protein design requires a fine-grained rotamer search, multiple backbone conformations, and a detailed energy function, creating a burden in runtime and memory requirements. A design task may be split into manageable pieces in both three-dimensional space and in the rotamer search space to produce small, fast jobs that are easily distributed. However, these jobs must overlap, presenting a problem in resolving conflicting solutions in the overlap regions.'),
(78132, 2014, 'bio', 'SigHunt: horizontal gene transfer finder optimized for eukaryotic genomes', 'Jaron, Kamil S|Moravec, Jixc5x99xc3xad C|Martxc3xadnkovxc3xa1, Natxc3xa1lia', 'Institute of Biostatistics and Analyses, Masaryk University and Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno, Czech Republic.|Institute of Biostatistics and Analyses, Masaryk University and Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno, Czech Republic.|Institute of Biostatistics and Analyses, Masaryk University and Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno, Czech Republic Institute of Biostatistics and Analyses, Masaryk University and Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno, Czech Republic.', '-|-|-', 0, 'Base Composition|Computational Biology|Eukaryota|Gene Transfer, Horizontal|Genomic Islands|Genomics|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt727', '24371153', 'Genomic islands (GIs) are DNA fragments incorporated into a genome through horizontal gene transfer (also called lateral gene transfer), often with functions novel for a given organism. While methods for their detection are well researched in prokaryotes, the complexity of eukaryotic genomes makes direct utilization of these methods unreliable, and so labour-intensive phylogenetic searches are used instead.'),
(78133, 2014, 'bio', 'MSIsensor: microsatellite instability detection using paired tumor-normal sequence data', 'Niu, Beifang|Ye, Kai|Zhang, Qunyuan|Lu, Charles|Xie, Mingchao|McLellan, Michael D|Wendl, Michael C|Ding, Li', 'Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.|Departments of Genetics and Mathematics, The Genome Institute, Department of Genetics, Division of Statistical Genomics, Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 165, 'Automation, Laboratory|Genome, Human|Humans|Microsatellite Instability|Neoplasms|Polymerase Chain Reaction|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt755', '24371154', 'Microsatellite instability (MSI) is an important indicator of larger genome instability and has been linked to many genetic diseases, including Lynch syndrome. MSI status is also an independent prognostic factor for favorable survival in multiple cancer types, such as colorectal and endometrial. It also informs the choice of chemotherapeutic agents. However, the current PCR-electrophoresis-based detection procedure is laborious and time-consuming, often requiring visual inspection to categorize samples. We developed MSIsensor, a C++ program for automatically detecting somatic microsatellite changes. It computes length distributions of microsatellites per site in paired tumor and normal sequence data, subsequently using these to statistically compare observed distributions in both samples. Comprehensive testing indicates MSIsensor is an efficient and effective tool for deriving MSI status from standard tumor-normal paired sequence data.'),
(78134, 2014, 'bio', 'Assimilating genome-scale metabolic reconstructions with modelBorgifier', 'Sauls, John T|Buescher, Joerg M', 'BRAIN Aktiengesellschaft, Microbial Production Technologies, Platform for Quantitative Biology and Sequencing, D-64673 Zwingenberg, Germany.|BRAIN Aktiengesellschaft, Microbial Production Technologies, Platform for Quantitative Biology and Sequencing, D-64673 Zwingenberg, Germany.', 'Germany|Germany', 8, 'Genome|Genomics|Metabolic Networks and Pathways|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btt747', '24371155', 'Genome-scale reconstructions and models, as collections of genomic and metabolic information, provide a useful means to compare organisms. Comparison requires that models are similarly notated to pair shared components.'),
(78135, 2014, 'bio', 'ALEA: a toolbox for allele-specific epigenomics analysis', 'Younesy, Hamid|Möller, Torsten|Heravi-Moussavi, Alireza|Cheng, Jeffrey B|Costello, Joseph F|Lorincz, Matthew C|Karimi, Mohammad M|Jones, Steven J M', 'Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 4S6, Canada, Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada, Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria, Department of Dermatology, University of California San Francisco, San Francisco, California 94143, USA, Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA and Department of Medical Genetics, Life Sciences Institute, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Alleles|Animals|Chromatin Immunoprecipitation|Computational Biology|Epigenomics|High-Throughput Nucleotide Sequencing|Humans|Mice|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt744', '24371156', 'The assessment of expression and epigenomic status using sequencing based methods provides an unprecedented opportunity to identify and correlate allelic differences with epigenomic status. We present ALEA, a computational toolbox for allele-specific epigenomics analysis, which incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. ALEA provides a customizable pipeline of command line tools for allele-specific analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. The pipeline has been validated using human and hybrid mouse ChIP-seq and RNA-seq data.'),
(78136, 2014, 'bio', 'Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data', 'Hu, Xihao|Wong, Thomas K F|Lu, Zhi John|Chan, Ting Fung|Lau, Terrence Chi Kong|Yiu, Siu Ming|Yip, Kevin Y', 'Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Binding Sites|Computational Biology|High-Throughput Nucleotide Sequencing|Nucleic Acid Conformation|Protein Binding|RNA|RNA-Binding Proteins|Saccharomyces cerevisiae|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt757', '24376038', 'High-throughput sequencing has been used to probe RNA structures, by treating RNAs with reagents that preferentially cleave or mark certain nucleotides according to their local structures, followed by sequencing of the resulting fragments. The data produced contain valuable information for studying various RNA properties.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78137, 2014, 'bio', 'HTS navigator: freely accessible cheminformatics software for analyzing high-throughput screening data', 'Fourches, Denis|Sassano, Maria F|Roth, Bryan L|Tropsha, Alexander', 'Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill and Department of Pharmacology and the NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill and Department of Pharmacology and the NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill and Department of Pharmacology and the NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill and Department of Pharmacology and the NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Chemical|Drug Discovery|High-Throughput Screening Assays|Information Storage and Retrieval|Quantitative Structure-Activity Relationship|Small Molecule Libraries|Software', 'Journal Article', '10.1093/bioinformatics/btt718', '24376084', 'We report on the development of the high-throughput screening (HTS) Navigator software to analyze and visualize the results of HTS of chemical libraries. The HTS Navigator processes output files from different plate readers formats, computes the overall HTS matrix, automatically detects hits and has different types of baseline navigation and correction features. The software incorporates advanced cheminformatics capabilities such as chemical structure storage and visualization, fast similarity search and chemical neighborhood analysis for retrieved hits. The software is freely available for academic laboratories.'),
(78138, 2014, 'bio', 'SNPdryad: predicting deleterious non-synonymous human SNPs using only orthologous protein sequences', 'Wong, Ka-Chun|Zhang, Zhaolei', 'Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8.|Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8.', 'Canada|Canada', 44, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Chromosome Mapping|Computational Biology|Humans|Polymorphism, Single Nucleotide|Proteome|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btt769', '24389653', 'The recent advances in genome sequencing have revealed an abundance of non-synonymous polymorphisms among human individuals, subsequently, it is of immense interest and importance to predict whether such substitutions are functional neutral or have deleterious effects. The accuracy of such prediction algorithms depends on the quality of the multiple-sequence alignment, which is used to infer how an amino acid substitution is tolerated at a given position. Because of the scarcity of orthologous protein sequences in the past, the existing prediction algorithms all include sequences of protein paralogs in the alignment, which can dilute the conservation signal and affect prediction accuracy. However, we believe that, with the sequencing of a large number of mammalian genomes, it is now feasible to include only protein orthologs in the alignment and improve the prediction performance.'),
(78139, 2014, 'bio', 'Optimization strategies for fast detection of positive selection on phylogenetic trees', 'Valle, Mario|Schabauer, Hannes|Pacher, Christoph|Stockinger, Heinz|Stamatakis, Alexandros|Robinson-Rechavi, Marc|Salamin, Nicolas', 'Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.|Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany Swiss National Supercomputing Centre, Scientific Computing Group, Lugano, Switzerland, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria, Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland, Heidelberg Institute for Theoretical Studies, Scientific Computing Group and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Algorithms|Codon|Computational Biology|Evolution, Molecular|Likelihood Functions|Phylogeny|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btt760', '24389654', 'The detection of positive selection is widely used to study gene and genome evolution, but its application remains limited by the high computational cost of existing implementations. We present a series of computational optimizations for more efficient estimation of the likelihood function on large-scale phylogenetic problems. We illustrate our approach using the branch-site model of codon evolution.'),
(78140, 2014, 'bio', 'Inference with viral quasispecies diversity indices: clonal and NGS approaches', 'Gregori, Josep|Salicrú, Miquel|Domingo, Esteban|Sanchez, Alex|Esteban, Juan I|Rodríguez-Frías, Francisco|Quer, Josep', 'Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.|Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain Liver Unit, Internal Medicine Lab Malalties Hepàtiques, Vall dHebron Institut Recerca (VHIR-HUVH), 08035 Barcelona, Spain, Roche Diagnostics SL, 08174, Sant Cugat del Vallès, Spain, Statistics Department, Biology Faculty, Barcelona University, 08028, Barcelona, Spain, CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd) del Instituto de Salud Carlos III, 28029 Madrid, Spain, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco, 28049, Madrid, Spain, Bioinformatics and Statistics Unit, Vall dHebron Institut Recerca (VHIR-HUVH), 08035, Barcelona, Spain, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain and Biochemistry Unit. Virology Unit/Microbiology Department, HUVH, 08035 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Computational Biology|Genetic Variation|Hepacivirus|High-Throughput Nucleotide Sequencing|RNA, Viral|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt768', '24389655', 'Given the inherent dynamics of a viral quasispecies, we are often interested in the comparison of diversity indices of sequential samples of a patient, or in the comparison of diversity indices of virus in groups of patients in a treated versus control design. It is then important to make sure that the diversity measures from each sample may be compared with no bias and within a consistent statistical framework. In the present report, we review some indices often used as measures for viral quasispecies complexity and provide means for statistical inference, applying procedures taken from the ecology field. In particular, we examine the Shannon entropy and the mutation frequency, and we discuss the appropriateness of different normalization methods of the Shannon entropy found in the literature. By taking amplicons ultra-deep pyrosequencing (UDPS) raw data as a surrogate of a real hepatitis C virus viral population, we study through in-silico sampling the statistical properties of these indices under two methods of viral quasispecies sampling, classical cloning followed by Sanger sequencing (CCSS) and next-generation sequencing (NGS) such as UDPS. We propose solutions specific to each of the two sampling methods-CCSS and NGS-to guarantee statistically conforming conclusions as free of bias as possible.'),
(78141, 2014, 'bio', 'Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads', 'Schröder, Jan|Hsu, Arthur|Boyle, Samantha E|Macintyre, Geoff|Cmero, Marek|Tothill, Richard W|Johnstone, Ricky W|Shackleton, Mark|Papenfuss, Anthony T', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Department of Medical Biology, University of Melbourne, Victoria 3010, Melanoma Research Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Department of Pathology, The University of Melbourne, Victoria 3010, NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Department of Computing and Information Systems, University of Melbourne, Victoria 3010, Cancer Therapeutics Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010 and Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt767', '24389656', 'Methods for detecting somatic genome rearrangements in tumours using next-generation sequencing are vital in cancer genomics. Available algorithms use one or more sources of evidence, such as read depth, paired-end reads or split reads to predict structural variants. However, the problem remains challenging due to the significant computational burden and high false-positive or false-negative rates.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78142, 2014, 'bio', 'Bias from removing read duplication in ultra-deep sequencing experiments', 'Zhou, Wanding|Chen, Tenghui|Zhao, Hao|Eterovic, Agda Karina|Meric-Bernstam, Funda|Mills, Gordon B|Chen, Ken', 'Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Systems Biology, Institute of Personalized Cancer Therapy and Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston TX 77030, USA.', 'United States|United States|United States|United States|United States|United States|United States', 20, 'Alleles|Computational Biology|Computer Simulation|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Models, Theoretical|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt771', '24389657', 'Identifying subclonal mutations and their implications requires accurate estimation of mutant allele fractions from possibly duplicated sequencing reads. Removing duplicate reads assumes that polymerase chain reaction amplification from library constructions is the primary source. The alternative-sampling coincidence from DNA fragmentation-has not been systematically investigated.'),
(78143, 2014, 'bio', 'CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data', 'Guan, Daogang|Shao, Jiaofang|Deng, Youping|Wang, Panwen|Zhao, Zhongying|Liang, Yan|Wang, Junwen|Yan, Bin', 'Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.', 'China|China|China|China|China|China|China|China', 18, 'Bayes Theorem|Chromatin Immunoprecipitation|Computational Biology|Computer Communication Networks|Epigenesis, Genetic|Gene Expression|Gene Regulatory Networks|Genomics|Internet|Oligonucleotide Array Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt761', '24389658', 'ChIP-seq technology provides an accurate characterization of transcription or epigenetic factors binding on genomic sequences. With integration of such ChIP-based and other high-throughput information, it would be dedicated to dissecting cross-interactions among multilevel regulators, genes and biological functions. Here, we devised an integrative web server CMGRN (constructing multilevel gene regulatory networks), to unravel hierarchical interactive networks at different regulatory levels. The newly developed method used the Bayesian network modeling to infer causal interrelationships among transcription factors or epigenetic modifications by using ChIP-seq data. Moreover, it used Bayesian hierarchical model with Gibbs sampling to incorporate binding signals of these regulators and gene expression profile together for reconstructing gene regulatory networks. The example applications indicate that CMGRN provides an effective web-based framework that is able to integrate heterogeneous high-throughput data and to reveal hierarchical regulome and the associated gene expression programs.'),
(78144, 2014, 'bio', 'DIYABC v20: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data', 'Cornuet, Jean-Marie|Pudlo, Pierre|Veyssier, Julien|Dehne-Garcia, Alexandre|Gautier, Mathieu|Leblois, Raphaël|Marin, Jean-Michel|Estoup, Arnaud', 'Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.|Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France Inra, UMR1062 cbgp, Montpellier, France, Université Montpellier 2, UMR CNRS 5149, I3M, Montpellier, France, Institut de Biologie Computationnelle (IBC), 34095 Montpellier, France and CNRS-UM2, Institut de Biologie Computationnelle, LIRMM, Montpellier, France.', 'France|France|France|France|France|France|France|France', 0, 'Bayes Theorem|Computational Biology|Genetics, Population|Humans|Microsatellite Repeats|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt763', '24389659', 'DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X.'),
(78145, 2014, 'bio', 'Estimation of fluorescence-tagged RNA numbers from spot intensities', 'Häkkinen, Antti|Kandhavelu, Meenakshisundaram|Garasto, Stefania|Ribeiro, Andre S', 'Department of Signal Processing, Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Tampere University of Technology, Tampere, Finland.|Department of Signal Processing, Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Tampere University of Technology, Tampere, Finland.|Department of Signal Processing, Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Tampere University of Technology, Tampere, Finland.|Department of Signal Processing, Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Tampere University of Technology, Tampere, Finland.', 'Finland|Finland|Finland|Finland', 15, 'Escherichia coli|Fluorescence|Microscopy, Fluorescence|RNA, Bacterial', 'Journal Article', '10.1093/bioinformatics/btt766', '24389660', 'Present research on gene expression using live cell imaging and fluorescent proteins or tagged RNA requires accurate automated methods of quantification of these molecules from the images. Here, we propose a novel automated method for classifying pixel intensities of fluorescent spots to RNA numbers.'),
(78146, 2014, 'bio', 'AbsCN-seq: a statistical method to estimate tumor purity, ploidy and absolute copy numbers from next-generation sequencing data', 'Bao, Lei|Pu, Minya|Messer, Karen', 'Division of Biostatistics, Moores Cancer Center, University of California-San Diego, La Jolla, CA 92093, USA.|Division of Biostatistics, Moores Cancer Center, University of California-San Diego, La Jolla, CA 92093, USA.|Division of Biostatistics, Moores Cancer Center, University of California-San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States', 30, 'Algorithms|Cell Line, Tumor|Computational Biology|DNA Copy Number Variations|Exome|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Ploidies|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt759', '24389661', 'Detection and quantification of the absolute DNA copy number alterations in tumor cells is challenging because the DNA specimen is extracted from a mixture of tumor and normal stromal cells. Estimates of tumor purity and ploidy are necessary to correctly infer copy number, and ploidy may itself be a prognostic factor in cancer progression. As deep sequencing of the exome or genome has become routine for characterization of tumor samples, in this work, we aim to develop a simple and robust algorithm to infer purity, ploidy and absolute copy numbers in whole numbers for tumor cells from sequencing data.'),
(78147, 2014, 'bio', 'CasOT: a genome-wide Cas9/gRNA off-target searching tool', 'Xiao, An|Cheng, Zhenchao|Kong, Lei|Zhu, Zuoyan|Lin, Shuo|Gao, Ge|Zhang, Bo', 'Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, College of Life Sciences, Peking University, Beijing 100871, China and Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.', 'China|China|China|China|China|China|China', 0, 'CRISPR-Cas Systems|Computational Biology|Gene Targeting|Humans|RNA, Guide|Software', 'Journal Article', '10.1093/bioinformatics/btt764', '24389662', 'The CRISPR/Cas or Cas9/guide RNA system is a newly developed, easily engineered and highly effective tool for gene targeting, it has considerable off-target effects in cultured human cells and in several organisms. However, the Cas9/guide RNA target site is too short for existing alignment tools to exhaustively and effectively identify potential off-target sites. CasOT is a local tool designed to find potential off-target sites in any given genome or user-provided sequence, with user-specified types of protospacer adjacent motif, and number of mismatches allowed in the seed and non-seed regions.'),
(78148, 2014, 'bio', 'CGAT: computational genomics analysis toolkit', 'Sims, David|Ilott, Nicholas E|Sansom, Stephen N|Sudbery, Ian M|Johnson, Jethro S|Fawcett, Katherine A|Berlanga-Taylor, Antonio J|Luna-Valero, Sebastian|Ponting, Chris P|Heger, Andreas', 'CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.|CGAT, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Parks Road, Oxford OX1 3PT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 27, 'Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btt756', '24395753', 'Computational genomics seeks to draw biological inferences from genomic datasets, often by integrating and contextualizing next-generation sequencing data. CGAT provides an extensive suite of tools designed to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy.'),
(78149, 2014, 'bio', 'QCGWAS: A flexible R package for automated quality control of genome-wide association results', 'van der Most, Peter J|Vaez, Ahmad|Prins, Bram P|Munoz, M Loretto|Snieder, Harold|Alizadeh, Behrooz Z|Nolte, Ilja M', 'Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.|Department of Epidemiology, University of Groningen, University Medical Center Groningen, P.O. box 30.001, 9700 RB Groningen, The Netherlands and Cardiogenetics Lab, Human Genetics Research Centre, St. Georges Hospital Medical School, London SW17 0RE, UK.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Genome-Wide Association Study|Humans|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btt745', '24395754', 'QCGWAS is an R package that automates the quality control of genome-wide association result files. Its main purpose is to facilitate the quality control of a large number of such files before meta-analysis. Alternatively, it can be used by individual cohorts to check their own result files. QCGWAS is flexible and has a wide range of options, allowing rapid generation of high-quality input files for meta-analysis of genome-wide association studies.'),
(78150, 2014, 'bio', 'FamAnn: an automated variant annotation pipeline to facilitate target discovery for family-based sequencing studies', 'Yao, Jianchao|Zhang, Kelvin Xi|Kramer, Melissa|Pellegrini, Matteo|McCombie, W Richard', 'Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA, Department of Biological Chemistry, Howard Hughes Medical Institute, University of California, Los Angeles, CA 90095, USA and Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA, Department of Biological Chemistry, Howard Hughes Medical Institute, University of California, Los Angeles, CA 90095, USA and Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA, Department of Biological Chemistry, Howard Hughes Medical Institute, University of California, Los Angeles, CA 90095, USA and Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA, Department of Biological Chemistry, Howard Hughes Medical Institute, University of California, Los Angeles, CA 90095, USA and Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, CA 90095, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA, Department of Biological Chemistry, Howard Hughes Medical Institute, University of California, Los Angeles, CA 90095, USA and Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Exome|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btt749', '24395755', 'FamAnn is an automated variant annotation pipeline designed for facilitating target discovery for family-based sequencing studies. It can apply a different inheritance pattern or a de novo mutations discovery model to each family and select single nucleotide variants and small insertions and deletions segregating in each family or shared by multiple families. It also provides a variety of variant annotations and retains and annotates all transcripts hit by a single variant. Excel-compatible outputs including all annotated variants segregating in each family or shared by multiple families will be provided for users to prioritize variants based on their customized thresholds. A list of genes that harbor the segregating variants will be provided as well for possible pathway/network analyses. FamAnn uses the de facto community standard Variant Call Format as the input format and can be applied to whole exome, genome or targeted resequencing data.'),
(78151, 2014, 'bio', 'D-Tailor: automated analysis and design of DNA sequences', 'Guimaraes, Joao C|Rocha, Miguel|Arkin, Adam P|Cambray, Guillaume', 'Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.|Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.|Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.|Department of Bioengineering, California Institute for Quantitative Biosciences, University of California, Berkeley, CA, 94720, USA, Computer Science and Technology Center, School of Engineering, University of Minho, Campus de Gualtar, Braga, Portugal and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.', 'Portugal|Portugal|Portugal|Portugal', 15, 'Computational Biology|DNA|Escherichia coli|Monte Carlo Method|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt742', '24398007', 'Current advances in DNA synthesis, cloning and sequencing technologies afford high-throughput implementation of artificial sequences into living cells. However, flexible computational tools for multi-objective sequence design are lacking, limiting the potential of these technologies.'),
(78152, 2014, 'bio', 'Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures', 'Cebamanos, L|Gray, A|Stewart, I|Tenesa, A', 'EPCC and The Roslin Institute, The University of Edinburgh, Edinburgh, UK.|EPCC and The Roslin Institute, The University of Edinburgh, Edinburgh, UK.|EPCC and The Roslin Institute, The University of Edinburgh, Edinburgh, UK.|EPCC and The Roslin Institute, The University of Edinburgh, Edinburgh, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Computational Biology|Likelihood Functions|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt754', '24403537', 'Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we present the REgional heritability advanced complex trait analysis software. Adapted from advanced complex trait analysis (and, in turn, genome-wide complex trait analysis), it is tailored to exploit the parallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, from workstations to supercomputers.'),
(78153, 2014, 'bio', 'Allerdictor: fast allergen prediction using text classification techniques', 'Dang, Ha X|Lawrence, Christopher B', 'Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA.', 'United States|United States', 30, 'Allergens|Amino Acid Sequence|Computational Biology|Databases, Protein|Humans|Proteins|Sequence Alignment|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btu004', '24403538', 'Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years. Although these tools have achieved certain levels of specificity, when applied to large-scale allergen discovery (e.g. at a whole-genome scale), they still yield many false positives and thus low precision (even at low recall) due to the extreme skewness of the data (allergens are rare). Moreover, the most accurate tools are relatively slow because they use protein sequence alignment to build feature vectors for allergen classifiers. Additionally, only web server implementations of the current allergen prediction tools are publicly available and are without the capability of large batch submission. These weaknesses make large-scale allergen discovery ineffective and inefficient in the public domain.'),
(78154, 2014, 'bio', 'Correlations between predicted protein disorder and post-translational modifications in plants', 'Kurotani, Atsushi|Tokmakov, Alexander A|Kuroda, Yutaka|Fukami, Yasuo|Shinozaki, Kazuo|Sakurai, Tetsuya', 'RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.|RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.|RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.|RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.|RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.|RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan, Department of Biotechnology and Life Science, Faculty of Technology, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan and Research Center for Environmental Genomics, Kobe University, 1-1 Rokko dai, Nada, Kobe 657-8501, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 31, 'Acetylation|Glycosylation|Methylation|Phosphorylation|Plant Proteins|Plants|Protein Processing, Post-Translational|Proteome', 'Journal Article', '10.1093/bioinformatics/btt762', '24403539', 'Protein structural research in plants lags behind that in animal and bacterial species. This lag concerns both the structural analysis of individual proteins and the proteome-wide characterization of structure-related properties. Until now, no systematic study concerning the relationships between protein disorder and multiple post-translational modifications (PTMs) in plants has been presented.'),
(78155, 2014, 'bio', 'Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models', 'Klein, Hans-Ulrich|Schäfer, Martin|Porse, Bo T|Hasemann, Marie S|Ickstadt, Katja|Dugas, Martin', 'Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.|Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.|Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.|Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.|Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.|Institute of Medical Informatics, University of Münster, D-48149 Münster, Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany, The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, Biotech Research and Innovation Center (BRIC), Danish Stem Cell Centre (DanStem), Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark and Faculty of Statistics, TU Dortmund University, D-44221 Dortmund, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Bayes Theorem|Chromatin Immunoprecipitation|Computational Biology|Epigenesis, Genetic|Histones|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btu003', '24403540', 'Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification.'),
(78156, 2014, 'bio', 'Prediction of therapeutic microRNA based on the human metabolic network', 'Wu, Ming|Chan, Christina', 'Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 8, 'Carcinoma, Hepatocellular|Cell Proliferation|Disease Progression|Humans|Liver Neoplasms|Metabolic Networks and Pathways|MicroRNAs', 'Journal Article', '10.1093/bioinformatics/btt751', '24403541', 'MicroRNA (miRNA) expression has been found to be deregulated in human cancer, contributing, in part, to the interest of the research community in using miRNAs as alternative therapeutic targets. Although miRNAs could be potential targets, identifying which miRNAs to target for a particular type of cancer has been difficult due to the limited knowledge on their regulatory roles in cancer. We address this challenge by integrating miRNA-target prediction, metabolic modeling and context-specific gene expression data to predict therapeutic miRNAs that could reduce the growth of cancer.'),
(78157, 2014, 'bio', 'Characterizing cancer subtypes as attractors of Hopfield networks', 'Maetschke, Stefan R|Ragan, Mark A', 'The University of Queensland, Institute for Molecular Bioscience, Brisbane, QLD 4072, Australia and Australian Research Council Centre of Excellence in Bioinformatics, Australia.|The University of Queensland, Institute for Molecular Bioscience, Brisbane, QLD 4072, Australia and Australian Research Council Centre of Excellence in Bioinformatics, Australia.', 'Australia|Australia', 14, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Kinetics|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt773', '24407221', 'Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78158, 2014, 'bio', 'PeaKDEck: a kernel density estimator-based peak calling program for DNaseI-seq data', 'McCarthy, Michael T|OCallaghan, Christopher A', 'Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.', 'United Kingdom|United Kingdom', 17, 'Algorithms|Genome|High-Throughput Nucleotide Sequencing|Probability|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1093/bioinformatics/btt774', '24407222', 'Hypersensitivity to DNaseI digestion is a hallmark of open chromatin, and DNaseI-seq allows the genome-wide identification of regions of open chromatin. Interpreting these data is challenging, largely because of inherent variation in signal-to-noise ratio between datasets. We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and using kernel density estimation to generate a dataset-specific probability distribution of random background signal. PeaKDEck uses this probability distribution to select an appropriate read density threshold for peak calling in each dataset. We benchmark PeaKDEck using published ENCODE DNaseI-seq data and other peak calling programs, and demonstrate superior performance in low signal-to-noise ratio datasets.'),
(78159, 2014, 'bio', 'Evolutionary solution for the RNA design problem', 'Esmaili-Taheri, Ali|Ganjtabesh, Mohammad|Mohammad-Noori, Morteza', 'Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P. O. Box: 14155-6455, Tehran, Iran, Laboratoire dInformatique (LIX), Ecole Polytechnique, 91128 Palaiseau CEDEX, France and School of Biological Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box: 19395-5746 Tehran, Iran.|Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P. O. Box: 14155-6455, Tehran, Iran, Laboratoire dInformatique (LIX), Ecole Polytechnique, 91128 Palaiseau CEDEX, France and School of Biological Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box: 19395-5746 Tehran, Iran.|Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P. O. Box: 14155-6455, Tehran, Iran, Laboratoire dInformatique (LIX), Ecole Polytechnique, 91128 Palaiseau CEDEX, France and School of Biological Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box: 19395-5746 Tehran, Iran.', 'France|France|France', 23, 'Algorithms|Nucleic Acid Conformation|RNA|RNA Folding|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btu001', '24407223', 'RNAs play fundamental roles in cellular processes. The function of an RNA is highly dependent on its 3D conformation, which is referred to as the RNA tertiary structure. Because the prediction or experimental determination of these structures is difficult, so many works focus on the problems associated with the RNA secondary structure. Here, we consider the RNA inverse folding problem, in which an RNA secondary structure is given as a target structure and the goal is to design an RNA sequence that folds into the target structure. In this article, we introduce a new evolutionary algorithm for the RNA inverse folding problem. Our algorithm, entitled Evolutionary RNA Design, generates a sequence whose minimum free energy structure is the same as the target structure.'),
(78160, 2014, 'bio', 'MAGIC: access portal to a cross-platform gene expression compendium for maize', 'Fu, Qiang|Fierro, Ana Carolina|Meysman, Pieter|Sanchez-Rodriguez, Aminael|Vandepoele, Klaas|Marchal, Kathleen|Engelen, Kristof', 'Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.|Center of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001, Leuven, Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, Ghent University, B-9052 Gent, Belgium, Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent University, B-9052 Gent, Belgium and Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele allAdige, Trento, Italy.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 2, 'Gene Expression|Gene Expression Regulation, Plant|Internet|Molecular Sequence Annotation|Software|Zea mays', 'Journal Article', '10.1093/bioinformatics/btt739', '24407224', 'To facilitate the exploration of publicly available Zea mays expression data, we constructed a maize expression compendium, making use of an integration methodology and a consistent probe to gene mapping based on the 5b.60 sequence release of Z. mays. The compendium is made available through a web portal MAGIC that hosts a variety of analysis tools to easily browse and analyze the data. Our compendium is different from previous initiatives in combining expression values across different experiments by providing a consistent gene annotation across different platforms.'),
(78161, 2014, 'bio', 'Characterization of the SOS meta-regulon in the human gut microbiome', 'Cornish, Joseph P|Sanchez-Alberola, Neus|ONeill, Patrick K|OKeefe, Ronald|Gheba, Jameel|Erill, Ivan', 'Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA.', 'United States|United States|United States|United States|United States|United States', 5, 'Bacterial Proteins|Bacteriophages|Base Sequence|Binding Sites|Gastrointestinal Tract|Humans|Metagenome|Metagenomics|Microbiota|Regulon|SOS Response (Genetics)|Serine Endopeptidases', 'Journal Article', '10.1093/bioinformatics/btt753', '24407225', 'Data from metagenomics projects remain largely untapped for the analysis of transcriptional regulatory networks. Here, we provide proof-of-concept that metagenomic data can be effectively leveraged to analyze regulatory networks by characterizing the SOS meta-regulon in the human gut microbiome.'),
(78162, 2014, 'bio', 'Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data', 'ONeill, Kieran|Jalali, Adrin|Aghaeepour, Nima|Hoos, Holger|Brinkman, Ryan R', 'Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.|Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.|Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.|Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.|Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.', 'Canada|Canada|Canada|Canada|Canada', 24, 'Antigens, CD|Biomarkers|Flow Cytometry|Software', 'Journal Article', '10.1093/bioinformatics/btt770', '24407226', 'We present a significantly improved version of the flowType and RchyOptimyx BioConductor-based pipeline that is both 14 times faster and can accommodate multiple levels of biomarker expression for up to 96 markers. With these improvements, the pipeline is positioned to be an integral part of data analysis for high-throughput experiments on high-dimensional single-cell assay platforms, including flow cytometry, mass cytometry and single-cell RT-qPCR.'),
(78163, 2014, 'bio', 'MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data', 'Jensen, Paul A|Papin, Jason A', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States', 23, 'Automation, Laboratory|Escherichia coli|Genome|High-Throughput Nucleotide Sequencing|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btt758', '24413519', 'Metabolic reaction maps allow visualization of genome-scale models and high-throughput data in a format familiar to many biologists. However, creating a map of a large metabolic model is a difficult and time-consuming process. MetDraw fully automates the map-drawing process for metabolic models containing hundreds to thousands of reactions. MetDraw can also overlay high-throughput omics data directly on the generated maps.'),
(78164, 2014, 'bio', 'BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution', 'Ye, Chengxi|Hsiao, Chiaowen|Corrada Bravo, Héctor', 'Department of Computer Science, Center for Bioinformatics and Computational Biology and Applied Mathematics and Scientific Computing, University of Maryland, College Park, USA.|Department of Computer Science, Center for Bioinformatics and Computational Biology and Applied Mathematics and Scientific Computing, University of Maryland, College Park, USA.|Department of Computer Science, Center for Bioinformatics and Computational Biology and Applied Mathematics and Scientific Computing, University of Maryland, College Park, USA.', 'United States|United States|United States', 6, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Probability|Reproducibility of Results|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btu010', '24413520', 'Base-calling of sequencing data produced by high-throughput sequencing platforms is a fundamental process in current bioinformatics analysis. However, existing third-party probabilistic or machine-learning methods that significantly improve the accuracy of base-calls on these platforms are impractical for production use due to their computational inefficiency.'),
(78165, 2014, 'bio', 'Identification of short terminal motifs enriched by antibodies using peptide mass fingerprinting', 'Planatscher, Hannes|Weiß, Frederik|Eisen, David|van den Berg, B H J|Zell, Andreas|Joos, Thomas|Poetz, Oliver', 'Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.|Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Markwiesenstr. 55, D-72770 Reutlingen and Center for Bioinformatics, University of Tübingen, Sand 1, D-72076 Tuebingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 3, 'Algorithms|Amino Acid Sequence|Antibodies|Databases, Protein|Epitopes|Mass Spectrometry|Peptide Mapping|Proteomics|Sequence Analysis, Protein|Software Design', 'Journal Article', '10.1093/bioinformatics/btu009', '24413521', 'Mass spectrometry-based protein profiling has become a key technology in biomedical research and biomarker discovery. Sample preparation strategies that reduce the complexity of tryptic digests by immunoaffinity substantially increase throughput and sensitivity in proteomic mass spectrometry. The scarce availability of peptide-specific capture antibodies limits these approaches. Recently antibodies directed against short terminal motifs were found to enrich subsets of peptides with identical terminal sequences. This approach holds the promise of a significant gain in efficiency. TXP (Triple X Proteomics) and context-independent motif specific/global proteome survey binders are variants of this concept. Principally the binding motifs of such antibodies have to be elucidated after generating these antibodies. This entails a substantial effort in the lab, as it requires synthetic peptide libraries and numerous mass spectrometry experiments.'),
(78166, 2014, 'bio', 'Ontodog: a web-based ontology community view generation tool', 'Zheng, Jie|Xiang, Zuoshuang|Stoeckert, Christian J|He, Yongqun', 'Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 and Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 and Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 and Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 and Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States', 14, 'Biological Ontologies|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btu008', '24413522', 'Biomedical ontologies are often very large and complex. Only a subset of the ontology may be needed for a specified application or community. For ontology end users, it is desirable to have community-based labels rather than the labels generated by ontology developers. Ontodog is a web-based system that can generate an ontology subset based on Excel input, and support generation of an ontology community view, which is defined as the whole or a subset of the source ontology with user-specified annotations including user-preferred labels. Ontodog allows users to easily generate community views with minimal ontology knowledge and no programming skills or installation required. Currently >100 ontologies including all OBO Foundry ontologies are available to generate the views based on user needs. We demonstrate the application of Ontodog for the generation of community views using the Ontology for Biomedical Investigations as the source ontology.'),
(78167, 2014, 'bio', 'myFX: a turn-key software for laboratory desktops to analyze spatial patterns of gene expression in Drosophila embryos', 'Montiel, Ivan|Konikoff, Charlotte|Braun, Bremen|Packard, Mary|Gramates, Sian L|Sun, Qian|Ye, Jieping|Kumar, Sudhir', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, USA, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA, School of Computing, Informatics, and Decision Systems Engineering, ASU, Tempe, AZ 85287, USA, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Ara', 0, 'Animals|Drosophila melanogaster|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Developmental|Software', 'Journal Article', '10.1093/bioinformatics/btu007', '24413523', 'Spatial patterns of gene expression are of key importance in understanding developmental networks. Using in situ hybridization, many laboratories are generating images to describe these spatial patterns and to test biological hypotheses. To facilitate such analyses, we have developed biologist-centric software (myFX) that contains computational methods to automatically process and analyze images depicting embryonic gene expression in the fruit fly Drosophila melanogaster. It facilitates creating digital descriptions of spatial patterns in images and enables measurements of pattern similarity and visualization of expression across genes and developmental stages. myFX interacts directly with the online FlyExpress database, which allows users to search thousands of existing patterns to find co-expressed genes by image comparison.'),
(78168, 2014, 'bio', 'Prediction of nuclear export signals using weighted regular expressions (Wregex)', 'Prieto, Gorka|Fullaondo, Asier|Rodriguez, Jose A', 'Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda. Urquijo s/n Bilbao, 48013 and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n Leioa, 48940, Spain.|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda. Urquijo s/n Bilbao, 48013 and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n Leioa, 48940, Spain.|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda. Urquijo s/n Bilbao, 48013 and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n Leioa, 48940, Spain.', 'Spain|Spain|Spain', 27, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Nuclear Export Signals|Position-Specific Scoring Matrices|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btu016', '24413524', 'Leucine-rich nuclear export signals (NESs) are short amino acid motifs that mediate binding of cargo proteins to the nuclear export receptor CRM1, and thus contribute to regulate the localization and function of many cellular proteins. Computational prediction of NES motifs is of great interest, but remains a significant challenge.'),
(78169, 2014, 'bio', 'CGARS: cancer genome analysis by rank sums', 'Lu, Xin|Thomas, Roman K|Peifer, Martin', 'Department of Translational Genomics, Department of Pathology and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.|Department of Translational Genomics, Department of Pathology and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.|Department of Translational Genomics, Department of Pathology and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.', 'Germany|Germany|Germany', 4, 'Carcinoma, Non-Small-Cell Lung|Carcinoma, Squamous Cell|DNA Copy Number Variations|Genome, Human|Genomics|HLA-A Antigens|Humans|Lung Neoplasms', 'Journal Article', '10.1093/bioinformatics/btu011', '24413525', 'Cancer genomes are characterized by the accumulation of point mutations and structural alterations such as copy-number alterations and genomic rearrangements. Among structural changes, systematic analyses of copy-number alterations have provided deeper insight into the architecture of cancer genomes and had led to new potential treatment opportunities. During the course of cancer genome evolution, selection mechanisms are leading to a non-random pattern of mutational events contributing to fitness benefits of the cancer cells. We therefore developed a new method to dissect random from non-random patterns in copy-number data and thereby to assess significantly enriched somatic copy-number aberrations across a set of tumor specimens or cell lines. In contrast to existing approaches, the method is invariant to any strictly monotonous transformation of the input data which results to an insensitivity of differences in tumor purity, array saturation effects and copy-number baseline levels.'),
(78170, 2014, 'bio', 'PARS: a web server for the prediction of Protein Allosteric and Regulatory Sites', 'Panjkovich, Alejandro|Daura, Xavier', 'Institute of Biotechnology and Biomedicine (IBB), Universitat Autònoma de Barcelona (UAB), Bellaterra 08193, Spain and Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain.|Institute of Biotechnology and Biomedicine (IBB), Universitat Autònoma de Barcelona (UAB), Bellaterra 08193, Spain and Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain.', 'Spain|Spain', 52, 'Allosteric Regulation|Allosteric Site|Internet|Ligands|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btu002', '24413526', 'The regulation of protein activity is a key aspect of life at the molecular level. Unveiling its details is thus crucial to understanding signalling and metabolic pathways. The most common and powerful mechanism of protein-function regulation is allostery, which has been increasingly calling the attention of medicinal chemists due to its potential for the discovery of novel therapeutics. In this context, PARS is a simple and fast method that queries protein dynamics and structural conservation to identify pockets on a protein structure that may exert a regulatory effect on the binding of a small-molecule ligand.'),
(78171, 2014, 'bio', 'Efficient haplotype matching and storage using the positional Burrows-Wheeler transform (PBWT)', 'Durbin, Richard', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.', 'United Kingdom', 126, 'Algorithms|Alleles|Genomics|Genotype|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu014', '24413527', 'Over the last few years, methods based on suffix arrays using the Burrows-Wheeler Transform have been widely used for DNA sequence read matching and assembly. These provide very fast search algorithms, linear in the search pattern size, on a highly compressible representation of the dataset being searched. Meanwhile, algorithmic development for genotype data has concentrated on statistical methods for phasing and imputation, based on probabilistic matching to hidden Markov model representations of the reference data, which while powerful are much less computationally efficient. Here a theory of haplotype matching using suffix array ideas is developed, which should scale too much larger datasets than those currently handled by genotype algorithms.'),
(78172, 2014, 'bio', 'MEGA-MD: molecular evolutionary genetics analysis software with mutational diagnosis of amino acid variation', 'Stecher, Glen|Liu, Li|Sanderford, Maxwell|Peterson, Daniel|Tamura, Koichiro|Kumar, Sudhir', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287, Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University (TMU), Hachioji, Tokyo, Japan, Department of Biological Sciences, TMU, Tokyo, Japan, School of Life Sciences, ASU, Tempe, AZ 85287, USA and Center for Excellence in Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.', 'Japan|Japan|Japan|Japan|Japan|Japan', 23, 'Amino Acids|Animals|Evolution, Molecular|Exome|High-Throughput Nucleotide Sequencing|Humans|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btu018', '24413669', 'Computational diagnosis of amino acid variants in the human exome is the first step in assessing the disruptive impacts of non-synonymous single nucleotide variants (nsSNVs) on human health and disease. The Molecular Evolutionary Genetics Analysis software with mutational diagnosis (MEGA-MD) is a suite of tools developed to forecast the deleteriousness of nsSNVs using multiple methods and to explore nsSNVs in the context of the variability permitted in the long-term evolution of the affected position. In its graphical interface for use on desktops, it enables interactive computational diagnosis and evolutionary exploration of nsSNVs. As a web service, MEGA-MD is suitable for diagnosing variants on an exome scale. The MEGA-MD suite intends to serve the needs for conducting low- and high-throughput analysis of nsSNVs in diverse applications.'),
(78173, 2014, 'bio', 'Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata', 'Gatto, Laurent|Breckels, Lisa M|Wieczorek, Samuel|Burger, Thomas|Lilley, Kathryn S', 'Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.|Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.|Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.|Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.|Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.', 'France|France|France|France|France', 31, 'Algorithms|Cluster Analysis|Mass Spectrometry|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btu013', '24413670', 'Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools.'),
(78174, 2014, 'bio', 'Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis', 'Sreedharan, Vipin T|Schultheiss, Sebastian J|Jean, Géraldine|Kahles, André|Bohnert, Regina|Drewe, Philipp|Mudrakarta, Pramod|Görnitz, Nico|Zeller, Georg|Rätsch, Gunnar', 'Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Machine Learning in Biology Group, Friedrich Miescher Laboratory, Tübingen, Germany, LINA, Combinatorics and Bioinformatics Group, University of Nantes, Nantes, France, Machine Learning/Intelligent Data Analysis Group, Technical University, Berlin, Germany and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 9, 'Base Sequence|Internet|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt731', '24413671', 'We present Oqtans, an open-source workbench for quantitative transcriptome analysis, that is integrated in Galaxy. Its distinguishing features include customizable computational workflows and a modular pipeline architecture that facilitates comparative assessment of tool and data quality. Oqtans integrates an assortment of machine learning-powered tools into Galaxy, which show superior or equal performance to state-of-the-art tools. Implemented tools comprise a complete transcriptome analysis workflow: short-read alignment, transcript identification/quantification and differential expression analysis. Oqtans and Galaxy facilitate persistent storage, data exchange and documentation of intermediate results and analysis workflows. We illustrate how Oqtans aids the interpretation of data from different experiments in easy to understand use cases. Users can easily create their own workflows and extend Oqtans by integrating specific tools. Oqtans is available as (i) a cloud machine image with a demo instance at cloud.oqtans.org, (ii) a public Galaxy instance at galaxy.cbio.mskcc.org, (iii) a git repository containing all installed software (oqtans.org/git), most of which is also available from (iv) the Galaxy Toolshed and (v) a share string to use along with Galaxy CloudMan.'),
(78175, 2014, 'bio', 'The EBI RDF platform: linked open data for the life sciences', 'Jupp, Simon|Malone, James|Bolleman, Jerven|Brandizi, Marco|Davies, Mark|Garcia, Leyla|Gaulton, Anna|Gehant, Sebastien|Laibe, Camille|Redaschi, Nicole|Wimalaratne, Sarala M|Martin, Maria|Le Novère, Nicolas|Parkinson, Helen|Birney, Ewan|Jenkinson, Andrew M', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 133, 'Academies and Institutes|Biomedical Research|Computational Biology|Databases, Genetic|Internet', 'Journal Article', '10.1093/bioinformatics/btt765', '24413672', 'Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78176, 2014, 'bio', 'Evolutionary footprint of coevolving positions in genes', 'Dib, Linda|Silvestro, Daniele|Salamin, Nicolas', 'Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland and Department of Plant and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, 413 19 Gothenburg, Sweden.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland and Department of Plant and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, 413 19 Gothenburg, Sweden.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland and Department of Plant and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, 413 19 Gothenburg, Sweden.', 'Switzerland|Switzerland|Switzerland', 0, 'Base Sequence|Bayes Theorem|Evolution, Molecular|Probability|RNA, Ribosomal, 16S|Ribulose-Bisphosphate Carboxylase|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu012', '24413673', 'The analysis of molecular coevolution provides information on the potential functional and structural implication of positions along DNA sequences, and several methods are available to identify coevolving positions using probabilistic or combinatorial approaches. The specific nucleotide or amino acid profile associated with the coevolution process is, however, not estimated, but only known profiles, such as the Watson-Crick constraint, are usually considered a priori in current measures of coevolution.'),
(78177, 2014, 'bio', 'INCA: a computational platform for isotopically non-stationary metabolic flux analysis', 'Young, Jamey D', 'Department of Chemical and Biomolecular Engineering and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37235-1604, USA.', 'United States', 155, 'Carbon Isotopes|Computer Simulation|Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btu015', '24413674', '13C flux analysis studies have become an essential component of metabolic engineering research. The scope of these studies has gradually expanded to include both isotopically steady-state and transient labeling experiments, the latter of which are uniquely applicable to photosynthetic organisms and slow-to-label mammalian cell cultures. Isotopomer network compartmental analysis (INCA) is the first publicly available software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis. The software provides a framework for comprehensive analysis of metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity.'),
(78178, 2014, 'bio', 'PANOGA: a web server for identification of SNP-targeted pathways from genome-wide association study data', 'Bakir-Gungor, Burcu|Egemen, Ece|Sezerman, Osman Ugur', 'Department of Genetics and Bioinformatics, Faculty of Arts and Sciences, Bahcesehir University, 34353, Besiktas, Istanbul, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039, Kayseri, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey and Department of Biological Sciences and Bioengineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey.|Department of Genetics and Bioinformatics, Faculty of Arts and Sciences, Bahcesehir University, 34353, Besiktas, Istanbul, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039, Kayseri, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey and Department of Biological Sciences and Bioengineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey.|Department of Genetics and Bioinformatics, Faculty of Arts and Sciences, Bahcesehir University, 34353, Besiktas, Istanbul, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Abdullah Gul University, 38039, Kayseri, Turkey, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey and Department of Biological Sciences and Bioengineering, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Tuzla, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 0, 'Genome-Wide Association Study|Humans|Internet|Linkage Disequilibrium|Phenotype|Polymorphism, Single Nucleotide|Proteins', 'Journal Article', '10.1093/bioinformatics/btt743', '24413675', 'Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.'),
(78179, 2014, 'bio', 'PanGP: a tool for quickly analyzing bacterial pan-genome profile', 'Zhao, Yongbing|Jia, Xinmiao|Yang, Junhui|Ling, Yunchao|Zhang, Zhang|Yu, Jun|Wu, Jiayan|Xiao, Jingfa', 'CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China and University of Chinese Academy of Sciences, Beijing 100049, Peoples Republic of China.', 'China|China|China|China|China|China|China|China', 59, 'Algorithms|Bacteria|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Multigene Family|Software', 'Journal Article', '10.1093/bioinformatics/btu017', '24420766', 'Pan-genome analyses have shed light on the dynamics and evolution of bacterial genome from the point of population. The explosive growth of bacterial genome sequence also brought an extremely big challenge to pan-genome profile analysis. We developed a tool, named PanGP, to complete pan-genome profile analysis for large-scale strains efficiently. PanGP has integrated two sampling algorithms, totally random (TR) and distance guide (DG). The DG algorithm drew sample strain combinations on the basis of genome diversity of bacterial population. The performance of these two algorithms have been evaluated on four bacteria populations with strain numbers varying from 30 to 200, and the DG algorithm exhibited overwhelming advantage on accuracy and stability than the TR algorithm.'),
(78180, 2014, 'bio', 'Subclonal variant calling with multiple samples and prior knowledge', 'Gerstung, Moritz|Papaemmanuil, Elli|Campbell, Peter J', 'Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK, Department of Haematology, Addenbrookes Hospital, Cambridge CB2 0QQ, UK and Department of Haematology, University of Cambridge, Cambridge CB22XY, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK, Department of Haematology, Addenbrookes Hospital, Cambridge CB2 0QQ, UK and Department of Haematology, University of Cambridge, Cambridge CB22XY, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK, Department of Haematology, Addenbrookes Hospital, Cambridge CB2 0QQ, UK and Department of Haematology, University of Cambridge, Cambridge CB22XY, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biometry|Genome, Human|Humans|Mutation|Neoplasms|Probability|Prognosis|Reproducibility of Results|Software Design', 'Journal Article', '10.1093/bioinformatics/btt750', '24443148', 'Targeted resequencing of cancer genes in large cohorts of patients is important to understand the biological and clinical consequences of mutations. Cancers are often clonally heterogeneous, and the detection of subclonal mutations is important from a diagnostic point of view, but presents strong statistical challenges.'),
(78181, 2014, 'bio', 'netClass: an R-package for network based, integrative biomarker signature discovery', 'Cun, Yupeng|Fröhlich, Holger', 'Bonn-Aachen International Center for IT (B-IT), University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany.|Bonn-Aachen International Center for IT (B-IT), University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany.', 'Germany|Germany', 0, 'Algorithms|Biomarkers|Gene Regulatory Networks|Humans|MicroRNAs|Software', 'Journal Article', '10.1093/bioinformatics/btu025', '24443376', 'In the past years, there has been a growing interest in methods that incorporate network information into classification algorithms for biomarker signature discovery in personalized medicine. The general hope is that this way the typical low reproducibility of signatures, together with the difficulty to link them to biological knowledge, can be addressed. Complementary to these efforts, there is an increasing interest in integrating different data entities (e.g. gene and miRNA expressions) into comprehensive models. To our knowledge, R-package netClass is the first software that addresses both, network and data integration. Besides several published approaches for network integration, it specifically contains our recently published STSVM method, which allows for additional integration of gene and miRNA expression data into one predictive classifier.'),
(78182, 2014, 'bio', 'GR-Align: fast and flexible alignment of protein 3D structures using graphlet degree similarity', 'Malod-Dognin, Noël|Pržulj, Nataša', 'Department of Computing, Imperial College London, SW7 2AZ, UK.|Department of Computing, Imperial College London, SW7 2AZ, UK.', 'United Kingdom|United Kingdom', 34, 'Algorithms|Carbon|Humans|Protein Conformation|Proteins|Time Factors', 'Journal Article', '10.1093/bioinformatics/btu020', '24443377', 'Protein structure alignment is key for transferring information from well-studied proteins to less studied ones. Structural alignment identifies the most precise mapping of equivalent residues, as structures are more conserved during evolution than sequences. Among the methods for aligning protein structures, maximum Contact Map Overlap (CMO) has received sustained attention during the past decade. Yet, known algorithms exhibit modest performance and are not applicable for large-scale comparison.'),
(78183, 2014, 'bio', 'PhyloJIVE: Integrating biodiversity data with the Tree of Life', 'Jolley-Rogers, Garry|Varghese, Temi|Harvey, Paul|dos Remedios, Nick|Miller, Joseph T', 'Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra ACT 2601, Australia and Atlas of Living Australia, CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia.|Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra ACT 2601, Australia and Atlas of Living Australia, CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia.|Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra ACT 2601, Australia and Atlas of Living Australia, CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia.|Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra ACT 2601, Australia and Atlas of Living Australia, CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia.|Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra ACT 2601, Australia and Atlas of Living Australia, CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Acacia|Algorithms|Biodiversity|Biological Evolution|Internet|Phylogeny|Software Design', 'Journal Article', '10.1093/bioinformatics/btu024', '24443378', 'Rich collections of biodiversity information such as spatial distributions, species descriptions and trait data are now synthesized in publicly available online sources such as GBIF. Also phylogenetic knowledge now provides a sound understanding of the origin of organisms and their place in the tree of life. We demonstrate with PhyloJIVE that any phylogenetic tree can be linked to online biodiversity data in the browser. This evolutionary view of biodiversity data is demonstrated in a case study that suggests that this approach may be useful to scientists and non-experts users.'),
(78184, 2014, 'bio', 'estMOI: estimating multiplicity of infection using parasite deep sequencing data', 'Assefa, Samuel A|Preston, Mark D|Campino, Susana|Ocholla, Harold|Sutherland, Colin J|Clark, Taane G', 'London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.|London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.|London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.|London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.|London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.|London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK, Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Box 30096 BT3, Blantyre, Malawia.', 'Malawi|Malawi|Malawi|Malawi|Malawi|Malawi', 0, 'Algorithms|Animals|Genome, Protozoan|Genotype|High-Throughput Nucleotide Sequencing|Humans|Malaria, Falciparum|Plasmodium falciparum|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btu005', '24443379', 'Individuals living in endemic areas generally harbour multiple parasite strains. Multiplicity of infection (MOI) can be an indicator of immune status and transmission intensity. It has a potentially confounding effect on a number of population genetic analyses, which often assume isolates are clonal. Polymerase chain reaction-based approaches to estimate MOI can lack sensitivity. For example, in the human malaria parasite Plasmodium falciparum, genotyping of the merozoite surface protein (MSP1/2) genes is a standard method for assessing MOI, despite the apparent problem of underestimation. The availability of deep coverage data from massively parallizable sequencing technologies means that MOI can be detected genome wide by considering the abundance of heterozygous genotypes. Here, we present a method to estimate MOI, which considers unique combinations of polymorphisms from sequence reads. The method is implemented within the estMOI software. When applied to clinical P.falciparum isolates from three continents, we find that multiple infections are common, especially in regions with high transmission.'),
(78185, 2014, 'bio', 'Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology', 'Starruß, Jörn|de Back, Walter|Brusch, Lutz|Deutsch, Andreas', 'Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany.|Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany.|Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany.|Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Models, Biological|Myxococcus xanthus|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt772', '24443380', 'Morpheus is a modeling environment for the simulation and integration of cell-based models with ordinary differential equations and reaction-diffusion systems. It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code. Its graphical user interface supports the entire workflow from model construction and simulation to visualization, archiving and batch processing.'),
(78186, 2014, 'bio', 'Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum', 'Mahé, Pierre|Arsac, Maud|Chatellier, Sonia|Monnin, Valérie|Perrot, Nadine|Mailler, Sandrine|Girard, Victoria|Ramjeet, Mahendrasingh|Surre, Jérémy|Lacroix, Bruno|van Belkum, Alex|Veyrieras, Jean-Baptiste', 'BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.|BioMérieux SA, Unit Innovation and Systems, Marcy lEtoile, 69280, France and BioMérieux SA, Unit Microbiology, R&D Microbiology, La Balme Les Grottes, 38390, France.', 'France|France|France|France|France|France|France|France|France|France|France|France', 0, 'Automation|Databases, Genetic|Gram-Negative Bacteria|Gram-Positive Bacteria|Linear Models|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btu022', '24443381', 'Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been broadly adopted by routine clinical microbiology laboratories for bacterial species identification. An isolated colony of the targeted microorganism is the single prerequisite. Currently, MS-based microbial identification directly from clinical specimens can not be routinely performed, as it raises two main challenges: (i) the nature of the sample itself may increase the level of technical variability and bring heterogeneity with respect to the reference database and (ii) the possibility of encountering polymicrobial samples that will yield a mixed MS fingerprint. In this article, we introduce a new method to infer the composition of polymicrobial samples on the basis of a single mass spectrum. Our approach relies on a penalized non-negative linear regression framework making use of species-specific prototypes, which can be derived directly from the routine reference database of pure spectra.'),
(78187, 2014, 'bio', 'Exploring genome characteristics and sequence quality without a reference', 'Simpson, Jared T', 'Ontario Institute for Cancer Research, Toronto, Canada.', 'Canada', 73, 'Algorithms|Base Sequence|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1093/bioinformatics/btu023', '24443382', 'The de novo assembly of large, complex genomes is a significant challenge with currently available DNA sequencing technology. While many de novo assembly software packages are available, comparatively little attention has been paid to assisting the user with the assembly.'),
(78188, 2014, 'bio', 'ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics', 'Silva, Ricardo R|Jourdan, Fabien|Salvanha, Diego M|Letisse, Fabien|Jamin, Emilien L|Guidetti-Gonzalez, Simone|Labate, Carlos A|Vêncio, Ricardo Z N', 'LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.|LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, Universit de Toulouse, INSA, UPS, INP, LISBP, Toulouse, France, Institute for Systems Biology, Seattle, Washington, USA, CNRS, UMR5504, Toulouse, France, Department of Genetics ESALQ-USP, University of Sao Paulo, Piracicaba, Brazil and Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol CTBE, Campinas, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Automation, Laboratory|Bayes Theorem|Chromatography, Liquid|Mass Spectrometry|Metabolome|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btu019', '24443383', 'We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions.'),
(78189, 2014, 'bio', 'jmodeltestorg: selection of nucleotide substitution models on the cloud', 'Santorum, Jose Manuel|Darriba, Diego|Taboada, Guillermo L|Posada, David', 'Department of Electronics and Systems, University of A Coruña, 15071 A Coruña and Department of Biochemistry, Genetics and Immunology, University of Vigo, 36201 Vigo, Spain.|Department of Electronics and Systems, University of A Coruña, 15071 A Coruña and Department of Biochemistry, Genetics and Immunology, University of Vigo, 36201 Vigo, Spain.|Department of Electronics and Systems, University of A Coruña, 15071 A Coruña and Department of Biochemistry, Genetics and Immunology, University of Vigo, 36201 Vigo, Spain.|Department of Electronics and Systems, University of A Coruña, 15071 A Coruña and Department of Biochemistry, Genetics and Immunology, University of Vigo, 36201 Vigo, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Cluster Analysis|Internet|Models, Genetic|Nucleotides|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btu032', '24451621', 'The selection of models of nucleotide substitution is one of the major steps of modern phylogenetic analysis. Different tools exist to accomplish this task, among which jModelTest 2 (jMT2) is one of the most popular. Still, to deal with large DNA alignments with hundreds or thousands of loci, users of jMT2 need to have access to High Performance Computing clusters, including installation and configuration capabilities, conditions not always met. Here we present jmodeltest.org, a novel web server for the transparent execution of jMT2 across different platforms and for a wide range of users. Its main benefit is straightforward execution, avoiding any configuration/execution issues, and reducing significantly in most cases the time required to complete the analysis.'),
(78190, 2014, 'bio', 'Reference-free cell mixture adjustments in analysis of DNA methylation data', 'Houseman, Eugene Andres|Molitor, John|Marsit, Carmen J', 'School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.', 'United States|United States|United States', 0, 'DNA Methylation|Gene Expression|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu029', '24451622', 'Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known.'),
(78191, 2014, 'bio', 'RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies', 'Stamatakis, Alexandros', 'Scientific Computing Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg and Department of Informatics, Institute of Theoretical Informatics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.', 'Germany', 0, 'High-Throughput Nucleotide Sequencing|Likelihood Functions|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btu033', '24451623', 'Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community.'),
(78192, 2014, 'bio', 'CAFE: an R package for the detection of gross chromosomal abnormalities from gene expression microarray data', 'Bollen, Sander|Leddin, Mathias|Andrade-Navarro, Miguel A|Mah, Nancy', 'Computational Biology and Data Mining Group, Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany, Graduate School of Life Sciences, Utrecht University, Universiteitsweg 98, 3584 CG, Utrecht, the Netherlands and Roche Diagnostics GmbH, 82377 Penzberg, Germany.|Computational Biology and Data Mining Group, Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany, Graduate School of Life Sciences, Utrecht University, Universiteitsweg 98, 3584 CG, Utrecht, the Netherlands and Roche Diagnostics GmbH, 82377 Penzberg, Germany.|Computational Biology and Data Mining Group, Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany, Graduate School of Life Sciences, Utrecht University, Universiteitsweg 98, 3584 CG, Utrecht, the Netherlands and Roche Diagnostics GmbH, 82377 Penzberg, Germany.|Computational Biology and Data Mining Group, Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany, Graduate School of Life Sciences, Utrecht University, Universiteitsweg 98, 3584 CG, Utrecht, the Netherlands and Roche Diagnostics GmbH, 82377 Penzberg, Germany.', 'Germany|Germany|Germany|Germany', 2, 'Chromosome Aberrations|Gene Expression|Markov Chains|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btu028', '24451624', 'The current methods available to detect chromosomal abnormalities from DNA microarray expression data are cumbersome and inflexible. CAFE has been developed to alleviate these issues. It is implemented as an R package that analyzes Affymetrix *.CEL files and comes with flexible plotting functions, easing visualization of chromosomal abnormalities.'),
(78193, 2014, 'bio', 'MEMBPLUGIN: studying membrane complexity in VMD', 'Guixà-González, Ramon|Rodriguez-Espigares, Ismael|Ramírez-Anguita, Juan Manuel|Carrió-Gaspar, Pau|Martinez-Seara, Hector|Giorgino, Toni|Selent, Jana', 'Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.|Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, Department of Physics, Tampere University of Technology, Tampere, Finland and Institute of Biomedical Engineering, National Research Council of Italy (ISIB-CNR), Padua, Italy.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Membrane Lipids|Membrane Proteins|Molecular Dynamics Simulation|Programming Languages|Protein Structure, Tertiary', 'Journal Article', '10.1093/bioinformatics/btu037', '24451625', 'Computer simulations are giving way to more complex and accurate studies of biological membranes by molecular dynamics (MD) simulations. The analysis of MD trajectories comprises the biophysical characterization of membrane properties or the study of protein-lipid interactions and dynamics. However, there is a lack of automated tools to analyse MD simulations of complex membrane or membrane-protein systems. Here we present MEMBPLUGIN, a plugin for the Visual Molecular Dynamics package that provides algorithms to measure a host of essential biophysical properties in simulated membranes. MEMBPLUGIN features are accessible both through a user-friendly graphical interface and as command-line procedures to be invoked in analysis scripts.'),
(78194, 2014, 'bio', 'InterProScan 5: genome-scale protein function classification', 'Jones, Philip|Binns, David|Chang, Hsin-Yu|Fraser, Matthew|Li, Weizhong|McAnulla, Craig|McWilliam, Hamish|Maslen, John|Mitchell, Alex|Nuka, Gift|Pesseat, Sebastien|Quinn, Antony F|Sangrador-Vegas, Amaia|Scheremetjew, Maxim|Yong, Siew-Yit|Lopez, Rodrigo|Hunter, Sarah', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, 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', 2125, 'Arabidopsis|Cluster Analysis|Genome|Genomics|High-Throughput Nucleotide Sequencing|Programming Languages|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btu031', '24451626', 'Robust large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterize many millions of sequences. Here, we describe a new Java-based architecture for the widely used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete reimplementation of the software framework, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the open source code is hosted at Google Code.'),
(78195, 2014, 'bio', 'IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics', 'Rosikiewicz, Marta|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, University of Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 5, 'Algorithms|Animals|Benchmarking|Biometry|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btu027', '24451627', 'Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability.'),
(78196, 2014, 'bio', 'BLESS: bloom filter-based error correction solution for high-throughput sequencing reads', 'Heo, Yun|Wu, Xiao-Long|Chen, Deming|Ma, Jian|Hwu, Wen-Mei', 'Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Automation, Laboratory|False Positive Reactions|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu030', '24451628', 'Rapid advances in next-generation sequencing (NGS) technology have led to exponential increase in the amount of genomic information. However, NGS reads contain far more errors than data from traditional sequencing methods, and downstream genomic analysis results can be improved by correcting the errors. Unfortunately, all the previous error correction methods required a large amount of memory, making it unsuitable to process reads from large genomes with commodity computers.'),
(78197, 2014, 'bio', 'h5vc: scalable nucleotide tallies with HDF5', 'Pyl, Paul Theodor|Gehring, Julian|Fischer, Bernd|Huber, Wolfgang', 'EMBL Heidelberg, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|EMBL Heidelberg, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|EMBL Heidelberg, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|EMBL Heidelberg, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Chromosome Mapping|Exome|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mutation|Nucleotides|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu026', '24451629', 'As applications of genome sequencing, including exomes and whole genomes, are expanding, there is a need for analysis tools that are scalable to large sets of samples and/or ultra-deep coverage. Many current tool chains are based on the widely used file formats BAM and VCF or VCF-derivatives. However, for some desirable analyses, data management with these formats creates substantial implementation overhead, and much time is spent parsing files and collating data. We observe that a tally data structure, i.e. the table of counts of nucleotides × samples × strands × genomic positions, provides a reasonable intermediate level of abstraction for many genomics analyses, including single nucleotide variant (SNV) and InDel calling, copy-number estimation and mutation spectrum analysis. Here we present h5vc, a data structure and associated software for managing tallies. The software contains functionality for creating tallies from BAM files, flexible and scalable data visualization, data quality assessment, computing statistics relevant to variant calling and other applications. Through the simplicity of its API, we envision making low-level analysis of large sets of genome sequencing data accessible to a wider range of researchers.');
