INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79060, 2015, 'bio', 'TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds', 'Liu, Xian|Gao, Yuan|Peng, Jianlong|Xu, Yuan|Wang, Yulan|Zhou, Nannan|Xing, Jing|Luo, Xiaomin|Jiang, Hualiang|Zheng, Mingyue', 'State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.', 'China|China|China|China|China|China|China|China|China|China', 21, 'Antiviral Agents|Computational Biology|Databases, Pharmaceutical|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Guanine|HIV|HIV Infections|Humans|Internet|Pharmaceutical Preparations|Software', 'Journal Article', '10.1093/bioinformatics/btv099', '25686637', 'Discovering the relevant therapeutic targets for drug-like molecules, or their unintended off-targets that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently.'),
(79061, 2015, 'bio', 'RAPTR-SV: a hybrid method for the detection of structural variants', 'Bickhart, Derek M|Hutchison, Jana L|Xu, Lingyang|Schnabel, Robert D|Taylor, Jeremy F|Reecy, James M|Schroeder, Steven|Van Tassell, Curt P|Sonstegard, Tad S|Liu, George E', 'Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.|Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 12, 'Algorithms|Chromosome Mapping|Genome, Human|Genomic Structural Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btv086', '25686638', 'Identification of structural variants (SVs) in sequence data results in a large number of false positive calls using existing software, which overburdens subsequent validation.'),
(79062, 2015, 'bio', 'Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry', 'Nikolskiy, Igor|Siuzdak, Gary|Patti, Gary J', 'Department of Genetics, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA, Scripps Center for Metabolomics and Mass Spectrometry, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA and Department of Chemistry, Washington University, St. Louis, MO 63130, USA.|Department of Genetics, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA, Scripps Center for Metabolomics and Mass Spectrometry, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA and Department of Chemistry, Washington University, St. Louis, MO 63130, USA.|Department of Genetics, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA, Scripps Center for Metabolomics and Mass Spectrometry, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA and Department of Chemistry, Washington University, St. Louis, MO 63130, USA Department of Genetics, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA, Scripps Center for Metabolomics and Mass Spectrometry, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA and Department of Chemistry, Washington University, St. Louis, MO 63130, USA Department of Genetics, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA, Scripps Center for Metabolomics and Mass Spectrometry, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA and Department of Chemistry, Washington University, St. Louis, MO 63130, USA.', 'United States|United States|United States', 0, 'Databases, Factual|Humans|Likelihood Functions|Mass Spectrometry|Metabolic Networks and Pathways|Metabolomics', 'Journal Article', '10.1093/bioinformatics/btv085', '25691443', 'The goal of large-scale metabolite profiling is to compare the relative concentrations of as many metabolites extracted from biological samples as possible. This is typically accomplished by measuring the abundances of thousands of ions with high-resolution and high mass accuracy mass spectrometers. Although the data from these instruments provide a comprehensive fingerprint of each sample, identifying the structures of the thousands of detected ions is still challenging and time intensive. An alternative, less-comprehensive approach is to use triple quadrupole (QqQ) mass spectrometry to analyze predetermined sets of metabolites (typically fewer than several hundred). This is done using authentic standards to develop QqQ experiments that specifically detect only the targeted metabolites, with the advantage that the need for ion identification after profiling is eliminated.'),
(79063, 2015, 'bio', 'The GPMDB REST interface', 'Fenyö, David|Beavis, Ronald C', 'Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York, NY, USA and Department of Biochemistry and Medical Genetics, The University of Manitoba, Winnipeg, MB, Canada.|Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York, NY, USA and Department of Biochemistry and Medical Genetics, The University of Manitoba, Winnipeg, MB, Canada.', 'Canada|Canada', 0, 'Algorithms|Computational Biology|Databases, Factual|Humans|Information Storage and Retrieval|Protein Processing, Post-Translational|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv107', '25697819', 'The Global Proteome Machine and Database (GPMDB) representational state transfer (REST) service was designed to provide simplified access to the proteomics information in GPMDB using a stable set of methods and parameters. Version 1 of this interface gives access to 25 methods for retrieving experimental information about protein post-translational modifications, amino acid variants, alternate splicing variants and protein cleavage patterns.'),
(79064, 2015, 'bio', 'Sambamba: fast processing of NGS alignment formats', 'Tarasov, Artem|Vilella, Albert J|Cuppen, Edwin|Nijman, Isaac J|Prins, Pjotr', 'Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands.|Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands.|Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands.|Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands.|Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands Department of Statistical Simulation, St. Petersburg State University, St. Petersburg, Russia, Illumina Cambridge, Cambridge, UK, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, The Netherlands, Department of Medical Genetics, Institute for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands and Department of Nematology, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 259, 'Algorithms|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv098', '25697820', 'Sambamba is a high-performance robust tool and library for working with SAM, BAM and CRAM sequence alignment files, the most common file formats for aligned next generation sequencing data. Sambamba is a faster alternative to samtools that exploits multi-core processing and dramatically reduces processing time. Sambamba is being adopted at sequencing centers, not only because of its speed, but also because of additional functionality, including coverage analysis and powerful filtering capability.'),
(79065, 2015, 'bio', 'Extending P450 site-of-metabolism models with region-resolution data', 'Zaretzki, Jed M|Browning, Michael R|Hughes, Tyler B|Swamidass, S Joshua', 'Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.', 'United States|United States|United States|United States', 11, 'Algorithms|Computational Biology|Cytochrome P-450 Enzyme System|Humans|Models, Molecular|Molecular Docking Simulation|Small Molecule Libraries|Structure-Activity Relationship|Xenobiotics', 'Journal Article', '10.1093/bioinformatics/btv100', '25697821', 'Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA-approved drugs. Medicinal chemists often want to know which atoms of a molecule-its metabolized sites-are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecules sites of metabolism. There is, however, much more data available at a lower resolution, where the exact site of metabolism is not known, but the region of the molecule that is oxidized is known. Until now, no site-of-metabolism models made use of region-resolution data.'),
(79066, 2015, 'bio', 'Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolution', 'Mao, Wenzhi|Kaya, Cihan|Dutta, Anindita|Horovitz, Amnon|Bahar, Ivet', 'Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA, Department of Pharmacology, School of Medicine, Tsinghua University, Beijing 100084, China and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.', 'China|China|China|China|China', 0, 'Algorithms|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Phylogeny|Proteins|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/btv103', '25697822', 'With rapid accumulation of sequence data on several species, extracting rational and systematic information from multiple sequence alignments (MSAs) is becoming increasingly important. Currently, there is a plethora of computational methods for investigating coupled evolutionary changes in pairs of positions along the amino acid sequence, and making inferences on structure and function. Yet, the significance of coevolution signals remains to be established. Also, a large number of false positives (FPs) arise from insufficient MSA size, phylogenetic background and indirect couplings.'),
(79067, 2015, 'bio', 'AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction', 'Xu, Dong|Jaroszewski, Lukasz|Li, Zhanwen|Godzik, Adam', 'Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Computational Biology|Humans|Internet|Models, Theoretical|Protein Conformation|Protein Interaction Domains and Motifs|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv092', '25701568', 'Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available.'),
(79068, 2015, 'bio', 'CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions', 'Desouki, Abdelmoneim Amer|Jarre, Florian|Gelius-Dietrich, Gabriel|Lercher, Martin J', 'Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.|Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.|Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.|Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt Institute for Computer Science, Institute for Mathematics, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.', 'Germany|Germany|Germany|Germany', 9, 'Algorithms|Computer Simulation|Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Biological|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btv096', '25701569', 'Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict metabolic phenotypes, e.g. growth rates, ATP yield or the fitness of gene knockouts. One frequent difficulty of constraint-based solutions is the inclusion of thermodynamically infeasible loops (or internal cycles), which add nonbiological fluxes to the predictions.'),
(79069, 2015, 'bio', 'Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors', 'Megjhani, Murad|Rey-Villamizar, Nicolas|Merouane, Amine|Lu, Yanbin|Mukherjee, Amit|Trett, Kristen|Chong, Peter|Harris, Carolyn|Shain, William|Roysam, Badrinath', 'Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Childrens Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Brain|Brain Mapping|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Mice|Microglia|Pattern Recognition, Automated|Rats|Software', 'Journal Article', '10.1093/bioinformatics/btv109', '25701570', 'The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions.'),
(79070, 2015, 'bio', 'tEFMA: computing thermodynamically feasible elementary flux modes in metabolic networks', 'Gerstl, Matthias P|Jungreuthmayer, Christian|Zanghellini, Jürgen', 'Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.|Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.|Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria Austrian Centre of Industrial Biotechnology, Vienna, Austria and Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.', 'Austria|Austria|Austria', 16, 'Algorithms|Computational Biology|Computer Simulation|Humans|Metabolic Flux Analysis|Metabolic Networks and Pathways|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btv111', '25701571', ': Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks.'),
(79071, 2015, 'bio', 'Unified representation of genetic variants', 'Tan, Adrian|Abecasis, Gonçalo R|Kang, Hyun Min', 'Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 0, 'Algorithms|Genetic Variation|Genome, Human|Humans|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv112', '25701572', 'A genetic variant can be represented in the Variant Call Format (VCF) in multiple different ways. Inconsistent representation of variants between variant callers and analyses will magnify discrepancies between them and complicate variant filtering and duplicate removal. We present a software tool vt normalize that normalizes representation of genetic variants in the VCF. We formally define variant normalization as the consistent representation of genetic variants in an unambiguous and concise way and derive a simple general algorithm to enforce it. We demonstrate the inconsistent representation of variants across existing sequence analysis tools and show that our tool facilitates integration of diverse variant types and call sets.'),
(79072, 2015, 'bio', 'Oasis: online analysis of small RNA deep sequencing data', 'Capece, Vincenzo|Garcia Vizcaino, Julio C|Vidal, Ramon|Rahman, Raza-Ur|Pena Centeno, Tonatiuh|Shomroni, Orr|Suberviola, Irantzu|Fischer, Andre|Bonn, Stefan', 'Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.|Laboratory of Computational Systems Biology and Laboratory of Epigenetic Mechanisms in Dementia, German Center for Neurodegenerative Diseases, 37077 Goettingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 35, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Online Systems|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv113', '25701573', 'Oasis is a web application that allows for the fast and flexible online analysis of small-RNA-seq (sRNA-seq) data. It was designed for the end user in the lab, providing an easy-to-use web frontend including video tutorials, demo data and best practice step-by-step guidelines on how to analyze sRNA-seq data. Oasis exclusive selling points are a differential expression module that allows for the multivariate analysis of samples, a classification module for robust biomarker detection and an advanced programming interface that supports the batch submission of jobs. Both modules include the analysis of novel miRNAs, miRNA targets and functional analyses including GO and pathway enrichment. Oasis generates downloadable interactive web reports for easy visualization, exploration and analysis of data on a local system. Finally, Oasis modular workflow enables for the rapid (re-) analysis of data.'),
(79073, 2015, 'bio', 'Annocript: a flexible pipeline for the annotation of transcriptomes able to identify putative long noncoding RNAs', 'Musacchia, Francesco|Basu, Swaraj|Petrosino, Giuseppe|Salvemini, Marco|Sanges, Remo', 'Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy and Department of Biology, University of Naples Federico II, Via Mezzocannone 8, 80134, Naples, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy and Department of Biology, University of Naples Federico II, Via Mezzocannone 8, 80134, Naples, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy and Department of Biology, University of Naples Federico II, Via Mezzocannone 8, 80134, Naples, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy and Department of Biology, University of Naples Federico II, Via Mezzocannone 8, 80134, Naples, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy and Department of Biology, University of Naples Federico II, Via Mezzocannone 8, 80134, Naples, Italy.', 'Italy|Italy|Italy|Italy|Italy', 54, 'Humans|Molecular Sequence Annotation|RNA, Long Noncoding|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv106', '25701574', 'The eukaryotic transcriptome is composed of thousands of coding and long non-coding RNAs (lncRNAs). However, we lack a software platform to identify both RNA classes in a given transcriptome. Here we introduce Annocript, a pipeline that combines the annotation of protein coding transcripts with the prediction of putative lncRNAs in whole transcriptomes. It downloads and indexes the needed databases, runs the analysis and produces human readable and standard outputs together with summary statistics of the whole analysis.'),
(79074, 2015, 'bio', 'Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum', 'Isakov, Ofer|Bordería, Antonio V|Golan, David|Hamenahem, Amir|Celniker, Gershon|Yoffe, Liron|Blanc, Hervé|Vignuzzi, Marco|Shomron, Noam', 'Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel, Institut Pasteur, Viral Populations and Pathogenesis, CNRS URA 3015, Paris, France and Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.', 'France|France|France|France|France|France|France|France|France', 0, 'Antiviral Agents|Biological Evolution|Genetic Variation|Genome, Viral|High-Throughput Nucleotide Sequencing|Humans|Mutation|Population Dynamics|RNA Viruses|Virus Diseases|Viruses', 'Journal Article', '10.1093/bioinformatics/btv101', '25701575', 'The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations.'),
(79075, 2015, 'bio', 'Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study', 'Hendrickx, Diana M|Jennen, Danyel G J|Briedé, Jacob J|Cavill, Rachel|de Kok, Theo M|Kleinjans, Jos C S', 'Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.|Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.|Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.|Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.|Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.|Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6200 MD Maastricht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Antifibrinolytic Agents|Benzo(a)pyrene|Carcinoma, Hepatocellular|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Linear Models|Liver Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Software|Time Factors|Vitamin K 3', 'Comparative Study', '10.1093/bioinformatics/btv108', '25701576', 'Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package]. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic endpoints (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data.'),
(79076, 2015, 'bio', 'LCR-eXXXplorer: a web platform to search, visualize and share data for low complexity regions in protein sequences', 'Kirmitzoglou, Ioannis|Promponas, Vasilis J', 'Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, CY 1678, Nicosia, Cyprus.|Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, CY 1678, Nicosia, Cyprus.', 'Cyprus|Cyprus', 0, 'Algorithms|Computer Graphics|Databases, Protein|Humans|Internet|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv115', '25712690', 'Local compositionally biased and low complexity regions (LCRs) in amino acid sequences have initially attracted the interest of researchers due to their implication in generating artifacts in sequence database searches. There is accumulating evidence of the biological significance of LCRs both in physiological and in pathological situations. Nonetheless, LCR-related algorithms and tools have not gained wide appreciation across the research community, partly due to the fact that only a handful of user-friendly software is currently freely available.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79077, 2015, 'bio', 'EpiToolKit--a web-based workbench for vaccine design', 'Schubert, Benjamin|Brachvogel, Hans-Philipp|Jürges, Christopher|Kohlbacher, Oliver', 'Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany.|Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany.|Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany.|Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany, Applied Bioinformatics, Department of Computer Science, 72076 Tübingen, Germany, Quantitative Biology Center, 72076 Tübingen, Germany and Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Epitopes|Humans|Internet|Major Histocompatibility Complex|Proteins|Software|Vaccines', 'Journal Article', '10.1093/bioinformatics/btv116', '25712691', 'EpiToolKit is a virtual workbench for immunological questions with a focus on vaccine design. It offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets.'),
(79078, 2015, 'bio', 'specL--an R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics', 'Panse, Christian|Trachsel, Christian|Grossmann, Jonas|Schlapbach, Ralph', 'Functional Genomics Center Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland.|Functional Genomics Center Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland.|Functional Genomics Center Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland.|Functional Genomics Center Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 7, 'Humans|Peptide Fragments|Proteins|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv105', '25712692', 'Targeted data extraction methods are attractive ways to obtain quantitative peptide information from a proteomics experiment. Sequential Window Acquisition of all Theoretical Spectra (SWATH) and Data Independent Acquisition (DIA) methods increase reproducibility of acquired data because the classical precursor selection is omitted and all present precursors are fragmented. However, especially for targeted data extraction, MS coordinates (retention time information precursor and fragment masses) are required for the particular entities (peptide ions). These coordinates are usually generated in a so-called discovery experiment earlier on in the project if not available in public spectral library repositories. The quality of the assay panel is crucial to ensure appropriate downstream analysis. For that, a method is needed to create spectral libraries and to export customizable assay panels.'),
(79079, 2015, 'bio', 'GlycanAnalysis Plug-in: a database search tool for N-glycan structures using mass spectrometry', 'Morimoto, Kentaro|Nishikaze, Takashi|Yoshizawa, Akiyasu C|Kajihara, Shigeki|Aoshima, Ken|Oda, Yoshiya|Tanaka, Koichi', 'Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.|Koichi Tanaka Laboratory of Advanced Science and Technology, Shimadzu Corporation, Kyoto, Japan and Eisai Product Creation Systems, Eisai Co., Ltd., Tsukuba, Ibaraki, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 8, 'Databases, Factual|Glycopeptides|Glycosylation|Humans|Mass Spectrometry|Polysaccharides|Proteomics|Search Engine|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv110', '25712693', 'Tandem mass spectrometry (MS/MS or MS(n)) is a potent technique for characterizing N-glycan structures. GlycanAnalysis searches a glycan database to support the identification of glycan structures from MS/MS spectra. It also calculates diagnostic ions of glycan structures registered in a glycan database (GlycomeDB or KEGG GLYCAN) and searches for MS/MS spectra of N-glycans that match diagnostic ions to determine the structures. This program functions as a plug-in for Mass++, a freeware mass spectrum visualization and analysis program.'),
(79080, 2015, 'bio', 'rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data', 'Shi, Yang|Chinnaiyan, Arul M|Jiang, Hui', 'Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 0, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Protein Isoforms|RNA|RNA Splicing|Sequence Analysis, RNA|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv119', '25717189', 'High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data.'),
(79081, 2015, 'bio', 'Detection of significant protein coevolution', 'Ochoa, David|Juan, David|Valencia, Alfonso|Pazos, Florencio', 'Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.', 'Spain|Spain|Spain|Spain', 11, 'Databases, Protein|Evolution, Molecular|Genome, Human|Humans|Internet|Macromolecular Substances|Phylogeny|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv102', '25717190', 'The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals.'),
(79082, 2015, 'bio', 'GeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes', 'Chen, Yi-Hsiung|Yang, Chi-Dung|Tseng, Ching-Ping|Huang, Hsien-Da|Ho, Shinn-Ying', 'Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China Institute of Bioinformatics and Systems Biology and Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.', 'China|China|China|China|China', 0, 'Algorithms|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btv075', '25717191', 'The establishment of quantitative gene regulatory networks (qGRNs) through existing network component analysis (NCA) approaches suffers from shortcomings such as usage limitations of problem constraints and the instability of inferred qGRNs. The proposed GeNOSA framework uses a global optimization algorithm (OptNCA) to cope with the stringent limitations of NCA approaches in large-scale qGRNs.'),
(79083, 2015, 'bio', 'Guidance for RNA-seq co-expression network construction and analysis: safety in numbers', 'Ballouz, S|Verleyen, W|Gillis, J', 'Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.', 'United States|United States|United States', 0, 'Algorithms|Brain|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv118', '25717192', 'RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks.'),
(79084, 2015, 'bio', 'Normalization and noise reduction for single cell RNA-seq experiments', 'Ding, Bo|Zheng, Lina|Zhu, Yun|Li, Nan|Jia, Haiyang|Ai, Rizi|Wildberg, Andre|Wang, Wei', 'Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.', 'China|China|China|China|China|China|China|China', 48, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Signal-To-Noise Ratio|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv122', '25717193', 'A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules.'),
(79085, 2015, 'bio', 'Fast, accurate, and reliable molecular docking with QuickVina 2', 'Alhossary, Amr|Handoko, Stephanus Daniel|Mu, Yuguang|Kwoh, Chee-Keong', 'School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.|School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.|School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.|School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.', 'Singapore|Singapore|Singapore|Singapore', 20, 'Computational Biology|Databases, Pharmaceutical|Drug Design|Humans|Ligands|Molecular Docking Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv082', '25717194', 'The need for efficient molecular docking tools for high-throughput screening is growing alongside the rapid growth of drug-fragment databases. AutoDock Vina (Vina) is a widely used docking tool with parallelization for speed. QuickVina (QVina 1) then further enhanced the speed via a heuristics, requiring high exhaustiveness. With low exhaustiveness, its accuracy was compromised. We present in this article the latest version of QuickVina (QVina 2) that inherits both the speed of QVina 1 and the reliability of the original Vina.'),
(79086, 2015, 'bio', 'An event-driven approach for studying gene block evolution in bacteria', 'Ream, David C|Bankapur, Asma R|Friedberg, Iddo', 'Department of Microbiology, Miami University, Oxford, OH, USA and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.|Department of Microbiology, Miami University, Oxford, OH, USA and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.|Department of Microbiology, Miami University, Oxford, OH, USA and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA Department of Microbiology, Miami University, Oxford, OH, USA and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.', 'United States|United States|United States', 0, 'Bacteria|Computational Biology|Evolution, Molecular|Genes, Bacterial|Genome, Bacterial|Genomics|Operon|Software', 'Journal Article', '10.1093/bioinformatics/btv128', '25717195', 'Gene blocks are genes co-located on the chromosome. In many cases, gene blocks are conserved between bacterial species, sometimes as operons, when genes are co-transcribed. The conservation is rarely absolute: gene loss, gain, duplication, block splitting and block fusion are frequently observed. An open question in bacterial molecular evolution is that of the formation and breakup of gene blocks, for which several models have been proposed. These models, however, are not generally applicable to all types of gene blocks, and consequently cannot be used to broadly compare and study gene block evolution. To address this problem, we introduce an event-based method for tracking gene block evolution in bacteria.'),
(79087, 2015, 'bio', 'Automated benchmarking of peptide-MHC class I binding predictions', 'Trolle, Thomas|Metushi, Imir G|Greenbaum, Jason A|Kim, Yohan|Sidney, John|Lund, Ole|Sette, Alessandro|Peters, Bjoern|Nielsen, Morten', 'Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 0, 'Automation|Benchmarking|Computer Simulation|Databases, Factual|Epitopes|Histocompatibility Antigens Class I|Humans|Peptide Fragments|Protein Binding|Software', 'Journal Article', '10.1093/bioinformatics/btv123', '25717196', 'Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study.'),
(79088, 2015, 'bio', 'RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis', 'Glaab, Enrico|Schneider, Reinhard', 'Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg', 10, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Databases, Factual|Genomics|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv127', '25717197', 'High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.'),
(79089, 2015, 'bio', 'A new method to improve network topological similarity search: applied to fold recognition', 'Lhota, John|Hauptman, Ruth|Hart, Thomas|Ng, Clara|Xie, Lei', 'Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.|Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.|Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.|Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.|Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A. Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.', '-|-|-|-|-', 0, 'Algorithms|Computational Biology|Gene Regulatory Networks|Humans|Pattern Recognition, Automated|Protein Folding|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv125', '25717198', 'Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Thus, new similarity search strategies are needed to efficiently and reliably infer the structure and function of new sequences. The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework-Enrichment of Network Topological Similarity (ENTS)-to improve the performance of large scale similarity searches in bioinformatics.'),
(79090, 2015, 'bio', 'SimSeq: a nonparametric approach to simulation of RNA-sequence datasets', 'Benidt, Sam|Nettleton, Dan', 'Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA.|Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA.', 'United States|United States', 0, 'Algorithms|Binomial Distribution|Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, RNA|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv124', '25725090', 'RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method.'),
(79091, 2015, 'bio', 'GlycoProfileAssigner: automated structural assignment with error estimation for glycan LC data', 'Duffy, Fergal J|Rudd, Pauline M', 'National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Co. Dublin, Ireland.|National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Co. Dublin, Ireland.', 'Ireland|Ireland', 6, 'Chromatography, Liquid|Databases, Factual|Glycopeptides|Glycosylation|Humans|Immunoglobulin G|Mass Spectrometry|Polysaccharides|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv129', '25725091', 'Sequencing glycan structures is a difficult problem that requires the use of multiple experimental approaches. One powerful approach to glycan sequencing is the combination of liquid chromatography with sequential exoglycosidase digestions, however, interpreting this can be difficult and time-consuming. To aid this process, we introduce GlycoProfileAssigner, software for automated structural assignment of glycan profile data from liquid chromatography experiments.'),
(79092, 2015, 'bio', 'GeLL: a generalized likelihood library for phylogenetic models', 'Money, Daniel|Whelan, Simon', 'Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, B2N 5E3 Canada, Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA and.|Department of Evolutionary Biology, Uppsala University, Uppsala, 75236, Sweden.', 'Canada|Sweden', 0, 'Likelihood Functions|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv126', '25725494', 'Phylogenetic models are an important tool in molecular evolution allowing us to study the pattern and rate of sequence change. The recent influx of new sequence data in the biosciences means that to address evolutionary questions, we need a means for rapid and easy model development and implementation. Here we present GeLL, a Java library that lets users use text to quickly and efficiently define novel forms of discrete data and create new substitution models that describe how those data change on a phylogeny. GeLL allows users to define general substitution models and data structures in a way that is not possible in other existing libraries, including mixture models and non-reversible models. Classes are provided for calculating likelihoods, optimizing model parameters and branch lengths, ancestral reconstruction and sequence simulation.'),
(79093, 2015, 'bio', 'Mammalian genome evolution is governed by multiple pacemakers', 'Duchêne, Sebastián|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.', 'Australia|Australia', 10, 'Animals|Biological Evolution|Evolution, Molecular|Genome|Genomics|Mammals|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btv121', '25725495', 'Genomic evolution is shaped by a dynamic combination of mutation, selection and genetic drift. These processes lead to evolutionary rate variation across loci and among lineages. In turn, interactions between these two forms of rate variation can produce residual effects, whereby the pattern of among-lineage rate heterogeneity varies across loci. The nature of rate variation is encapsulated in the pacemaker models of genome evolution, which differ in the degree of importance assigned to residual effects: none (Universal Pacemaker), some (Multiple Pacemaker) or total (Degenerate Multiple Pacemaker). Here we use a phylogenetic method to partition the rate variation across loci, allowing comparison of these pacemaker models. Our analysis of 431 genes from 29 mammalian taxa reveals that rate variation across these genes can be explained by 13 pacemakers, consistent with the Multiple Pacemaker model. We find no evidence that these pacemakers correspond to gene function. Our results have important consequences for understanding the factors driving genomic evolution and for molecular-clock analyses.'),
(79094, 2015, 'bio', 'hiHMM: Bayesian non-parametric joint inference of chromatin state maps', 'Sohn, Kyung-Ah|Ho, Joshua W K|Djordjevic, Djordje|Jeong, Hyun-Hwan|Park, Peter J|Kim, Ju Han', 'Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department o|Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department o|Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Animals|Bayes Theorem|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Drosophila melanogaster|Gene Expression Regulation, Developmental|Histones|Humans|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv117', '25725496', 'Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79095, 2015, 'bio', 'IVA: accurate de novo assembly of RNA virus genomes', 'Hunt, Martin|Gall, Astrid|Ong, Swee Hoe|Brener, Jacqui|Ferns, Bridget|Goulder, Philip|Nastouli, Eleni|Keane, Jacqueline A|Kellam, Paul|Otto, Thomas D', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Department of Paediatrics, University of Oxford, Oxford, UK.|Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, UK and.|Department of Paediatrics, University of Oxford, Oxford, UK.|Department of Virology, University College London Hospital NHS Foundation Trust, London, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK, Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, UK and.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Genome, Viral|HIV Infections|HIV-1|High-Throughput Nucleotide Sequencing|Humans|Influenza A virus|Influenza B virus|Influenza, Human|Polymerase Chain Reaction|RNA Viruses|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv120', '25725497', 'An accurate genome assembly from short read sequencing data is critical for downstream analysis, for example allowing investigation of variants within a sequenced population. However, assembling sequencing data from virus samples, especially RNA viruses, into a genome sequence is challenging due to the combination of viral population diversity and extremely uneven read depth caused by amplification bias in the inevitable reverse transcription and polymerase chain reaction amplification process of current methods.'),
(79096, 2015, 'bio', 'L-GRAAL: Lagrangian graphlet-based network aligner', 'Malod-Dognin, Noël|Pržulj, Nataša', 'Department of Computing, Imperial College London, London, UK.|Department of Computing, Imperial College London, London, UK.', 'United Kingdom|United Kingdom', 61, 'Algorithms|Computational Biology|Computer Graphics|Databases, Protein|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv130', '25725498', 'Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge.'),
(79097, 2015, 'bio', 'Mining viral proteins for antimicrobial and cell-penetrating drug delivery peptides', 'Freire, João Miguel|Almeida Dias, Susana|Flores, Luís|Veiga, Ana Salomé|Castanho, Miguel A R B', 'Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.|Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.|Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.|Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.|Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 0, 'Anti-Infective Agents|Capsid Proteins|Cell-Penetrating Peptides|Membrane Proteins|Sequence Analysis, Protein|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btv131', '25725499', 'The need for more effective and safer pharmaceuticals is a persistent quest. Microbial adaptations create the need to permanently develop new antimicrobials (AMPs), for instance. Similarly, intracellular delivery of drugs is still a challenge and translocation of membranes for drug delivery is an area of intense research. Peptides can be used both as AMP drug leads and drug carrier systems for intracellular delivery. Multifunctional proteins are abundant in viruses but, surprisingly, have never been thoroughly screened for bioactive peptide sequences.'),
(79098, 2015, 'bio', 'Logical transformation of genome-scale metabolic models for gene level applications and analysis', 'Zhang, Cheng|Ji, Boyang|Mardinoglu, Adil|Nielsen, Jens|Hua, Qiang', 'State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China, Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.|Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.|Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.|Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China, Shanghai Collaborative Innovation Center for Biomanufacturing Technology (SCICBT), Shanghai 200237, China.', 'China|Sweden|Sweden|Sweden|China', 26, 'Carcinoma, Hepatocellular|Cell Line, Tumor|Computational Biology|Computer Simulation|Escherichia coli Proteins|Genome, Bacterial|Genome, Fungal|Genome, Human|Humans|Liver Neoplasms|Metabolic Networks and Pathways|Models, Biological|Programming Languages|Saccharomyces cerevisiae Proteins|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv134', '25735769', 'In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of gene-reaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites.'),
(79099, 2015, 'bio', 'Alternative splicing and co-option of transposable elements: the case of TMPO/LAP2xcexb1 and ZNF451 in mammals', 'Abascal, Federico|Tress, Michael L|Valencia, Alfonso', 'Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid 28029, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid 28029, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid 28029, Spain.', 'Spain|Spain|Spain', 0, 'Alternative Splicing|Animals|DNA-Binding Proteins|Evolution, Molecular|Exons|Genome|Humans|Mammals|Membrane Proteins|Protein Isoforms|Retroelements|Transcription Factors|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btv132', '25735770', 'Transposable elements constitute a large fraction of vertebrate genomes and, during evolution, may be co-opted for new functions. Exonization of transposable elements inserted within or close to host genes is one possible way to generate new genes, and alternative splicing of the new exons may represent an intermediate step in this process. The genes TMPO and ZNF451 are present in all vertebrate lineages. Although they are not evolutionarily related, mammalian TMPO and ZNF451 do have something in common-they both code for splice isoforms that contain LAP2alpha domains. We found that these LAP2alpha domains have sequence similarity to repetitive sequences in non-mammalian genomes, which are in turn related to the first ORF from a DIRS1-like retrotransposon. This retrotransposon domestication happened separately and resulted in proteins that combine retrotransposon and host protein domains. The alternative splicing of the retrotransposed sequence allowed the production of both the new and the untouched original isoforms, which may have contributed to the success of the colonization process. The LAP2alpha-specific isoform of TMPO (LAP2xcexb1) has been co-opted for important roles in the cell, whereas the ZNF451 LAP2alpha isoform is evolving under strong purifying selection but remains uncharacterized.'),
(79100, 2015, 'bio', 'Estimating the proportion of true null hypotheses when the statistics are discrete', 'Dialsingh, Isaac|Austin, Stefanie R|Altman, Naomi S', 'Department of Mathematics and Statistics, The University of the West Indies, St. Augustine Campus, Trinidad and Tobago and.|Department of Statistics, The Pennsylvania State University, State College, PA 16802-2111, USA.|Department of Statistics, The Pennsylvania State University, State College, PA 16802-2111, USA.', 'Trinidad and Tobago|United States|United States', 6, 'Animals|Cattle|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Iron|Liver|Macaca mulatta|Male|Models, Statistical|Muscles|Pan troglodytes|Polymorphism, Single Nucleotide|Regression Analysis|Sequence Analysis, RNA|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/btv104', '25735771', 'In high-dimensional testing problems xcfx800, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating xcfx800 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems.'),
(79101, 2015, 'bio', '14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides', 'Madeira, Fábio|Tinti, Michele|Murugesan, Gavuthami|Berrett, Emily|Stafford, Margaret|Toth, Rachel|Cole, Christian|MacKintosh, Carol|Barton, Geoffrey J', 'Division of Computational Biology.|Division of Cell and Developmental Biology.|Division of Computational Biology.|Division of Computational Biology.|MRC Protein Phosphorylation Unit.|Division of Signal Transduction Therapy.|Division of Computational Biology.|Division of Cell and Developmental Biology.|Division of Computational Biology, Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 60, '14-3-3 Proteins|Amino Acid Motifs|Binding Sites|HEK293 Cells|Humans|Neural Networks (Computer)|Phosphopeptides|Phosphoproteins|Position-Specific Scoring Matrices|Proteome|Proteomics|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv133', '25735772', 'The 14-3-3 family of phosphoprotein-binding proteins regulates many cellular processes by docking onto pairs of phosphorylated Ser and Thr residues in a constellation of intracellular targets. Therefore, there is a pressing need to develop new prediction methods that use an updated set of 14-3-3-binding motifs for the identification of new 14-3-3 targets and to prioritize the downstream analysis of >2000 potential interactors identified in high-throughput experiments.'),
(79102, 2015, 'bio', 'EpIC: a rational pipeline for epitope immunogenicity characterization', 'Marciniuk, Kristen|Trost, Brett|Napper, Scott', 'Department of Biochemistry, University of Saskatchewan, Saskatoon, SK, S7N 5E5 Canada, Vaccine and Infectious Disease Organization-International Vaccine Centre, University of Saskatchewan, Saskatoon, SK, S7N 5E3 Canada and.|Vaccine and Infectious Disease Organization-International Vaccine Centre, University of Saskatchewan, Saskatoon, SK, S7N 5E3 Canada and Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 5C9 Canada.|Department of Biochemistry, University of Saskatchewan, Saskatoon, SK, S7N 5E5 Canada, Vaccine and Infectious Disease Organization-International Vaccine Centre, University of Saskatchewan, Saskatoon, SK, S7N 5E3 Canada and.', 'Canada|Canada|Canada', 0, 'Epitopes, B-Lymphocyte|Peptides|Sequence Analysis, Protein|Software|Vaccines, Subunit', 'Journal Article', '10.1093/bioinformatics/btv136', '25750418', 'Efforts to develop peptide-based vaccines, in particular those requiring site-specific targeting of self-proteins, rely on the ability to optimize the immunogenicity of the peptide epitopes. Currently, screening of candidate vaccines is typically performed through low-throughput, high-cost animal trials. To improve on this we present the program EpIC, which enables high-throughput prediction of peptide immunogenicity based on the endogenous occurrence of B-cell epitopes within native protein sequences. This information informs rational selection of immunogenicity-optimized epitopes for peptide vaccines.'),
(79103, 2015, 'bio', 'Reducing the search space for causal genetic variants with VASP', 'Field, Matthew A|Cho, Vicky|Cook, Matthew C|Enders, Anselm|Vinuesa, Carola G|Whittle, Belinda|Andrews, T Daniel|Goodnow, Chris C', 'Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia.|Australian Phenomics Facility, Australian National University, Canberra, ACT 2601, Australia.|Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Department of Immunology, The Canberra Hospital, Canberra, ACT 2605, Australia.|Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Rammaciotti Immunisation Genomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia and.|Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia.|Australian Phenomics Facility, Australian National University, Canberra, ACT 2601, Australia.|Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia.|Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Immunogenomics Group, Immunology Research Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Female|Genetic Linkage|Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Heterozygote|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Male|Pedigree|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btv135', '25755272', 'Increasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant analysis of sequenced pedigrees (VASP), a flexible data integration environment capable of producing a summary of pedigree variation, providing relevant information such as compound heterozygosity, genome phasing and disease inheritance patterns. Designed to aggregate data across a sequenced pedigree, VASP allows both powerful filtering and custom prioritization of both single nucleotide variants (SNVs) and small indels. Hence, clinical and research users with prior knowledge of a disease are able to dramatically reduce the variant search space based on a wide variety of custom prioritization criteria.'),
(79104, 2015, 'bio', 'BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method', 'Shi, Xu|Barnes, Robert O|Chen, Li|Shajahan-Haq, Ayesha N|Hilakivi-Clarke, Leena|Clarke, Robert|Wang, Yue|Xuan, Jianhua', 'Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA and.|Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC 20057, USA.|Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC 20057, USA.|Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC 20057, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Breast Neoplasms|Female|Gene Expression|Humans|Markov Chains|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btv137', '25755273', 'Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence.'),
(79105, 2015, 'bio', 'Hi-Jack: a novel computational framework for pathway-based inference of host-pathogen interactions', 'Kleftogiannis, Dimitrios|Wong, Limsoon|Archer, John A C|Kalnis, Panos', 'Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Jeddah 23955-6900, Saudi Arabia.|School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore and.|Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Jeddah 23955-6900, Saudi Arabia, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Jeddah 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Jeddah 23955-6900, Saudi Arabia.', 'Saudi Arabia|Singapore|Saudi Arabia|Saudi Arabia', 3, 'Algorithms|Computational Biology|Host-Pathogen Interactions|Humans|Metabolic Networks and Pathways|Metabolomics|Mycobacterium tuberculosis|Proteins|Tuberculosis', 'Journal Article', '10.1093/bioinformatics/btv138', '25758402', 'Pathogens infect their host and hijack the host machinery to produce more progeny pathogens. Obligate intracellular pathogens, in particular, require resources of the host to replicate. Therefore, infections by these pathogens lead to alterations in the metabolism of the host, shifting in favor of pathogen protein production. Some computational identification of mechanisms of host-pathogen interactions have been proposed, but it seems the problem has yet to be approached from the metabolite-hijacking angle.'),
(79106, 2015, 'bio', 'miR2GO: comparative functional analysis for microRNAs', 'Bhattacharya, Anindya|Cui, Yan', 'Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.|Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.', 'United States|United States', 20, 'Gene Ontology|Humans|MicroRNAs|Molecular Sequence Annotation|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btv140', '25762653', 'miR2GO is a web-based platform for comparative analyses of human miRNA functions. It includes two programs: miRmut2GO and miRpair2GO. miRmut2GO implements a knowledge-based method to assess the functional effects of genetic and somatic mutations in microRNA seed regions. The functional effects of a mutation are analysed by semantic comparison of enriched gene ontology (GO) annotations of the target gene sets for the wild-type and mutated alleles. miRpair2GO compares the functions of two different miRNAs based on the enriched functional annotations of their target gene sets.'),
(79107, 2015, 'bio', 'MIRA: mutual information-based reporter algorithm for metabolic networks', 'Cicek, A Ercument|Roeder, Kathryn|Ozsoyoglu, Gultekin', 'Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.|Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.|Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.', 'United States|United States|United States', 0, '-', 'Published Erratum', '10.1093/bioinformatics/btv081', '25762654', '-'),
(79108, 2015, 'bio', 'MUGBAS: a species free gene-based programme suite for post-GWAS analysis', 'Capomaccio, S|Milanesi, M|Bomba, L|Vajana, E|Ajmone-Marsan, P', 'Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.|Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.|Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.|Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.|Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.', 'Italy|Italy|Italy|Italy|Italy', 4, 'Genetic Markers|Genome-Wide Association Study|Genotype|Humans|Molecular Sequence Annotation|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btv144', '25765345', 'Genome Wide Association Studies between molecular markers and phenotypes are now routinely run in model and non-model species. However, tools to estimate the probability of association of functional units (e.g. genes) containing multiple markers are not developed for species other than humans. Here we introduce MUGBAS (MUlti species Gene-Based Association Suite), software that estimates the P-value of a gene using information on annotation, single marker GWA results and genotype. The software is species and annotation independent, fast, highly parallelized and ready for high-density marker studies.'),
(79109, 2015, 'bio', 'Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein-protein interfaces', 'Vxc3xa1zquez, Miguel|Valencia, Alfonso|Pons, Tirso', 'Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.', 'Spain|Spain|Spain', 27, 'Databases, Protein|Genetic Variation|Humans|Molecular Sequence Annotation|Multiprotein Complexes|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btv142', '25765346', 'The interpretation of cancer-related single-nucleotide variants (SNVs) considering the protein features they affect, such as known functional sites, protein-protein interfaces, or relation with already annotated mutations, might complement the annotation of genetic variants in the analysis of NGS data. Current tools that annotate mutations fall short on several aspects, including the ability to use protein structure information or the interpretation of mutations in protein complexes.'),
(79110, 2015, 'bio', 'ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization', 'Yu, Guangchuang|Wang, Li-Gen|He, Qing-Yu', 'Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China, State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, China and.|Guangdong Information Center, Guangzhou 510031, China.|Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.', 'China|China|China', 353, 'Binding Sites|Chromatin Immunoprecipitation|Computer Graphics|DNA|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Sequence Analysis, DNA|Software|Transcription Factors|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btv145', '25765347', 'ChIPseeker is an R package for annotating ChIP-seq data analysis. It supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 15 000 bed file information from GEO database. These datasets can be downloaded and compare with users own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.'),
(79111, 2015, 'bio', 'VisualCNA: a GUI for interactive constraint network analysis and protein engineering for improving thermostability', 'Rathi, Prakash Chandra|Mulnaes, Daniel|Gohlke, Holger', 'Institute for Pharmaceutical and Medicinal Chemistry, Department of Mathematics and Natural Sciences, Heinrich-Heine-University, Universitätsstr. 1, Düsseldorf, Germany.|Institute for Pharmaceutical and Medicinal Chemistry, Department of Mathematics and Natural Sciences, Heinrich-Heine-University, Universitätsstr. 1, Düsseldorf, Germany.|Institute for Pharmaceutical and Medicinal Chemistry, Department of Mathematics and Natural Sciences, Heinrich-Heine-University, Universitätsstr. 1, Düsseldorf, Germany.', 'Germany|Germany|Germany', 8, 'Computer Graphics|Models, Molecular|Mutant Proteins|Protein Conformation|Protein Engineering|Protein Stability|Protein Unfolding|Software|Thermodynamics|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv139', '25770091', 'Constraint network analysis (CNA) is a graph theory-based rigidity analysis approach for linking a biomolecules structure, flexibility, (thermo)stability and function. Results from CNA are highly information-rich and require intuitive, synchronized and interactive visualization for a comprehensive analysis. We developed VisualCNA, an easy-to-use PyMOL plug-in that allows setup of CNA runs and analysis of CNA results linking plots with molecular graphics representations. From a practical viewpoint, the most striking feature of VisualCNA is that it facilitates interactive protein engineering aimed at improving thermostability.'),
(79112, 2015, 'bio', 'LBIBCell: a cell-based simulation environment for morphogenetic problems', 'Tanaka, Simon|Sichau, David|Iber, Dagmar', 'Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland and Swiss Institute of Bioinformatics, Basel, Switzerland.|Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland and.|Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland and Swiss Institute of Bioinformatics, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 21, 'Algorithms|Cell Communication|Cell Differentiation|Cell Division|Computer Simulation|Environment|Extracellular Fluid|Humans|Models, Theoretical|Morphogenesis|Software', 'Journal Article', '10.1093/bioinformatics/btv147', '25770313', 'The simulation of morphogenetic problems requires the simultaneous and coupled simulation of signalling and tissue dynamics. A cellular resolution of the tissue domain is important to adequately describe the impact of cell-based events, such as cell division, cell-cell interactions and spatially restricted signalling events. A tightly coupled cell-based mechano-regulatory simulation tool is therefore required.'),
(79113, 2015, 'bio', 'Computational approaches towards understanding human long non-coding RNA biology', 'Jalali, Saakshi|Kapoor, Shruti|Sivadas, Ambily|Bhartiya, Deeksha|Scaria, Vinod', 'GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and.|GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and.|GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and.|GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and.|GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110020, India and.', 'India|India|India|India|India', 31, 'Disease|Genome, Human|Genomics|Humans|Molecular Sequence Annotation|Neoplasms|RNA, Long Noncoding', 'Journal Article', '10.1093/bioinformatics/btv148', '25777523', 'Long non-coding RNAs (lncRNAs) form the largest class of non-protein coding genes in the human genome. While a small subset of well-characterized lncRNAs has demonstrated their significant role in diverse biological functions like chromatin modifications, post-transcriptional regulation, imprinting etc., the functional significance of a vast majority of them still remains an enigma. Increasing evidence of the implications of lncRNAs in various diseases including cancer and major developmental processes has further enhanced the need to gain mechanistic insights into the lncRNA functions. Here, we present a comprehensive review of the various computational approaches and tools available for the identification and annotation of long non-coding RNAs. We also discuss a conceptual roadmap to systematically explore the functional properties of the lncRNAs using computational approaches.'),
(79114, 2015, 'bio', 'WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools', 'Rackham, Owen J L|Dellaportas, Petros|Petretto, Enrico|Bottolo, Leonardo', 'Program in Cardiovascular & Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, MRC Clinical Sciences Centre, Imperial College London, UK.|Department of Statistics, Athens University of Economics and Business, Greece and.|Program in Cardiovascular & Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, MRC Clinical Sciences Centre, Imperial College London, UK.|Department of Mathematics, Imperial College London, UK.', 'Singapore|Greece|Singapore|United Kingdom', 10, 'Benchmarking|Computer Simulation|DNA Methylation|Genome, Human|Humans|Models, Statistical|Sequence Analysis, DNA|Software|Stochastic Processes|Sulfites', 'Journal Article', '10.1093/bioinformatics/btv114', '25777524', 'As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models.'),
(79115, 2015, 'bio', 'Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments', 'Bemis, Kyle D|Harry, April|Eberlin, Livia S|Ferreira, Christina|van de Ven, Stephanie M|Mallick, Parag|Stolowitz, Mark|Vitek, Olga', 'Department of Statistics and.|Department of Statistics and.|Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA.|Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA.|Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.|Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.|Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.|College of Science and College of Computer and Information Science, Northeastern University, Boston, MA 02115 USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Data Interpretation, Statistical|Image Processing, Computer-Assisted|Software|Spectrometry, Mass, Electrospray Ionization|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Swine', 'Journal Article', '10.1093/bioinformatics/btv146', '25777525', 'Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.'),
(79116, 2015, 'bio', 'Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes', 'Smaoui, Mohamed Raef|Orland, Henri|Waldispxc3xbchl, Jxc3xa9rxc3xb4me', 'School of Computer Science and McGill Center for Bioinformatics, McGill University, Montreal, QC, Canada and.|Institut de Physique Thxc3xa9orique, CEA-Saclay, 91191 Gif/Yvette Cedex, France.|School of Computer Science and McGill Center for Bioinformatics, McGill University, Montreal, QC, Canada and.', 'Canada|France|Canada', 3, 'Amyloid|Blood Glucose|Computational Biology|Diabetes Mellitus|Humans|Hypoglycemic Agents|Insulin|Insulin-Secreting Cells|Islet Amyloid Polypeptide|Mutation', 'Journal Article', '10.1093/bioinformatics/btv143', '25777526', 'Amyloids play a role in the degradation of xcexb2-cells in diabetes patients. In particular, short amyloid oligomers inject themselves into the membranes of these cells and create pores that disrupt the strictly controlled flow of ions through the membranes. This leads to cell death. Getting rid of the short oligomers either by a deconstruction process or by elongating them into longer fibrils will reduce this toxicity and allow the xcexb2-cells to live longer.'),
(79117, 2015, 'bio', 'SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds', 'Rudik, Anastasia|Dmitriev, Alexander|Lagunin, Alexey|Filimonov, Dmitry|Poroikov, Vladimir', 'Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia.|Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia.|Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia.|Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia.|Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, 119121, Russia and Medicobiological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., Moscow, 117997, Russia.', '-|-|-|-|-', 44, 'Algorithms|Bayes Theorem|Computational Biology|Cytochrome P-450 Enzyme System|Humans|Metabolic Networks and Pathways|Pharmaceutical Preparations|Software|Xenobiotics', 'Journal Article', '10.1093/bioinformatics/btv087', '25777527', 'A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of structure-SOM relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively.'),
(79118, 2015, 'bio', 'SiPAN: simultaneous prediction and alignment of protein-protein interaction networks', 'Alkan, Ferhat|Erten, Cesim', 'Center for Non-Coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grxc3xb8nnegardsvej 3, DK-1870 Frederiksberg, Denmark and Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083, Turkey.', 'Denmark|Turkey', 5, 'Algorithms|Computational Biology|Humans|Models, Biological|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btv160', '25788620', 'Network prediction as applied to protein-protein interaction (PPI) networks has received considerable attention within the last decade. Because of the limitations of experimental techniques for interaction detection and network construction, several computational methods for PPI network reconstruction and growth have been suggested. Such methods usually limit the scope of study to a single network, employing data based on genomic context, structure, domain, sequence information or existing network topology. Incorporating multiple species network data for network reconstruction and growth entails the design of novel models encompassing both network reconstruction and network alignment, since the goal of network alignment is to provide functionally orthologous proteins from multiple networks and such orthology information can be used in guiding interolog transfers. However, such an approach raises the classical chicken or egg problem, alignment methods assume error-free networks, whereas network prediction via orthology works affectively if the functionally orthologous proteins are determined with high precision. Thus to resolve this intertwinement, we propose a framework to handle both problems simultaneously, that of SImultaneous Prediction and Alignment of Networks (SiPAN).'),
(79119, 2015, 'bio', '4DGenome: a comprehensive database of chromatin interactions', 'Teng, Li|He, Bing|Wang, Jiahui|Tan, Kai', 'Department of Internal Medicine and.|Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, 52242, USA.|Department of Internal Medicine and.|Department of Internal Medicine and.', 'United States|United States|United States|United States', 51, 'Chromatin|Computational Biology|Databases, Factual|Gene Expression Regulation|Genome, Human|Genomics|Humans', 'Journal Article', '10.1093/bioinformatics/btv158', '25788621', 'The 3D structure of the genome plays a critical role in regulating gene expression. Recent progress in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data. This trend will continue as research in this burgeoning field intensifies.'),
(79120, 2015, 'bio', 'A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human', 'Liang, Cheng|Li, Yue|Luo, Jiawei|Zhang, Zhaolei', 'College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China and.|Department of Computer Science, Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China and.|Department of Computer Science, Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.', 'China|Canada|China|Canada', 19, 'Algorithms|Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Genome, Human|Humans|MicroRNAs|Regulatory Sequences, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv159', '25788622', 'Interplays between transcription factors (TFs) and microRNAs (miRNAs) in gene regulation are implicated in various physiological processes. It is thus important to identify biologically meaningful network motifs involving both types of regulators to understand the key co-regulatory mechanisms underlying the cellular identity and function. However, existing motif finders do not scale well for large networks and are not designed specifically for co-regulatory networks.'),
(79121, 2015, 'bio', 'Two-group comparisons of zero-inflated intensity values: the choice of test statistic matters', 'Gleiss, Andreas|Dakna, Mohammed|Mischak, Harald|Heinze, Georg', 'Center for Medical Statistics, Informatics and Intelligent Systems, Medical University Vienna, Austria, Vienna, Austria and.|Mosaiques Diagnostics and Therapeutics AG, Hannover, Germany.|Mosaiques Diagnostics and Therapeutics AG, Hannover, Germany.|Center for Medical Statistics, Informatics and Intelligent Systems, Medical University Vienna, Austria, Vienna, Austria and.', 'Austria|Germany|Germany|Austria', 9, 'Likelihood Functions|Models, Statistical|Peptides|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv154', '25788623', 'A special characteristic of data from molecular biology is the frequent occurrence of zero intensity values which can arise either by true absence of a compound or by a signal that is below a technical limit of detection.'),
(79122, 2015, 'bio', 'Crossfinder-assisted mapping of protein crosslinks formed by site-specifically incorporated crosslinkers', 'Mueller-Planitz, Felix', 'BioMedical Center, Ludwig-Maximilians-University, 80336 Munich, Germany.', 'Germany', 12, 'Amino Acids|Cross-Linking Reagents|Humans|Multiprotein Complexes|Proteins|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv083', '25788624', 'Protein crosslinking has been used for decades to derive structural information about proteins and protein complexes. Only recently, however, it became possible to map the amino acids involved in the crosslinks with the advent of high resolution mass spectrometry (MS). Here, we present Crossfinder, which automates the search for crosslinks formed by site-specifically incorporated crosslinking amino acids in LC-MS-MS data.'),
(79123, 2015, 'bio', 'Efficient visualization of high-throughput targeted proteomics experiments: TAPIR', 'Röst, Hannes L|Rosenberger, George|Aebersold, Ruedi|Malmström, Lars', 'ETH Zurich, Institute of Molecular Systems Biology, CH-8093 Zurich, Switzerland and Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, CH-8057 Zurich, Switzerland.|ETH Zurich, Institute of Molecular Systems Biology, CH-8093 Zurich, Switzerland and Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, CH-8057 Zurich, Switzerland.|ETH Zurich, Institute of Molecular Systems Biology, CH-8093 Zurich, Switzerland and.|ETH Zurich, Institute of Molecular Systems Biology, CH-8093 Zurich, Switzerland and.', 'Switzerland|Switzerland|Switzerland|Switzerland', 7, 'Computer Graphics|Mass Spectrometry|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv152', '25788625', 'Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required.'),
(79124, 2015, 'bio', 'Family genome browser: visualizing genomes with pedigree information', 'Juan, Liran|Liu, Yongzhuang|Wang, Yongtian|Teng, Mingxiang|Zang, Tianyi|Wang, Yadong', 'Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|China|China|China|China', 0, 'Computer Graphics|Genetic Variation|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btv151', '25788626', 'Families with inherited diseases are widely used in Mendelian/complex disease studies. Owing to the advances in high-throughput sequencing technologies, family genome sequencing becomes more and more prevalent. Visualizing family genomes can greatly facilitate human genetics studies and personalized medicine. However, due to the complex genetic relationships and high similarities among genomes of consanguineous family members, family genomes are difficult to be visualized in traditional genome visualization framework. How to visualize the family genome variants and their functions with integrated pedigree information remains a critical challenge.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79125, 2015, 'bio', 'Multiscale reaction-diffusion simulations with Smoldyn', 'Robinson, Martin|Andrews, Steven S|Erban, Radek', 'Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom and.|Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.|Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom and.', 'United Kingdom|United States|United Kingdom', 29, 'Algorithms|Computer Simulation|Diffusion|Models, Biological|Saccharomyces cerevisiae|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btv149', '25788627', 'Smoldyn is a software package for stochastic modelling of spatial biochemical networks and intracellular systems. It was originally developed with an accurate off-lattice particle-based model at its core. This has recently been enhanced with the addition of a computationally efficient on-lattice model, which can be run stand-alone or coupled together for multiscale simulations using both models in regions where they are most required, increasing the applicability of Smoldyn to larger molecule numbers and spatial domains. Simulations can switch between models with only small additions to their configuration file, enabling users with existing Smoldyn configuration files to run the new on-lattice model with any reaction, species or surface descriptions they might already have.'),
(79126, 2015, 'bio', 'Test set bias affects reproducibility of gene signatures', 'Patil, Prasad|Bachant-Winner, Pierre-Olivier|Haibe-Kains, Benjamin|Leek, Jeffrey T', 'Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA.|Institut de Recherches Cliniques de Montréal, Montreal, Quebec H2W 1R7, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada and Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.|Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA.', 'United States|Canada|Canada|United States', 41, 'Biomarkers, Tumor|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Middle Aged|Models, Statistical|Neoplasm Grading|Neoplasm Staging|Oligonucleotide Array Sequence Analysis|Prognosis|Receptor, ErbB-2|Receptors, Estrogen|Reproducibility of Results|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btv157', '25788628', 'Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that the test set data are comparable to the data upon which the predictor was trained. The most effective normalization methods depend on data from multiple patients. From a biomedical perspective, this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction.'),
(79127, 2015, 'bio', 'iTagPlot: an accurate computation and interactive drawing tool for tag density plot', 'Kim, Sung-Hwan|Ezenwoye, Onyeka|Cho, Hwan-Gue|Robertson, Keith D|Choi, Jeong-Hyeon', 'School of Computer Science and Engineering, Pusan National University, Busan, South Korea.|Hull College of Business, Georgia Regents University, Augusta, GA, USA.|School of Computer Science and Engineering, Pusan National University, Busan, South Korea.|Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.|Cancer Center and Department of Biostatistics and Epidemiology, Medical College of Georgia, Georgia Regents University, Augusta, GA, USA.', 'United States|Georgia|United States|United States|Georgia', 0, 'Cell Line|Cluster Analysis|Computer Graphics|CpG Islands|DNA Methylation|Gene Expression|High-Throughput Nucleotide Sequencing|Humans|Sequence Tagged Sites|Software', 'Journal Article', '10.1093/bioinformatics/btv166', '25792550', 'Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region.'),
(79128, 2015, 'bio', 'QSLiMFinder: improved short linear motif prediction using specific query protein data', 'Palopoli, Nicolas|Lythgow, Kieren T|Edwards, Richard J', 'Centre for Biological Sciences, University of Southampton, Southampton, UK.|Centre for Biological Sciences, University of Southampton, Southampton, UK, Public Health England, London, UK.|Centre for Biological Sciences, University of Southampton, Southampton, UK, Institute for Life Sciences, University of Southampton, Southampton, UK and School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.', 'United Kingdom|United Kingdom|Australia', 9, 'Algorithms|Amino Acid Motifs|Humans|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv155', '25792551', 'The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.'),
(79129, 2015, 'bio', 'MAGNA++: Maximizing Accuracy in Global Network Alignment via both node and edge conservation', 'Vijayan, V|Saraph, V|Milenkovixc4x87, T', 'Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Application, University of Notre Dame, IN 46556, USA and.|Department of Computer Science, Brown University, Providence, RI 02912, USA.|Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Application, University of Notre Dame, IN 46556, USA and.', 'United States|United States|United States', 70, 'Algorithms|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btv161', '25792552', 'Network alignment aims to find conserved regions between different networks. Existing methods aim to maximize total similarity over all aligned nodes (i.e. node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Thus, we recently introduced MAGNA (Maximizing Accuracy in Global Network Alignment) to directly maximize edge conservation while producing alignments and showed its superiority over the existing methods. Here, we extend the original MAGNA with several important algorithmic advances into a new MAGNA++ framework.'),
(79130, 2015, 'bio', 'A robust approach for identifying differentially abundant features in metagenomic samples', 'Sohn, Michael B|Du, Ruofei|An, Lingling', 'Interdisciplinary Program in Statistics and.|Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA.|Interdisciplinary Program in Statistics and Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA.', 'United States|United States|United States', 27, 'Diabetes Mellitus, Type 2|Humans|Metagenomics|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btv165', '25792553', 'The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large.'),
(79131, 2015, 'bio', 'Automatic determination of NET (neutrophil extracellular traps) coverage in fluorescent microscopy images', 'Coelho, Luis Pedro|Pato, Catarina|Friães, Ana|Neumann, Ariane|von Köckritz-Blickwede, Maren|Ramirez, Mário|Carriço, João André', 'Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.|Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and.', '-|-|-|-|-|-|-', 14, 'Algorithms|Extracellular Traps|Humans|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Neutrophils|Observer Variation|Pattern Recognition, Automated|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btv156', '25792554', 'Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases.'),
(79132, 2015, 'bio', 'Solubis: optimize your protein', 'De Baets, Greet|Van Durme, Joost|van der Kant, Rob|Schymkowitz, Joost|Rousseau, Frederic', 'VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and Vrije Universiteit Brussel, 1050 Brussels, Belgium.|VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and Vrije Universiteit Brussel, 1050 Brussels, Belgium.|VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and.|VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and.|VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 12, 'Databases, Protein|Humans|Mutation|Protein Conformation|Protein Folding|Protein Multimerization|Protein Stability|Proteins|Sequence Analysis, Protein|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btv162', '25792555', 'Protein aggregation is associated with a number of protein misfolding diseases and is a major concern for therapeutic proteins. Aggregation is caused by the presence of aggregation-prone regions (APRs) in the amino acid sequence of the protein. The lower the aggregation propensity of APRs and the better they are protected by native interactions within the folded structure of the protein, the more aggregation is prevented. Therefore, both the local thermodynamic stability of APRs in the native structure and their intrinsic aggregation propensity are a key parameter that needs to be optimized to prevent protein aggregation.'),
(79133, 2015, 'bio', 'Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancers', 'Orenstein, Yaron|Shamir, Ron', 'VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and.|VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and.', 'Belgium|Belgium', 1, '-', 'Published Erratum', '10.1093/bioinformatics/btv084', '25805721', '-'),
(79134, 2015, 'bio', 'Identification of cell types from single-cell transcriptomes using a novel clustering method', 'Xu, Chen|Su, Zhengchang', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States', 164, 'Algorithms|Animals|Cell Lineage|Cluster Analysis|Embryo, Mammalian|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Mice|Neoplasms|Programming Languages|Single-Cell Analysis|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv088', '25805722', 'The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes).'),
(79135, 2015, 'bio', 'EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles', 'Wang, Quan|Yu, Hui|Zhao, Zhongming|Jia, Peilin', 'Department of Biomedical Informatics.|Department of Biomedical Informatics.|Department of Biomedical Informatics, Center for Quantitative Sciences, Department of Psychiatry and Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, USA.|Department of Biomedical Informatics, Center for Quantitative Sciences.', 'United States|United States|United States|United States', 30, 'Algorithms|Breast Neoplasms|Female|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Protein Interaction Maps|Schizophrenia|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv150', '25805723', 'We previously developed dmGWAS to search for dense modules in a human protein-protein interaction (PPI) network, it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. Here, we introduce an upgraded algorithm, EW_dmGWAS, to boost GWAS signals in a node- and edge-weighted PPI network. In EW_dmGWAS, we utilize condition-specific gene expression profiles for edge weights. Specifically, differential gene co-expression is used to infer the edge weights. We applied EW_dmGWAS to two diseases and compared it with other relevant methods. The results suggest that EW_dmGWAS is more powerful in detecting disease-associated signals.'),
(79136, 2015, 'bio', 'ASBench: benchmarking sets for allosteric discovery', 'Huang, Wenkang|Wang, Guanqiao|Shen, Qiancheng|Liu, Xinyi|Lu, Shaoyong|Geng, Lv|Huang, Zhimin|Zhang, Jian', 'Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.|Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.', 'China|China|China|China|China|China|China|China', 49, 'Allosteric Regulation|Allosteric Site|Benchmarking|Drug Design|Glycogen Phosphorylase|Humans|Ligands|Software', 'Journal Article', '10.1093/bioinformatics/btv169', '25810427', 'Allostery allows for the fine-tuning of protein function. Targeting allosteric sites is gaining increasing recognition as a novel strategy in drug design. The key challenge in the discovery of allosteric sites has strongly motivated the development of computational methods and thus high-quality, publicly accessible standard data have become indispensable. Here, we report benchmarking data for experimentally determined allosteric sites through a complex process, including a Core set with 235 unique allosteric sites and a Core-Diversity set with 147 structurally diverse allosteric sites. These benchmarking sets can be exploited to develop efficient computational methods to predict unknown allosteric sites in proteins and reveal unique allosteric ligand-protein interactions to guide allosteric drug design.'),
(79137, 2015, 'bio', 'PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R', 'Grau, Jan|Grosse, Ivo|Keilwagen, Jens', 'Institute of Computer Science and Universitätszentrum Informatik, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.|Institute of Computer Science and Universitätszentrum Informatik, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany and.|Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, Germany.', 'Germany|Germany|Germany', 84, 'Area Under Curve|Computer Graphics|Data Interpretation, Statistical|Humans|Mathematical Computing|Pattern Recognition, Automated|ROC Curve|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv153', '25810428', 'Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation between the points of PR curves. In addition, PRROC provides a generic plot function for generating publication-quality graphics of PR and ROC curves.'),
(79138, 2015, 'bio', 'DISSCO: direct imputation of summary statistics allowing covariates', 'Xu, Zheng|Duan, Qing|Yan, Song|Chen, Wei|Li, Mingyao|Lange, Ethan|Li, Yun', 'Department of Biostatistics, Department of Genetics, Department of Computer Science.|Department of Genetics, Curriculum in Bioinformatics and Computational Biology, Department of Statistics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science.|Division of Pediatric Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Childrens Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Department of Biostatistics, Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, PA 15224, USA and.|Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, Department of Genetics.|Department of Biostatistics, Department of Genetics, Department of Computer Science.', 'United States|United States|United States|United States|United States|United States|United States', 11, 'Data Interpretation, Statistical|Female|Gene Frequency|Genetic Association Studies|Genotype|Humans|Normal Distribution', 'Journal Article', '10.1093/bioinformatics/btv168', '25810429', 'Imputation of individual level genotypes at untyped markers using an external reference panel of genotyped or sequenced individuals has become standard practice in genetic association studies. Direct imputation of summary statistics can also be valuable, for example in meta-analyses where individual level genotype data are not available. Two methods (DIST and ImpG-Summary/LD), that assume a multivariate Gaussian distribution for the association summary statistics, have been proposed for imputing association summary statistics. However, both methods assume that the correlations between association summary statistics are the same as the correlations between the corresponding genotypes. This assumption can be violated in the presence of confounding covariates.'),
(79139, 2015, 'bio', 'Learning chromatin states with factorized information criteria', 'Hamada, Michiaki|Ono, Yukiteru|Fujimaki, Ryohei|Asai, Kiyoshi', 'Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan, Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan.|Information and Mathematical Science and Bioinformatics Co., Ltd., 4-21-1-601 Higashi-Ikebukuro, Toshima-ku, Tokyo 170-0013, Japan.|Department of Media Analytics, NEC Laboratories America, 10080 North Wolfe Road, Suite SW3-350, Cupertino, CA 95014 and.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8562, Japan.', 'Japan|Japan|-|Japan', 6, 'Chromatin|Epigenesis, Genetic|Epigenomics|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Nucleosomes', 'Journal Article', '10.1093/bioinformatics/btv163', '25810430', 'Recent studies have suggested that both the genome and the genome with epigenetic modifications, the so-called epigenome, play important roles in various biological functions, such as transcription and DNA replication, repair, and recombination. It is well known that specific combinations of histone modifications (e.g. methylations and acetylations) of nucleosomes induce chromatin states that correspond to specific functions of chromatin. Although the advent of next-generation sequencing (NGS) technologies enables measurement of epigenetic information for entire genomes at high-resolution, the variety of chromatin states has not been completely characterized.'),
(79140, 2015, 'bio', 'Proper evaluation of alignment-free network comparison methods', 'Yaveroxc4x9flu, xc3x96mer Nebil|Milenkovixc4x87, Tijana|Prxc5xbeulj, Nataxc5xa1a', 'California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, CA 92697, USA.|Department of Computer Science and Engineering, University of Notre Dame, IN 46556, USA and.|Department of Computing, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 32, 'Algorithms|Cluster Analysis|Gene Regulatory Networks|Humans|Phylogeny|Protein Interaction Mapping|Reference Standards|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/btv170', '25810431', 'Network comparison is a computationally intractable problem with important applications in systems biology and other domains. A key challenge is to properly quantify similarity between wiring patterns of two networks in an alignment-free fashion. Also, alignment-based methods exist that aim to identify an actual node mapping between networks and as such serve a different purpose. Various alignment-free methods that use different global network properties (e.g. degree distribution) have been proposed. Methods based on small local subgraphs called graphlets perform the best in the alignment-free network comparison task, due to high level of topological detail that graphlets can capture. Among different graphlet-based methods, Graphlet Correlation Distance (GCD) was shown to be the most accurate for comparing networks. Recently, a new graphlet-based method called NetDis was proposed, which was claimed to be superior. We argue against this, as the performance of NetDis was not properly evaluated to position it correctly among the other alignment-free methods.'),
(79141, 2015, 'bio', 'Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival', 'Suo, Chen|Hrydziuszko, Olga|Lee, Donghwan|Pramana, Setia|Saputra, Dhany|Joshi, Himanshu|Calza, Stefano|Pawitan, Yudi', 'School of Life Sciences, Peking University, Beijing, China, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Statistics, Ewha Womans University, Seoul, South Korea.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Computational Statistics, Institute of Statistics, Jakarta, Indonesia and.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.', 'China|Sweden|-|Indonesia|Sweden|Sweden|Italy|Sweden', 30, 'Adult|Aged|Aged, 80 and over|Biomarkers, Tumor|Breast|Breast Neoplasms|Carcinoma, Ductal, Breast|Case-Control Studies|Computational Biology|Exome|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genetic Predisposition to Disease|Genomics|High-Throughput Nucleotide Sequencing|Humans|Middle Aged|Mutation|Neoplasm Invasiveness|Neoplasm Staging|Prognosis|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btv164', '25810432', 'Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures.'),
(79142, 2015, 'bio', 'Computational modeling of the expansion of human cord blood CD133+ hematopoietic stem/progenitor cells with different cytokine combinations', 'Gullo, Francesca|van der Garde, Mark|Russo, Giulia|Pennisi, Marzio|Motta, Santo|Pappalardo, Francesco|Watt, Suzanne', 'Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Department of Drug Science and.|Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.|Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.|Department of Drug Science and.|Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.', 'United Kingdom|United Kingdom|United Kingdom|Italy|Italy|United Kingdom|United Kingdom', 22, 'Antigens, CD34|Cell Differentiation|Cell Survival|Cells, Cultured|Computational Biology|Computer Simulation|Cytokines|Fetal Blood|Hematopoietic Stem Cells|Humans', 'Journal Article', '10.1093/bioinformatics/btv172', '25810433', 'Many important problems in cell biology require dense non-linear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. Expansion of hematopoietic stem and progenitor cells (HSC/HPC) in ex vivo culture with cytokines and small molecules is a method to increase the restricted numbers of stem cells found in umbilical cord blood (CB), while also enhancing the content of early engrafting neutrophil and platelet precursors. The efficacy of the expanded product depends on the composition of the cocktail of cytokines and small molecules used for culture. Testing the influence of a cytokine or small molecule on the expansion of HSC/HPC is a laborious and expensive process. We therefore developed a computational model based on cellular signaling interactions that predict the influence of a cytokine on the survival, duplication and differentiation of the CD133(+) HSC/HPC subset from human umbilical CB.'),
(79143, 2015, 'bio', 'A statistical physics perspective on alignment-independent protein sequence comparison', 'Chattopadhyay, Amit K|Nasiev, Diar|Flower, Darren R', 'School of Engineering and Applied Science, Nonlinearity and Complexity Research Group and.|School of Engineering and Applied Science, Nonlinearity and Complexity Research Group and.|School of Life and Health Sciences, University of Aston, Aston Triangle, Birmingham, UK.', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Algorithms|Data Interpretation, Statistical|Physics|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btv167', '25810434', 'Within bioinformatics, the textual alignment of amino acid sequences has long dominated the determination of similarity between proteins, with all that implies for shared structure, function and evolutionary descent. Despite the relative success of modern-day sequence alignment algorithms, so-called alignment-free approaches offer a complementary means of determining and expressing similarity, with potential benefits in certain key applications, such as regression analysis of protein structure-function studies, where alignment-base similarity has performed poorly.'),
(79144, 2015, 'bio', 'MeDuSa: a multi-draft based scaffolder', 'Bosi, Emanuele|Donati, Beatrice|Galardini, Marco|Brunetti, Sara|Sagot, Marie-France|Lió, Pietro|Crescenzi, Pierluigi|Fani, Renato|Fondi, Marco', 'Department of Biology, ComBo, Florence Computational Biology Group, Department of Biology, LEMM, Laboratory of Microbial and Molecular Evolution Florence, University of Florence, I-50019 Sesto F.no, Italy.|INRIA Rhône-Alpes, Villeurbanne Cedex, France, Université de Lyon, F-69000 Lyon, France, Dipartimento di Ingegneria dellInformazione, University of Florence, I-50139 Firenze, Italy.|EMBL-EBI - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD Cambridge, UK.|Dipartimento di Ingegneria dellInformazione e Scienze Matematiche, University of Siena, Siena I-53100, Italy.|INRIA Rhône-Alpes, Villeurbanne Cedex, France, Université de Lyon, F-69000 Lyon, France, Université Lyon 1, CNRS,UMR5558, 69622 Villeurbanne Cedex, France and.|Computer Laboratory, University of Cambridge, CB3 0FD Cambridge, UK.|Dipartimento di Ingegneria dellInformazione, University of Florence, I-50139 Firenze, Italy.|Department of Biology, ComBo, Florence Computational Biology Group, Department of Biology, LEMM, Laboratory of Microbial and Molecular Evolution Florence, University of Florence, I-50019 Sesto F.no, Italy.|Department of Biology, ComBo, Florence Computational Biology Group, Department of Biology, LEMM, Laboratory of Microbial and Molecular Evolution Florence, University of Florence, I-50019 Sesto F.no, Italy.', 'Italy|France|United Kingdom|Italy|France|United Kingdom|Italy|Italy|Italy', 115, 'Algorithms|Contig Mapping|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btv171', '25810435', 'Completing the genome sequence of an organism is an important task in comparative, functional and structural genomics. However, this remains a challenging issue from both a computational and an experimental viewpoint. Genome scaffolding (i.e. the process of ordering and orientating contigs) of de novo assemblies usually represents the first step in most genome finishing pipelines.'),
(79145, 2015, 'bio', 'HAlign: Fast multiple similar DNA/RNA sequence alignment based on the centre star strategy', 'Zou, Quan|Hu, Qinghua|Guo, Maozu|Wang, Guohua', 'School of Computer Science and Technology, Tianjin University, Tianjin, China, State Key Laboratory of System Bioengineering of the Ministry of Education, Tianjin University, Tianjin, China and.|School of Computer Science and Technology, Tianjin University, Tianjin, China, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.', 'China|China|China|China', 105, 'Algorithms|Humans|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv177', '25812743', 'Multiple sequence alignment (MSA) is important work, but bottlenecks arise in the massive MSA of homologous DNA or genome sequences. Most of the available state-of-the-art software tools cannot address large-scale datasets, or they run rather slowly. The similarity of homologous DNA sequences is often ignored. Lack of parallelization is still a challenge for MSA research.'),
(79146, 2015, 'bio', 'RiboTools: a Galaxy toolbox for qualitative ribosome profiling analysis', 'Legendre, Rachel|Baudin-Baillieu, Agnès|Hatin, Isabelle|Namy, Olivier', 'Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, Bat 400, 91400 Orsay, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, Bat 400, 91400 Orsay, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, Bat 400, 91400 Orsay, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, Bat 400, 91400 Orsay, France.', 'France|France|France|France', 0, 'Codon, Terminator|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Genome, Fungal|High-Throughput Nucleotide Sequencing|Humans|Prions|Protein Biosynthesis|Ribosomes|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btv174', '25812744', 'Ribosome profiling provides genome-wide information about translational regulation. However, there is currently no standard tool for the qualitative analysis of Ribo-seq data. We present here RiboTools, a Galaxy toolbox for the analysis of ribosome profiling (Ribo-seq) data. It can be used to detect translational ambiguities, stop codon readthrough events and codon occupancy. It provides a large number of plots for the visualisation of these events.'),
(79147, 2015, 'bio', 'KeBABS: an R package for kernel-based analysis of biological sequences', 'Palme, Johannes|Hochreiter, Sepp|Bodenhofer, Ulrich', 'Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.', 'Austria|Austria|Austria', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|HLA-A2 Antigen|Humans|Models, Theoretical|Peptide Fragments|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv176', '25812745', 'KeBABS provides a powerful, flexible and easy to use framework for KE: rnel- B: ased A: nalysis of B: iological S: equences in R. It includes efficient implementations of the most important sequence kernels, also including variants that allow for taking sequence annotations and positional information into account. KeBABS seamlessly integrates three common support vector machine (SVM) implementations with a unified interface. It allows for hyperparameter selection by cross validation, nested cross validation and also features grouped cross validation. The biological interpretation of SVM models is supported by (1) the computation of weights of sequence patterns and (2) prediction profiles that highlight the contributions of individual sequence positions or sections.'),
(79148, 2015, 'bio', 'Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimers disease', 'Agarwal, Swapna|Ghanty, Pradip|Pal, Nikhil R', 'Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta 700108 and.|Praxis Softek Solutions Pvt. Ltd., Saltlake Electronic Complex, Calcutta 700091, India.|Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta 700108 and.', 'India|India|India', 12, 'Alzheimer Disease|Biomarkers|Blood Proteins|Brain|Cognitive Dysfunction|Enzyme-Linked Immunosorbent Assay|Humans|Neural Networks (Computer)|Principal Component Analysis', 'Journal Article', '10.1093/bioinformatics/btv173', '25819077', 'Alzheimers disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is xe2x88xbc7 years. In 2006, researchers estimated that 0.40% of the world population (range 0.17-0.89%) was afflicted by AD, and that the prevalence rate would be tripled by 2050. Usually, examination of brain tissues is required for definite diagnosis of AD. So, it is crucial to diagnose AD at an early stage via some alternative methods. As the brain controls many functions via releasing signalling proteins through blood, we analyse blood plasma proteins for diagnosis of AD.'),
(79149, 2015, 'bio', 'Halvade: scalable sequence analysis with MapReduce', 'Decap, Dries|Reumers, Joke|Herzeel, Charlotte|Costanza, Pascal|Fostier, Jan', 'Department of Information Technology, Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium, ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium.|ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium, Janssen Research & Development, a division of Janssen Pharmaceutica N.V., 2340 Beerse, Belgium.|ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium, Imec, Kapeldreef 75, 3001 Leuven, Belgium, and.|ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium, Intel Corporation Belgium.|Department of Information Technology, Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium, ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Genome, Human|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv179', '25819078', 'Post-sequencing DNA analysis typically consists of read mapping followed by variant calling. Especially for whole genome sequencing, this computational step is very time-consuming, even when using multithreading on a multi-core machine.'),
(79150, 2015, 'bio', 'DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method', 'Amgalan, Bayarbaatar|Lee, Hyunju', 'School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.|School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.', '-|-', 0, 'Breast Neoplasms|DNA-Binding Proteins|Female|Gene Dosage|Gene Expression|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Dominant|Genes, Neoplasm|Genes, myc|Genomics|Humans|Models, Statistical|Mutation|Oncogenes|Survival Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv175', '25819079', 'The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.'),
(79151, 2015, 'bio', 'MSA-PAD: DNA multiple sequence alignment framework based on PFAM accessed domain information', 'Balech, Bachir|Vicario, Saverio|Donvito, Giacinto|Monaco, Alfonso|Notarangelo, Pasquale|Pesole, Graziano', 'Istituto di Biomembrane e Bioenergetica and.|Istituto Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, 70126 Bari, Italy.|Istituto Nazionale di Fisica Nucleare, 70126 Bari, Italy and.|Istituto Nazionale di Fisica Nucleare, 70126 Bari, Italy and.|Istituto Nazionale di Fisica Nucleare, 70126 Bari, Italy and.|Istituto di Biomembrane e Bioenergetica and Dipartimento Bioscienze, Biotecnologie e Biofarmaceutica, University of Bari, 70126 Bari, Italy.', '-|Italy|Italy|Italy|Italy|Italy', 0, 'DNA|Databases, Factual|Genome, Human|Humans|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv141', '25819080', 'Here we present the MSA-PAD application, a DNA multiple sequence alignment framework that uses PFAM protein domain information to align DNA sequences encoding either single or multiple protein domains. MSA-PAD has two alignment options: gene and genome mode.'),
(79152, 2015, 'bio', 'Assessing allele-specific expression across multiple tissues from RNA-seq read data', 'Pirinen, Matti|Lappalainen, Tuuli|Zaitlen, Noah A|GTEx Consortium|Dermitzakis, Emmanouil T|Donnelly, Peter|McCarthy, Mark I|Rivas, Manuel A', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.|Department of Genetic Medicine and Development and, Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland, Swiss Institute of Bioinformatics, Geneva, Switzerland, Department of Genetics, Stanford University, Palo Alto, CA, USA, New York Genome Center, New York, NY, USA, Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Medicine, University of California, San Francisco, CA, USA.|Department of Medicine, University of California, San Francisco, CA, USA.|Department of Genetic Medicine and Development and, Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland, Swiss Institute of Bioinformatics, Geneva, Switzerland.|Wellcome Trust Centre for Human Genetics and Department of Statistics, University of Oxford, Oxford, UK and.|Wellcome Trust Centre for Human Genetics and Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, UK.|Wellcome Trust Centre for Human Genetics and.', 'Finland|Switzerland|United Kingdom|United Kingdom|Switzerland|United Kingdom|United Kingdom|United Kingdom', 26, 'Alleles|Extracellular Matrix Proteins|High-Throughput Nucleotide Sequencing|Humans|Lipoid Proteinosis of Urbach and Wiethe|Organ Specificity|Polymorphism, Single Nucleotide|Protein Isoforms|RNA', 'Journal Article', '10.1093/bioinformatics/btv074', '25819081', 'RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data.'),
(79153, 2015, 'bio', 'bio-samtools 2: a package for analysis and visualization of sequence and alignment data with SAMtools in Ruby', 'Etherington, Graham J|Ramirez-Gonzalez, Ricardo H|MacLean, Dan', 'The Sainsbury Laboratory and.|The Genome Analysis Centre, Norwich Research Park, Norwich NR4 7UH, UK.|The Sainsbury Laboratory and.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Electronic Data Processing|Gene Frequency|Genomics|Humans|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv178', '25819670', 'bio-samtools is a Ruby language interface to SAMtools, the highly popular library that provides utilities for manipulating high-throughput sequence alignments in the Sequence Alignment/Map format. Advances in Ruby, now allow us to improve the analysis capabilities and increase bio-samtools utility, allowing users to accomplish a large amount of analysis using a very small amount of code. bio-samtools can also be easily developed to include additional SAMtools methods and hence stay current with the latest SAMtools releases.'),
(79154, 2015, 'bio', 'Outlier detection at the transcriptome-proteome interface', 'Gunawardana, Yawwani|Fujiwara, Shuhei|Takeda, Akiko|Woo, Jeongmin|Woelk, Christopher|Niranjan, Mahesan', 'School of Electronics and Computer Science, University of Southampton, Southampton, UK.|Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan and.|Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan and.|Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK.|Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK.|School of Electronics and Computer Science, University of Southampton, Southampton, UK.', 'United Kingdom|Japan|Japan|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biomarkers|Computational Biology|Gene Expression Regulation, Fungal|Genome, Fungal|Proteome|RNA, Messenger|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv182', '25819671', 'In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation.'),
(79155, 2015, 'bio', 'Global optimization-based inference of chemogenomic features from drug-target interactions', 'Zu, Songpeng|Chen, Ting|Li, Shao', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.', 'China|China|China', 0, 'Algorithms|Data Mining|Databases, Factual|Drug Delivery Systems|Drug Design|Drug Discovery|Drug Interactions|Drug Repositioning|Humans|Pharmaceutical Preparations|Protein Binding|Protein Interaction Domains and Motifs|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btv181', '25819672', 'Gaining insight into chemogenomic drug-target interactions, such as those involving the substructures of synthetic drugs and protein domains, is important in fragment-based drug discovery and drug repositioning. Previous studies evaluated the interactions locally, thereby ignoring the competitive effects of different substructures or domains, but this could lead to high false-positive estimation, calling for a computational method that presents more predictive power.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79156, 2015, 'bio', 'Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences', 'Aguilera-Mendoza, Longendri|Marrero-Ponce, Yovani|Tellez-Ibarra, Roberto|Llorente-Quesada, Monica T|Salgado, Jesxc3xbas|Barigye, Stephen J|Liu, Jun', 'Grupo de Investigacixc3xb3n de Bioinformxc3xa1tica, Centro de Estudio de Matemxc3xa1tica Computacional, Universidad de las Ciencias Informxc3xa1ticas, La Habana, Cuba.|Grupo de Investigacixc3xb3n en Estudios Quxc3xadmicos y Biolxc3xb3gicos, Facultad de Ciencias Bxc3xa1sicas, Universidad Tecnolxc3xb3gica de Bolxc3xadvar, Cartagena de Indias, Bolxc3xadvar, Colombia, Facultad de Quxc3xadmica Farmacxc3xa9utica, Universidad de Cartagena, Cartagena de Indias, Bolxc3xadvar, Colombia, Instituto de Ciencia Molecular (ICMol), Universitat de Valxc3xa8ncia, C/ Catedrxc3xa1tico Josxc3xa9 Beltrxc3xa1n, 2, 46980, Paterna (Valencia), Spain.|Grupo de Investigacixc3xb3n de Bioinformxc3xa1tica, Centro de Estudio de Matemxc3xa1tica Computacional, Universidad de las Ciencias Informxc3xa1ticas, La Habana, Cuba.|Grupo de Investigacixc3xb3n de Bioinformxc3xa1tica, Centro de Estudio de Matemxc3xa1tica Computacional, Universidad de las Ciencias Informxc3xa1ticas, La Habana, Cuba.|Instituto de Ciencia Molecular (ICMol), Universitat de Valxc3xa8ncia, C/ Catedrxc3xa1tico Josxc3xa9 Beltrxc3xa1n, 2, 46980, Paterna (Valencia), Spain.|Departamento de Quxc3xadmica, Universidade Federal de Lavras, UFLA Caixa Postal 3037, 37200-000 Lavras, MG, Brazil and.|School of Computing and Mathematics, Faculty of Computing and Engineering, Ulster University, Jordanstown campus, Northern Ireland, UK.', 'Cuba|Colombia|Cuba|Cuba|Spain|Brazil|Ireland', 19, 'Algorithms|Antimicrobial Cationic Peptides|Databases, Nucleic Acid|Databases, Protein|Humans|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv180', '25819673', 'The large variety of antimicrobial peptide (AMP) databases developed to date are characterized by a substantial overlap of data and similarity of sequences. Our goals are to analyze the levels of redundancy for all available AMP databases and use this information to build a new non-redundant sequence database. For this purpose, a new software tool is introduced.'),
(79157, 2015, 'bio', 'Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA', 'Kelly, Brendan J|Gross, Robert|Bittinger, Kyle|Sherrill-Mix, Scott|Lewis, James D|Collman, Ronald G|Bushman, Frederic D|Li, Hongzhe', 'Department of Medicine.|Department of Medicine.|Department of Microbiology and.|Department of Microbiology and.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Medicine.|Department of Microbiology and.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 103, 'Humans|Microbiota|Models, Statistical|Multivariate Analysis|Sample Size|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv183', '25819674', 'The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA.'),
(79158, 2015, 'bio', 'ExaML version 3: a tool for phylogenomic analyses on supercomputers', 'Kozlov, Alexey M|Aberer, Andre J|Stamatakis, Alexandros', 'Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany and.|Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany and.|Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany and Department of informatics, Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.', 'Germany|Germany|Germany', 119, 'Algorithms|Computer Simulation|Computers|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Phylogeny|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv184', '25819675', 'Phylogenies are increasingly used in all fields of medical and biological research. Because of the next generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. We present ExaML version 3, a dedicated production-level code for inferring phylogenies on whole-transcriptome and whole-genome alignments using supercomputers.'),
(79159, 2015, 'bio', 'do_x3dna: a tool to analyze structural fluctuations of dsDNA or dsRNA from molecular dynamics simulations', 'Kumar, Rajendra|Grubmüller, Helmut', 'Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen 37077, Germany.|Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen 37077, Germany.', 'Germany|Germany', 28, 'DNA|Humans|Molecular Dynamics Simulation|Nucleosomes|RNA, Double-Stranded|Software', 'Journal Article', '10.1093/bioinformatics/btv190', '25838463', 'The do_x3dna package has been developed to analyze the structural fluctuations of DNA or RNA during molecular dynamics simulations. It extends the capability of the 3DNA package to GROMACS MD trajectories and includes new methods to calculate the global-helical axis of DNA and bending fluctuations during simulations. The package also includes a Python module dnaMD to perform and visualize statistical analyses of complex data obtained from the trajectories.'),
(79160, 2015, 'bio', 'Using neighborhood cohesiveness to infer interactions between protein domains', 'Segura, Joan|Sorzano, C O S|Cuenca-Alba, Jesus|Aloy, Patrick|Carazo, J M', 'GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), c/ Darwin no 3, Campus of Cantoblanco, 28049, Madrid, Spain.|GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), c/ Darwin no 3, Campus of Cantoblanco, 28049, Madrid, Spain.|GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), c/ Darwin no 3, Campus of Cantoblanco, 28049, Madrid, Spain.|Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), c/ Baldiri Reixac 10-12, 08028, Barcelona, Spain and Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010, Barcelona, Spain.|GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), c/ Darwin no 3, Campus of Cantoblanco, 28049, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Gene Expression Regulation|Humans|Protein Interaction Mapping|Protein Interaction Maps|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btv188', '25838464', 'In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis.'),
(79161, 2015, 'bio', 'SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics', 'Will, Sebastian|Otto, Christina|Miladi, Milad|Möhl, Mathias|Backofen, Rolf', 'Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany, Bioinformatics, Department of Computer Science, University of Leipzig, Leipzig, Germany.|Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany, Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany, Centre for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen, Denmark and Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 20, 'Algorithms|Heuristics|RNA Folding|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv185', '25838465', 'RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of [Formula: see text]. Subsequently, numerous faster Sankoff-style approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments, however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity ([Formula: see text] quartic time).'),
(79162, 2015, 'bio', 'DREAM: a webserver for the identification of editing sites in mature miRNAs using deep sequencing data', 'Alon, Shahar|Erew, Muhammad|Eisenberg, Eli', 'Department of Neurobiology, George S. Wise Faculty of Life Sciences, Sagol School of Neuroscience and.|Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.|Sagol School of Neuroscience and Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.', '-|Israel|Israel', 13, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btv187', '25840043', 'detecting RNA editing associated with microRNAs, is a webserver for the identification of mature microRNA editing events using deep sequencing data. Raw microRNA sequencing reads can be provided as input, the reads are aligned against the genome and custom scripts process the data, search for potential editing sites and assess the statistical significance of the findings. The output is a text file with the location and the statistical description of all the putative editing sites detected.'),
(79163, 2015, 'bio', 'Regulatory network inferred using expression data of small sample size: application and validation in erythroid system', 'Zhu, Fan|Shi, Lihong|Engel, James Douglas|Guan, Yuanfang', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China.|Department of Cell and Developmental Biology.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Internal Medicine, and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|China|United States|United States', 8, 'Algorithms|Cell Differentiation|Cell Lineage|Cells, Cultured|Chromatin Immunoprecipitation|Erythroid Cells|Erythropoiesis|Gene Expression Regulation|Gene Regulatory Networks|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Nuclear Receptor Subfamily 2, Group C, Member 2|Prions|ROC Curve|Sample Size|Stem Cells|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv186', '25840044', 'Modeling regulatory networks using expression data observed in a differentiation process may help identify context-specific interactions. The outcome of the current algorithms highly depends on the quality and quantity of a single time-course dataset, and the performance may be compromised for datasets with a limited number of samples.'),
(79164, 2015, 'bio', 'Three minimal sequences found in Ebola virus genomes and absent from human DNA', 'Silva, Raquel M|Pratas, Diogo|Castro, Luxc3xadsa|Pinho, Armando J|Ferreira, Paulo J S G', 'IEETA and.|IEETA and DETI, University of Aveiro, Campus Universitxc3xa1rio de Santiago, 3810-193 Aveiro, Portugal.|IEETA and.|IEETA and DETI, University of Aveiro, Campus Universitxc3xa1rio de Santiago, 3810-193 Aveiro, Portugal.|IEETA and DETI, University of Aveiro, Campus Universitxc3xa1rio de Santiago, 3810-193 Aveiro, Portugal.', '-|Portugal|-|Portugal|Portugal', 21, 'DNA, Viral|Disease Outbreaks|Ebolavirus|Genome, Human|Genome, Viral|Hemorrhagic Fever, Ebola|Humans|Sequence Analysis, DNA|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btv189', '25840045', 'Ebola virus causes high mortality hemorrhagic fevers, with more than 25xe2x80x89000 cases and 10xe2x80x89000 deaths in the current outbreak. Only experimental therapies are available, thus, novel diagnosis tools and druggable targets are needed.'),
(79165, 2015, 'bio', 'MetaMapR: pathway independent metabolomic network analysis incorporating unknowns', 'Grapov, Dmitry|Wanichthanarak, Kwanjeera|Fiehn, Oliver', 'National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and.|National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and.|National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and King Abdulaziz University, Biochemistry Department, Jeddah, Saudi Arabia.', '-|-|Saudi Arabia', 67, 'Metabolic Networks and Pathways|Metabolomics|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv194', '25847005', 'Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools.'),
(79166, 2015, 'bio', 'Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data', 'Gusnanto, Arief|Tcherveniakov, Peter|Shuweihdi, Farag|Samman, Manar|Rabbitts, Pamela|Wood, Henry M', 'Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK.|Department of Thoracic Surgery, St. James Hospital, Leeds, LS9 7TF, UK.|Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK.|Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and King Fahad Medical City, Riyadh, Saudi Arabia.|Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and.|Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and.', 'United Kingdom|United Kingdom|United Kingdom|Saudi Arabia|United Kingdom|United Kingdom', 6, 'DNA Copy Number Variations|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Logistic Models|Lung Neoplasms|Models, Biological|Mouth Neoplasms|Neoplasms|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv191', '25847006', 'The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data.'),
(79167, 2015, 'bio', 'EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments', 'Leng, Ning|Li, Yuan|McIntosh, Brian E|Nguyen, Bao Kim|Duffin, Bret|Tian, Shulan|Thomson, James A|Dewey, Colin N|Stewart, Ron|Kendziorski, Christina', 'Department of Statistics, University of Wisconsin, Madison, WI, USA, Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Department of Statistics, University of Wisconsin, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA, Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA and.|Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.|Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 34, 'Bayes Theorem|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv193', '25847007', 'With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data.'),
(79168, 2015, 'bio', 'Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach', 'Ghadie, Mohamed A|Japkowicz, Nathalie|Perkins, Theodore J', 'School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Avenue East, Ottawa, ON K1N 6N5, Canada, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada and.|School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Avenue East, Ottawa, ON K1N 6N5, Canada.|School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Avenue East, Ottawa, ON K1N 6N5, Canada, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada and Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada.', 'Canada|Canada|Canada', 1, 'Blood Cells|Cell Differentiation|Cluster Analysis|Gene Expression Regulation|Genes|Humans|Programming, Linear|Stem Cells', 'Journal Article', '10.1093/bioinformatics/btv192', '25847008', 'Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself.'),
(79169, 2015, 'bio', 'PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels', 'Choi, Yongwook|Chan, Agnes P', 'The J. Craig Venter Institute, Rockville, MD 20850, USA.|The J. Craig Venter Institute, Rockville, MD 20850, USA.', 'United States|United States', 681, 'Amino Acid Substitution|Animals|Genetic Variation|Genome|Humans|INDEL Mutation|Internet|Mice|Proteins|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btv195', '25851949', 'We present a web server to predict the functional effect of single or multiple amino acid substitutions, insertions and deletions using the prediction tool PROVEAN. The server provides rapid analysis of protein variants from any organisms, and also supports high-throughput analysis for human and mouse variants at both the genomic and protein levels.'),
(79170, 2015, 'bio', 'GS-align for glycan structure alignment and similarity measurement', 'Lee, Hui Sun|Jo, Sunhwan|Mukherjee, Srayanta|Park, Sang-Jun|Skolnick, Jeffrey|Lee, Jooyoung|Im, Wonpil', 'Department of Molecular Biosciences and Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA.|Department of Molecular Biosciences and Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA.|Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA.|School of Computational Sciences and Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul 130-722, Korea and.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30076, USA.|School of Computational Sciences and Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul 130-722, Korea and.|Department of Molecular Biosciences and Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA.', 'United States|United States|United States|United States|Georgia|United States|United States', 6, 'Carbohydrate Conformation|Carbohydrate Sequence|Computational Biology|Glycoproteins|Humans|Molecular Sequence Data|Polysaccharides|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv202', '25857669', 'Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner.'),
(79171, 2015, 'bio', 'Cellular phenotype database: a repository for systems microscopy data', 'Kirsanova, Catherine|Brazma, Alvis|Rustici, Gabriella|Sarkans, Ugis', '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', 8, 'Cells|Databases as Topic|Microscopy|Phenotype|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv199', '25861964', 'The Cellular Phenotype Database (CPD) is a repository for data derived from high-throughput systems microscopy studies. The aims of this resource are: (i) to provide easy access to cellular phenotype and molecular localization data for the broader research community, (ii) to facilitate integration of independent phenotypic studies by means of data aggregation techniques, including use of an ontology and (iii) to facilitate development of analytical methods in this field.'),
(79172, 2015, 'bio', 'Conformational sampling and structure prediction of multiple interacting loops in soluble and xcexb2-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method', 'Tang, Ke|Wong, Samuel W K|Liu, Jun S|Zhang, Jinfeng|Liang, Jie', 'Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL.|Department of Statistics, University of Florida, Gainesville, FL.|Department of Statistics, Harvard University, Science Center, Cambridge, MA and.|Department of Statistics, Florida State University, Tallahassee, FL, USA.|Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL.', 'United States|United States|United States|United States|United States', 9, 'Computer Simulation|Humans|Membrane Proteins|Models, Molecular|Monte Carlo Method|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btv198', '25861965', 'Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task.'),
(79173, 2015, 'bio', 'AVIA v20: annotation, visualization and impact analysis of genomic variants and genes', 'Vuong, Hue|Che, Anney|Ravichandran, Sarangan|Luke, Brian T|Collins, Jack R|Mudunuri, Uma S', 'Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Databases, Genetic|Genes|Genetic Variation|Internet|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btv200', '25861966', 'As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIAs improved interface, users can better visualize their data, perform comprehensive searches and categorize both coding and non-coding variants.'),
(79174, 2015, 'bio', 'GeneTIER: prioritization of candidate disease genes using tissue-specific gene expression profiles', 'Antanaviciute, Agne|Daly, Catherine|Crinnion, Laura A|Markham, Alexander F|Watson, Christopher M|Bonthron, David T|Carr, Ian M', 'Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds, St Jamess University Hospital and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds, St Jamess University Hospital and.|Yorkshire Regional Genetics Service, St Jamess University Hospital, Leeds, UK.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds, St Jamess University Hospital and.|Yorkshire Regional Genetics Service, St Jamess University Hospital, Leeds, UK.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds, St Jamess University Hospital and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds, St Jamess University Hospital and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Algorithms|Area Under Curve|Disease|Genetic Association Studies|Humans|Organ Specificity|ROC Curve|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv196', '25861967', 'In attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually.'),
(79175, 2015, 'bio', 'MetaSV: an accurate and integrative structural-variant caller for next generation sequencing', 'Mohiyuddin, Marghoob|Mu, John C|Li, Jian|Bani Asadi, Narges|Gerstein, Mark B|Abyzov, Alexej|Wong, Wing H|Lam, Hugo Y K', 'Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.|Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.|Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.|Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA.|Department of Statistics, Stanford University, Stanford, CA 94035, USA and Department of Health Research and Policy, Stanford University, Stanford, CA 94035, USA.|Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 51, 'Genetic Variation|High-Throughput Nucleotide Sequencing|Mutagenesis, Insertional|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btv204', '25861968', 'Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed, however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.'),
(79176, 2015, 'bio', 'Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data', 'Sill, Martin|Saadati, Maral|Benner, Axel', 'Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany.|Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany.|Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 9, 'Computer Simulation|Databases, Genetic|Humans|Principal Component Analysis|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btv197', '25861969', 'Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso, to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results.'),
(79177, 2015, 'bio', 'Accurate prediction of RNA nucleotide interactions with backbone k-tree model', 'Ding, Liang|Xue, Xingran|LaMarca, Sal|Mohebbi, Mohammad|Samad, Abdul|Malmberg, Russell L|Cai, Liming', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Department of Computer Science, BUITEMS, Pakistan.|Institute of Bioinformatics and Department of Plant Biology, University of Georgia, GA 30602, USA and.|Department of Computer Science, Institute of Bioinformatics and.', '-|-|-|-|Pakistan|Georgia|-', 0, 'Algorithms|Humans|Models, Theoretical|Nucleic Acid Conformation|Nucleotide Motifs|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv210', '25886978', 'Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense, existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides.'),
(79178, 2015, 'bio', 'FastMotif: spectral sequence motif discovery', 'Colombo, Nicoló|Vlassis, Nikos', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg and.|Adobe Research, San Jose, CA, USA.', 'Luxembourg|United States', 8, 'Algorithms|Binding Sites|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Machine Learning|Models, Theoretical|Nucleotide Motifs|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv208', '25886979', 'Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies.'),
(79179, 2015, 'bio', 'StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo', 'Tang, Yin|Bouvier, Emil|Kwok, Chun Kit|Ding, Yiliang|Nekrutenko, Anton|Bevilacqua, Philip C|Assmann, Sarah M', 'Department of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program.|Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.|Center for RNA Molecular Biology, Department of Chemistry and.|Department of Biology, Center for RNA Molecular Biology, Department of Chemistry and.|Bioinformatics and Genomics Graduate Program, Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.|Center for RNA Molecular Biology, Department of Chemistry and Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.|Department of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program, Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.', 'United States|United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Arabidopsis|Computational Biology|Genome, Plant|Nucleic Acid Conformation|RNA|RNA, Plant|RNA, Ribosomal, 18S|Regulatory Sequences, Ribonucleic Acid|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv213', '25886980', 'RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise.'),
(79180, 2015, 'bio', 'PDIviz: analysis and visualization of protein-DNA binding interfaces', 'Ribeiro, Judemir|Melo, Francisco|Schüller, Andreas', 'Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.', 'Chile|Chile|Chile', 12, 'Computational Biology|DNA|DNA-Binding Proteins|Models, Molecular|Software|Solvents', 'Journal Article', '10.1093/bioinformatics/btv203', '25886981', 'Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein-DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different pharmacophoric type (hydrogen bond donors/acceptors, hydrophobic and thymine methyl). Each mode comes in three styles to focus the visual analysis on the protein or DNA side of the interface, or on the nucleotide sequence. PDIviz allows for the generation of publication quality images, all calculated data can be written to disk, and a command line interface is provided for automating tasks. The plugin may be helpful for the detailed identification of regions involved in DNA base and shape readout, and can be particularly useful in rapidly pinpointing the overall mode of interaction.'),
(79181, 2015, 'bio', 'GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach', 'Schmidt, Ellen M|Zhang, Ji|Zhou, Wei|Chen, Jin|Mohlke, Karen L|Chen, Y Eugene|Willer, Cristen J', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.|Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109 and.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.|Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109 and.|Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109 and.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109 and.', 'United States|United States|United States|United States|United States|United States|United States', 51, 'Algorithms|Epigenomics|Genetic Variation|Genome-Wide Association Study|Genomics|Humans|Organ Specificity|Phenotype|Programming Languages|Quantitative Trait Loci|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btv201', '25886982', 'The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci.'),
(79182, 2015, 'bio', 'ScaffMatch: scaffolding algorithm based on maximum weight matching', 'Mandric, Igor|Zelikovsky, Alex', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.', 'Georgia|Georgia', 0, 'Algorithms|Body Weight|Contig Mapping|Genome|High-Throughput Nucleotide Sequencing|Humans|Plasmodium falciparum|Rhodobacter sphaeroides|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btv211', '25890305', 'Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist.'),
(79183, 2015, 'bio', 'MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chous pseudo amino acid composition and a novel multi-label classifier', 'Wang, Xiao|Zhang, Weiwei|Zhang, Qiuwen|Li, Guo-Zheng', 'School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and.|School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and.|School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and.|Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.', 'China|China|China|China', 75, 'Algorithms|Amino Acids|Chloroplast Proteins|Chloroplasts|Internet|Protein Transport|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btv212', '25900916', 'Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins.'),
(79184, 2015, 'bio', 'Protein homology reveals new targets for bioactive small molecules', 'Gfeller, David|Zoete, Vincent', 'Department of Fundamental Oncology, Ludwig Center for Cancer Research, University of Lausanne, 1066 Epalinges, Switzerland and Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 0, 'Algorithms|Animals|Area Under Curve|Databases, Protein|Humans|Proteins|Sequence Homology, Amino Acid|Small Molecule Libraries|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btv214', '25900917', 'The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species.'),
(79185, 2015, 'bio', 'Analysis of impedance-based cellular growth assays', 'Witzel, Franziska|Fritsche-Guenther, Raphaela|Lehmann, Nadine|Sieber, Anja|Blüthgen, Nils', 'Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.|BIMSB, Max-Delbrück-Centrum für Molekulare Medizin, 13092 Berlin and.|Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.|Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.|Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.', 'Germany|-|Germany|Germany|Germany', 19, 'Biological Assay|Cell Adhesion|Cell Cycle|Cell Line, Tumor|Cell Proliferation|Cells|Electric Impedance|Humans|Inhibitory Concentration 50|Models, Biological|Small Molecule Libraries|Time Factors', 'Journal Article', '10.1093/bioinformatics/btv216', '25900918', 'Impedance-based technologies are advancing methods for measuring proliferation of adherent cell cultures non-invasively and in real time. The analysis of the resulting data has so far been hampered by inappropriate computational methods and the lack of systematic data to evaluate the characteristics of the assay.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79186, 2015, 'bio', 'edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test', 'Dimont, Emmanuel|Shi, Jiantao|Kirchner, Rory|Hide, Winston', 'Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA, Harvard Stem Cell Institute, 1350 Massachusetts Ave, Cambridge, MA 02138, USA and Sheffield Institute of Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Male|Prostatic Neoplasms|RNA, Neoplasm|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv209', '25900919', 'Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far, few exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as two replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes.'),
(79187, 2015, 'bio', 'MultiMeta: an R package for meta-analyzing multi-phenotype genome-wide association studies', 'Vuckovic, D|Gasparini, P|Soranzo, N|Iotchkova, V', 'Department of Medical, Surgical and Health Sciences, University of Trieste, 34100 Trieste, Italy.|Department of Medical, Surgical and Health Sciences, University of Trieste, 34100 Trieste, Italy, Medical Genetics, Institute for Maternal and Child Health IRCCS Burlo Garofolo, 34100 Trieste, Italy.|Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, Department of Haematology, University of Cambridge, Cambridge CB2 0AH and.|Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.', 'Italy|Italy|United Kingdom|United Kingdom', 8, 'Genetic Loci|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Multivariate Analysis|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btv222', '25908790', 'As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n-dimensional setting.'),
(79188, 2015, 'bio', 'ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies', 'Gaye, Amadou|Burton, Thomas W Y|Burton, Paul R', 'School of Social and Community Medicine, University of Bristol, UK and.|School of Computing Science, Newcastle University, UK.|School of Social and Community Medicine, University of Bristol, UK and.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Blood Pressure|Canada|Cohort Studies|Genetic Association Studies|Humans|Internet|Linear Models|Quantitative Trait, Heritable|Sample Size|Software|Systole', 'Journal Article', '10.1093/bioinformatics/btv219', '25908791', 'Very large studies are required to provide sufficiently big sample sizes for adequately powered association analyses. This can be an expensive undertaking and it is important that an accurate sample size is identified. For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account. Conventional methods to analyse power use closed-form solutions that are not flexible enough to cater for all of these elements easily. They often result in a potentially substantial overestimation of the actual power.'),
(79189, 2015, 'bio', 'ms-data-core-api: an open-source, metadata-oriented library for computational proteomics', 'Perez-Riverol, Yasset|Uszkoreit, Julian|Sanchez, Aniel|Ternent, Tobias|Del Toro, Noemi|Hermjakob, Henning|Vizcaíno, Juan Antonio|Wang, Rui', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|Ruhr-Universität Bochum, Medizinisches Proteom-Zenter, Medical Bioinformatics, ZKF, E.142, Universitätsstr. 150, D-44801 Bochum, Germany and.|Department of Proteomics, Center for Genetic Engineering and Biotechnology, Ciudad de la Habana, Cuba.|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|Germany|Cuba|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Databases, Protein|Humans|Mass Spectrometry|Peptide Fragments|Proteins|Proteomics|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btv250', '25910694', 'The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.'),
(79190, 2015, 'bio', 'Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data', 'Patrick, Ellis|Buckley, Michael|Müller, Samuel|Lin, David M|Yang, Jean Y H', 'School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.|CSIRO Mathematical & Information Sciences, Clayton South, VIC 3168, Australia and.|School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.|Department of Biomedical Sciences, Cornell University, Ithaca, NY, USA.|School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.', 'Australia|Australia|Australia|United States|Australia', 0, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/btv220', '25910695', 'In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult.'),
(79191, 2015, 'bio', 'Data-dependent bucketing improves reference-free compression of sequencing reads', 'Patro, Rob|Kingsford, Carl', 'Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA and.|Department Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.', 'United States|United States', 20, 'Algorithms|Computational Biology|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv248', '25910696', 'The storage and transmission of high-throughput sequencing data consumes significant resources. As our capacity to produce such data continues to increase, this burden will only grow. One approach to reduce storage and transmission requirements is to compress this sequencing data.'),
(79192, 2015, 'bio', 'Addressing false discoveries in network inference', 'Petri, Tobias|Altmann, Stefan|Geistlinger, Ludwig|Zimmer, Ralf|Kxc3xbcffner, Robert', 'Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Institut fxc3xbcr Informatik, Munich, Germany and.|Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Institut fxc3xbcr Informatik, Munich, Germany and.|Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Institut fxc3xbcr Informatik, Munich, Germany and.|Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Institut fxc3xbcr Informatik, Munich, Germany and.|Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Institut fxc3xbcr Informatik, Munich, Germany and Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Mxc3xbcnchen, German Research Center for Environmental Health, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Machine Learning|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae Proteins|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv215', '25910697', 'Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles.'),
(79193, 2015, 'bio', 'PDBest: a user-friendly platform for manipulating and enhancing protein structures', 'Gonçalves, Wellisson R S|Gonçalves-Almeida, Valdete M|Arruda, Aleksander L|Meira, Wagner|da Silveira, Carlos H|Pires, Douglas E V|de Melo-Minardi, Raquel C', 'Department of Computer Science, Universidade Federal de Minas Gerais, Brazil.|Department of Computer Science, Universidade Federal de Minas Gerais, Brazil.|Department of Computer Science, Universidade Federal de Minas Gerais, Brazil.|Department of Computer Science, Universidade Federal de Minas Gerais, Brazil.|Advanced Campus at Itabira, Universidade Federal de Itajubá, Brazil and.|Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Brazil.|Department of Computer Science, Universidade Federal de Minas Gerais, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 2, 'Computer Graphics|Databases, Protein|Humans|Protein Conformation|Proteins|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv223', '25910698', 'PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility.'),
(79194, 2015, 'bio', 'CiVi: circular genome visualization with unique features to analyze sequence elements', 'Overmars, Lex|van Hijum, Sacha A F T|Siezen, Roland J|Francke, Christof', 'CMBI, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands, Netherlands Bioinformatics Centre, Nijmegen 6500HB, The Netherlands, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.|CMBI, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands, Netherlands Bioinformatics Centre, Nijmegen 6500HB, The Netherlands, NIZO Food Research B.V., Ede, The Netherlands and.|CMBI, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands, Netherlands Bioinformatics Centre, Nijmegen 6500HB, The Netherlands.|CMBI, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands, Netherlands Bioinformatics Centre, Nijmegen 6500HB, The Netherlands, Hogeschool Arnhem en Nijmegen BioCentre, 6525EM Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Genes, Bacterial|Genome, Bacterial|Genomics|Information Storage and Retrieval|Molecular Sequence Annotation|Regulatory Sequences, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv249', '25910699', 'We have developed CiVi, a user-friendly web-based tool to create custom circular maps to aid the analysis of microbial genomes and sequence elements. Sequence related data such as gene-name, COG class, PFAM domain, GC%, and subcellular location can be comprehensively viewed. Quantitative gene-related data (e.g. expression ratios or read counts) as well as predicted sequence elements (e.g. regulatory sequences) can be uploaded and visualized. CiVi accommodates the analysis of genomic elements by allowing a visual interpretation in the context of: (i) their genome-wide distribution, (ii) provided experimental data and (iii) the local orientation and location with respect to neighboring genes. CiVi thus enables both experts and non-experts to conveniently integrate public genome data with the results of genome analyses in circular genome maps suitable for publication.'),
(79195, 2015, 'bio', 'iFoldRNA v2: folding RNA with constraints', 'Krokhotin, Andrey|Houlihan, Kevin|Dokholyan, Nikolay V', 'Department of Biochemistry & Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA.|Department of Biochemistry & Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA.|Department of Biochemistry & Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA.', 'United States|United States|United States', 0, 'Algorithms|Base Pairing|Computational Biology|Computer Simulation|Hydroxyl Radical|Models, Molecular|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv221', '25910700', 'A key to understanding RNA function is to uncover its complex 3D structure. Experimental methods used for determining RNA 3D structures are technologically challenging and laborious, which makes the development of computational prediction methods of substantial interest. Previously, we developed the iFoldRNA server that allows accurate prediction of short (<50xe2x80x89nt) tertiary RNA structures starting from primary sequences. Here, we present a new version of the iFoldRNA server that permits the prediction of tertiary structure of RNAs as long as a few hundred nucleotides. This substantial increase in the server capacity is achieved by utilization of experimental information such as base-pairing and hydroxyl-radical probing. We demonstrate a significant benefit provided by integration of experimental data and computational methods.'),
(79196, 2015, 'bio', 'chipPCR: an R package to pre-process raw data of amplification curves', 'Rxc3xb6diger, Stefan|Burdukiewicz, Michaxc5x82|Schierack, Peter', 'Faculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany and.|Department of Genomics, Faculty of Biotechnology, University of Wrocxc5x82aw, Wrocxc5x82aw, Poland.|Faculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany and.', 'Germany|Poland|Germany', 11, 'Algorithms|Gene Expression Profiling|Humans|Models, Theoretical|Programming Languages|RNA|Real-Time Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btv205', '25913204', 'Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments.'),
(79197, 2015, 'bio', 'Genome-scale strain designs based on regulatory minimal cut sets', 'Mahadevan, Radhakrishnan|von Kamp, Axel|Klamt, Steffen', 'Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S3E5, Canada, Institute of Biomaterials and Biomedical Engineering, Toronto, ON, M5S 3G9, Canada and.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, D-39106, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, D-39106, Germany.', 'Canada|Germany|Germany', 18, 'Algorithms|Computer Simulation|Escherichia coli|Ethanol|Genetic Engineering|Genome, Bacterial|Metabolic Networks and Pathways|Models, Biological|Models, Theoretical|Phenotype', 'Journal Article', '10.1093/bioinformatics/btv217', '25913205', 'Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype.'),
(79198, 2015, 'bio', 'kSNP30: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome', 'Gardner, Shea N|Slezak, Tom|Hall, Barry G', 'Computations/Global Security, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA and.|Computations/Global Security, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA and.|Bellingham Research Institute, Bellingham, WA 98229, USA.', 'United States|United States|United States', 0, 'Computational Biology|Databases, Nucleic Acid|Escherichia coli|Genome, Bacterial|Molecular Sequence Annotation|Phylogeny|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv271', '25913206', 'We announce the release of kSNP3.0, a program for SNP identification and phylogenetic analysis without genome alignment or the requirement for reference genomes. kSNP3.0 is a significantly improved version of kSNP v2.'),
(79199, 2015, 'bio', 'DockStar: a novel ILP-based integrative method for structural modeling of multimolecular protein complexes', 'Amir, Naama|Cohen, Dan|Wolfson, Haim J', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel', 10, 'Algorithms|Animals|Cattle|Computational Biology|Humans|Models, Molecular|Molecular Docking Simulation|Multiprotein Complexes|Protein Conformation|Proteins|Saccharomyces cerevisiae Proteins|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btv270', '25913207', 'Atomic resolution modeling of large multimolecular assemblies is a key task in Structural Cell Biology. Experimental techniques can provide atomic resolution structures of single proteins and small complexes, or low resolution data of large multimolecular complexes.'),
(79200, 2015, 'bio', 'phylogeo: an R package for geographic analysis and visualization of microbiome data', 'Charlop-Powers, Zachary|Brady, Sean F', 'Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, New York, NY 10065, USA.|Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, New York, NY 10065, USA.', 'United States|United States', 0, 'Computer Graphics|Data Interpretation, Statistical|Humans|Metagenome|Microbiota|Phylogeny|Programming Languages|Sequence Analysis, DNA|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv269', '25913208', 'We have created an R package named phylogeo that provides a set of geographic utilities for sequencing-based microbial ecology studies. Although the geographic location of samples is an important aspect of environmental microbiology, none of the major software packages used in processing microbiome data include utilities that allow users to map and explore the spatial dimension of their data. phylogeo solves this problem by providing a set of plotting and mapping functions that can be used to visualize the geographic distribution of samples, to look at the relatedness of microbiomes using ecological distance, and to map the geographic distribution of particular sequences. By extending the popular phyloseq package and using the same data structures and command formats, phylogeo allows users to easily map and explore the geographic dimensions of their data from the R programming language.'),
(79201, 2015, 'bio', 'Polyester: simulating RNA-seq datasets with differential transcript expression', 'Frazee, Alyssa C|Jaffe, Andrew E|Langmead, Ben|Leek, Jeffrey T', 'Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Center for Computational Biology and.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Center for Computational Biology and.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Center for Computational Biology and Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Center for Computational Biology and.', 'United States|United States|United States|United States', 0, 'Algorithms|Binomial Distribution|Chromosomes, Human, Pair 22|Computational Biology|Europe|Gene Expression Profiling|Gene Expression Regulation|Genetics, Population|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Protein Isoforms|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv272', '25926345', 'Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.'),
(79202, 2016, 'bio', 'PopIns: population-scale detection of novel sequence insertions', 'Kehr, Birte|Melsted, Pxc3xa1ll|Halldxc3xb3rsson, Bjarni V', 'deCODE genetics/Amgen, Reykjavxc3xadk, Iceland.|deCODE genetics/Amgen, Reykjavxc3xadk, Iceland, Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavxc3xadk, Iceland and.|deCODE genetics/Amgen, Reykjavxc3xadk, Iceland, Institute of Biomedical and Neural Engineering, Reykjavxc3xadk University, Reykjavxc3xadk, Iceland.', 'Iceland|Iceland|Iceland', 14, 'Computational Biology|Genomic Structural Variation|High-Throughput Nucleotide Sequencing|Humans|Mutagenesis, Insertional|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv273', '25926346', 'The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. Dexc2xa0novo assembly is not only computationally challenging, but also requires high-quality data. Although the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions.'),
(79203, 2015, 'bio', 'Flatten plus: a recent implementation in WSxM for biological research', 'Gimeno, Alejandro|Ares, Pablo|Horcas, Ignacio|Gil, Adriana|Gómez-Rodríguez, José M|Colchero, Jaime|Gómez-Herrero, Julio', 'Laboratorio de Nuevas Microscopías, Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid, WSxM solutions, C Júpiter, 7, E-28042 Madrid.|Laboratorio de Nuevas Microscopías, Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid.|WSxM solutions, C Júpiter, 7, E-28042 Madrid.|Nanotec Electrónica S.L., Ronda de Poniente 12, E-28760 Tres Cantos, Madrid and.|Laboratorio de Nuevas Microscopías, Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid.|Departamento de Física, Facultad de Química. Universidad de Murcia, E-30100 Murcia, Spain.|Laboratorio de Nuevas Microscopías, Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid.', '-|-|-|-|-|Spain|-', 0, 'Algorithms|Biomedical Research|Cell Cycle Proteins|Chromosomal Proteins, Non-Histone|DNA|Humans|Image Enhancement|Image Processing, Computer-Assisted|Microscopy, Scanning Probe|Software', 'Journal Article', '10.1093/bioinformatics/btv278', '25931516', 'Scanning probe microscopy (SPM) is already a relevant tool in biological research at the nanoscale. We present Flatten plus, a recent and helpful implementation in the well-known WSxM free software package. Flatten plus allows reducing low-frequency noise in SPM images in a semi-automated way preventing the appearance of typical artifacts associated with such filters.'),
(79204, 2015, 'bio', 'RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data', 'He, Yuting|Zhang, Fan|Flaherty, Patrick', 'Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and.|Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and.|Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.', 'United States|United States|United States', 0, 'Adult|Algorithms|Alleles|Bayes Theorem|Biomarkers, Tumor|Breast Neoplasms|Carcinoma, Ductal, Breast|Female|Gene Frequency|Genomics|High-Throughput Nucleotide Sequencing|Humans|Loss of Heterozygosity|Mutation|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv275', '25931517', 'Next-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed.'),
(79205, 2015, 'bio', 'HTT-DB: horizontally transferred transposable elements database', 'Dotto, Bruno Reis|Carvalho, Evelise Leis|Silva, Alexandre Freitas|Duarte Silva, Luiz Fernando|Pinto, Paulo Marcos|Ortiz, Mauro Freitas|Wallau, Gabriel Luz', 'Campus São Gabriel, Universidade Federal do Pampa, São Gabriel.|Pós Graduação em Ciências Biológicas, Universidade Federal do Pampa, São Gabriel and.|Campus São Gabriel, Universidade Federal do Pampa, São Gabriel.|Campus São Gabriel, Universidade Federal do Pampa, São Gabriel.|Campus São Gabriel, Universidade Federal do Pampa, São Gabriel, Pós Graduação em Ciências Biológicas, Universidade Federal do Pampa, São Gabriel and.|Pós Graduação em Genetica e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brasil.|Pós Graduação em Ciências Biológicas, Universidade Federal do Pampa, São Gabriel and.', '-|-|-|-|-|-|-', 34, 'Animals|DNA Transposable Elements|Databases, Factual|Eukaryota|Evolution, Molecular|Gene Transfer, Horizontal|Genome|Humans|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btv281', '25940562', 'Horizontal transfer of transposable (HTT) elements among eukaryotes was discovered in the mid-1980s. As then, >300 new cases have been described. New findings about HTT are revealing the evolutionary impact of this phenomenon on host genomes. In order to provide an up to date, interactive and expandable database for such events, we developed the HTT-DB database.'),
(79206, 2015, 'bio', 'Phylesystem: a git-based data store for community-curated phylogenetic estimates', 'McTavish, Emily Jane|Hinchliff, Cody E|Allman, James F|Brown, Joseph W|Cranston, Karen A|Holder, Mark T|Rees, Jonathan A|Smith, Stephen A', 'Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA, Heidelberg Institute for Theoretical Studies, Heidelberg 69118, Germany.|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.|Interrobang Corporation, Wake Forest, NC 27587, USA and.|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.|National Evolutionary Synthesis Center, Duke University, Durham, NC, USA.|Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA, Heidelberg Institute for Theoretical Studies, Heidelberg 69118, Germany.|National Evolutionary Synthesis Center, Duke University, Durham, NC, USA.|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.', 'Germany|United States|United States|United States|United States|Germany|United States|United States', 15, 'Computational Biology|Databases, Factual|Humans|Information Storage and Retrieval|Internet|Phylogeny|Programming Languages|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv276', '25940563', 'Phylogenetic estimates from published studies can be archived using general platforms like Dryad (Vision, 2010) or TreeBASE (Sanderson et al., 1994). Such services fulfill a crucial role in ensuring transparency and reproducibility in phylogenetic research. However, digital tree data files often require some editing (e.g. rerooting) to improve the accuracy and reusability of the phylogenetic statements. Furthermore, establishing the mapping between tip labels used in a tree and taxa in a single common taxonomy dramatically improves the ability of other researchers to reuse phylogenetic estimates. As the process of curating a published phylogenetic estimate is not error-free, retaining a full record of the provenance of edits to a tree is crucial for openness, allowing editors to receive credit for their work and making errors introduced during curation easier to correct.'),
(79207, 2015, 'bio', 'The SwissLipids knowledgebase for lipid biology', 'Aimo, Lucila|Liechti, Robin|Hyka-Nouspikel, Nevila|Niknejad, Anne|Gleizes, Anne|Gxc3xb6tz, Lou|Kuznetsov, Dmitry|David, Fabrice P A|van der Goot, F Gisou|Riezman, Howard|Bougueleret, Lydie|Xenarios, Ioannis|Bridge, Alan', 'Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bxc3xa2timent Gxc3xa9nopode, CH-1015 Lausanne, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bxc3xa2timent Gxc3xa9nopode, CH-1015 Lausanne, Switzerland.|Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bxc3xa2timent Gxc3xa9nopode, CH-1015 Lausanne, Switzerland.|Bioinformatics and Biostatistics Core Facility, xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne, CH-1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.|Global Health Institute, xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne, Station 19, CH-1015 Lausanne, Switzerland.|Department of Biochemistry, University of Geneva, CH-1211 Geneva, Switzerland, Switzerland National Centre of Competence in Research Chemical Biology, University of Geneva, CH-1211 Geneva, Switzerland and.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland, Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bxc3xa2timent Gxc3xa9nopode, CH-1015 Lausanne, Switzerland, Department of Biochemistry, University of Geneva, CH-1211 Geneva, Switzerland, Centre for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland.|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 0, 'Computational Biology|Databases, Factual|Humans|Knowledge Bases|Lipid Metabolism|Lipids|Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv285', '25943471', 'Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it.'),
(79208, 2015, 'bio', 'Automated band annotation for RNA structure probing experiments with numerous capillary electrophoresis profiles', 'Lee, Seungmyung|Kim, Hanjoo|Tian, Siqi|Lee, Taehoon|Yoon, Sungroh|Das, Rhiju', 'Department of ECE, Seoul National University, Seoul 151-744, Korea.|Department of ECE, Seoul National University, Seoul 151-744, Korea.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of ECE, Seoul National University, Seoul 151-744, Korea.|Department of ECE, Seoul National University, Seoul 151-744, Korea, Interdisciplinary Program in Bionformatics, Seoul National University, Seoul 151-744, Korea and.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Electrophoresis, Capillary|Humans|RNA|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv282', '25943472', 'Capillary electrophoresis (CE) is a powerful approach for structural analysis of nucleic acids, with recent high-throughput variants enabling three-dimensional RNA modeling and the discovery of new rules for RNA structure design. Among the steps composing CE analysis, the process of finding each band in an electrophoretic trace and mapping it to a position in the nucleic acid sequence has required significant manual inspection and remains the most time-consuming and error-prone step. The few available tools seeking to automate this band annotation have achieved limited accuracy and have not taken advantage of information across dozens of profiles routinely acquired in high-throughput measurements.'),
(79209, 2015, 'bio', 'NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks', 'Hurley, Daniel G|Cursons, Joseph|Kan Wang, Yi|Budden, David M|Print, Cristin G|Crampin, Edmund J', 'Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1093/bioinformatics/btv095', '25948714', '-'),
(79210, 2015, 'bio', 'pez: phylogenetics for the environmental sciences', 'Pearse, William D|Cadotte, Marc W|Cavender-Bares, Jeannine|Ives, Anthony R|Tucker, Caroline M|Walker, Steve C|Helmus, Matthew R', 'Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, USA, Department of Biology, McGill University, Montréal, QC, Canada, Département des Sciences Biologiques, Université du Québec à Montréal, Succursale Centre-ville, Montréal, QC, Canada.|Department of Biological Sciences, University of Toronto Scarborough, Scarborough, ON, Canada.|Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, USA.|Department of Zoology, University of Wisconsin, Madison, WI, USA.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.|Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada, and.|Department of Animal Ecology, Vrije Universiteit, Amsterdam, The Netherlands.', 'Canada|Canada|United States|United States|United States|Canada|Netherlands', 65, 'Biodiversity|Databases, Genetic|Ecology|Phenotype|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv277', '25948716', 'pez is an R package that permits measurement, modelling and simulation of phylogenetic structure in ecological data. pez contains the first implementation of many methods in R, and aggregates existing data structures and methods into a single, coherent package.'),
(79211, 2015, 'bio', 'IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data', 'Baez-Ortega, Adrian|Lorenzo-Diaz, Fabian|Hernandez, Mariano|Gonzalez-Vila, Carlos Ignacio|Roda-Garcia, Jose Luis|Colebrook, Marcos|Flores, Carlos', 'Information Technology Department, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.|Applied Genomics Group (G2A), Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (CIBICAN), Universidad de La Laguna, Santa Cruz de Tenerife, Spain, Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain.|Applied Genomics Group (G2A), Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (CIBICAN), Universidad de La Laguna, Santa Cruz de Tenerife, Spain.|Information Technology Department, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.|Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Santa Cruz de Tenerife, Spain and.|Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Santa Cruz de Tenerife, Spain and.|Applied Genomics Group (G2A), Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (CIBICAN), Universidad de La Laguna, Santa Cruz de Tenerife, Spain, Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain, CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Bacteria|Computational Biology|Databases, Genetic|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Information Storage and Retrieval|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv283', '25953799', 'We introduce IonGAP, a publicly available Web platform designed for the analysis of whole bacterial genomes using Ion Torrent sequence data. Besides assembly, it integrates a variety of comparative genomics, annotation and bacterial classification routines, based on the widely used FASTQ, BAM and SRA file formats. Benchmarking with different datasets evidenced that IonGAP is a fast, powerful and simple-to-use bioinformatics tool. By releasing this platform, we aim to translate low-cost bacterial genome analysis for microbiological prevention and control in healthcare, agroalimentary and pharmaceutical industry applications.'),
(79212, 2015, 'bio', 'Identification of C2H2-ZF binding preferences from ChIP-seq data using RCADE', 'Najafabadi, Hamed S|Albu, Mihai|Hughes, Timothy R', 'Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.|Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.|Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada and Canadian Institute for Advanced Research, Toronto, ON, Canada.', 'Canada|Canada|Canada', 8, 'Algorithms|Binding Sites|Carrier Proteins|Chromatin Immunoprecipitation|Gene Expression Regulation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Nuclear Proteins|Nucleotide Motifs|Regulatory Sequences, Nucleic Acid|Retroelements|Sequence Analysis, DNA|Transcription Factors|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/btv284', '25953800', 'Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys2His2 zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail.'),
(79213, 2015, 'bio', 'BFC: correcting Illumina sequencing errors', 'Li, Heng', 'Medical Population Genetics Program, Broad Institute, Cambridge, MA 02142, USA.', 'United States', 0, 'Algorithms|Computational Biology|False Positive Reactions|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv290', '25953801', 'BFC is a free, fast and easy-to-use sequencing error corrector designed for Illumina short reads. It uses a non-greedy algorithm but still maintains a speed comparable to implementations based on greedy methods. In evaluations on real data, BFC appears to correct more errors with fewer overcorrections in comparison to existing tools. It particularly does well in suppressing systematic sequencing errors, which helps to improve the base accuracy of de novo assemblies.'),
(79214, 2015, 'bio', 'INPS: predicting the impact of non-synonymous variations on protein stability from sequence', 'Fariselli, Piero|Martelli, Pier Luigi|Savojardo, Castrense|Casadio, Rita', 'Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and.', 'Italy|-|-|-', 43, 'Algorithms|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Machine Learning|Mutation|Protein Engineering|Protein Stability|Proteins|Software|Thermodynamics|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btv291', '25957347', 'A tool for reliably predicting the impact of variations on protein stability is extremely important for both protein engineering and for understanding the effects of Mendelian and somatic mutations in the genome. Next Generation Sequencing studies are constantly increasing the number of protein sequences. Given the huge disproportion between protein sequences and structures, there is a need for tools suited to annotate the effect of mutations starting from protein sequence without relying on the structure. Here, we describe INPS, a novel approach for annotating the effect of non-synonymous mutations on the protein stability from its sequence. INPS is based on SVM regression and it is trained to predict the thermodynamic free energy change upon single-point variations in protein sequences.'),
(79215, 2015, 'bio', 'INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments', 'de Pretis, Stefano|Kress, Theresia|Morelli, Marco J|Melloni, Giorgio E M|Riva, Laura|Amati, Bruno|Pelizzola, Mattia', 'Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and Department of Experimental Oncology, European Institute of Oncology (IEO), 20139, Milano, Italy.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139, Milano, Italy and.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Cells, Cultured|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Mice|RNA|RNA Precursors|RNA Stability|RNA, Messenger|Sequence Analysis, RNA|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btv288', '25957348', 'Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79216, 2015, 'bio', 'Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data', 'Aßhauer, Kathrin P|Wemheuer, Bernd|Daniel, Rolf|Meinicke, Peter', 'Department of Bioinformatics and.|Department of Genomic and Applied Microbiology and Göttingen Genomics Laboratory, Institute of Microbiology and Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany.|Department of Genomic and Applied Microbiology and Göttingen Genomics Laboratory, Institute of Microbiology and Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany.|Department of Bioinformatics and.', '-|Germany|Germany|-', 359, 'Bacteria|Computational Biology|Databases, Factual|Databases, Nucleic Acid|Genes, Bacterial|Genetic Markers|Metagenome|Metagenomics|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv287', '25957349', 'The characterization of phylogenetic and functional diversity is a key element in the analysis of microbial communities. Amplicon-based sequencing of marker genes, such as 16S rRNA, is a powerful tool for assessing and comparing the structure of microbial communities at a high phylogenetic resolution. Because 16S rRNA sequencing is more cost-effective than whole metagenome shotgun sequencing, marker gene analysis is frequently used for broad studies that involve a large number of different samples. However, in comparison to shotgun sequencing approaches, insights into the functional capabilities of the community get lost when restricting the analysis to taxonomic assignment of 16S rRNA data.'),
(79217, 2015, 'bio', 'BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data', 'Kähärä, Juhani|Lähdesmäki, Harri', 'Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.|Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.', 'Finland|Finland', 0, 'Algorithms|Binding Sites|Chromatin|Chromatin Immunoprecipitation|DNA|DNA Footprinting|Deoxyribonuclease I|Gene Expression Regulation|Genomics|Hep G2 Cells|High-Throughput Nucleotide Sequencing|Humans|K562 Cells|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv294', '25957350', 'Transcription factors (TFs) are a class of DNA-binding proteins that have a central role in regulating gene expression. To reveal mechanisms of transcriptional regulation, a number of computational tools have been proposed for predicting TF-DNA interaction sites. Recent studies have shown that genome-wide sequencing data on open chromatin sites from a DNase I hypersensitivity experiments (DNase-seq) has a great potential to map putative binding sites of all transcription factors in a single experiment. Thus, computational methods for analysing DNase-seq to accurately map TF-DNA interaction sites are highly needed.'),
(79218, 2015, 'bio', 'Using combined evidence from replicates to evaluate ChIP-seq peaks', 'Jalili, Vahid|Matteucci, Matteo|Masseroli, Marco|Morelli, Marco J', 'Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy and.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy and.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy and.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Data Interpretation, Statistical|Gene Expression Regulation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|K562 Cells|Nucleotide Motifs|Protein Binding|Protein Structure, Tertiary|Proto-Oncogene Proteins c-myc|Quality Control|Reproducibility of Results|Sequence Analysis, DNA|Software|Transcription Factors|Ubiquitin-Protein Ligases', 'Evaluation Studies', '10.1093/bioinformatics/btv293', '25957351', 'Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA-protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives, replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence.'),
(79219, 2015, 'bio', 'Interactive analysis of large cancer copy number studies with Copy Number Explorer', 'Newman, Scott', 'Biostatistics & Bioinformatics Shared Resource, Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA.', 'United States', 1, 'Algorithms|DNA Copy Number Variations|Data Interpretation, Statistical|Databases, Genetic|Genome, Human|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btv298', '25957352', 'Copy number abnormalities (CNAs) such as somatically-acquired chromosomal deletions and duplications drive the development of cancer. As individual tumor genomes can contain tens or even hundreds of large and/or focal CNAs, a major difficulty is differentiating between important, recurrent pathogenic changes and benign changes unrelated to the subjects phenotype. Here we present Copy Number Explorer, an interactive tool for mining large copy number datasets. Copy Number Explorer facilitates rapid visual and statistical identification of recurrent regions of gain or loss, identifies the genes most likely to drive CNA formation using the cghMCR method and identifies recurrently broken genes that may be disrupted or fused. The software also allows users to identify recurrent CNA regions that may be associated with differential survival.'),
(79220, 2015, 'bio', 'Gener: a minimal programming module for chemical controllers based on DNA strand displacement', 'Kahramanoxc4x9fullarxc4xb1, Ozan|Cardelli, Luca', 'Department of Mathematics, University of Trento, Trento, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology.|Microsoft Research Cambridge and Department of Computer Science, University of Oxford.', 'Italy|-', 0, 'Computational Biology|Computers, Molecular|DNA|DNA Replication|Humans|Models, Molecular|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv286', '25957353', ': Gener is a development module for programming chemical controllers based on DNA strand displacement. Gener is developed with the aim of providing a simple interface that minimizes the opportunities for programming errors: Gener allows the user to test the computations of the DNA programs based on a simple two-domain strand displacement algebra, the minimal available so far. The tool allows the user to perform stepwise computations with respect to the rules of the algebra as well as exhaustive search of the computation space with different options for exploration and visualization. Gener can be used in combination with existing tools, and in particular, its programs can be exported to Microsoft Researchs DSD tool as well as to LaTeX.'),
(79221, 2015, 'bio', 'MemGen: a general web server for the setup of lipid membrane simulation systems', 'Knight, Christopher J|Hub, Jochen S', 'Georg-August-University Göttingen, Institute for Microbiology and Genetics, 37077 Göttingen, Germany.|Georg-August-University Göttingen, Institute for Microbiology and Genetics, 37077 Göttingen, Germany.', 'Germany|Germany', 23, 'Cell Membrane|Humans|Internet|Lipid Bilayers|Membrane Lipids|Models, Biological|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btv292', '25957354', 'Molecular dynamics simulations provide atomic insight into the physicochemical characteristics of lipid membranes and hence, a wide range of force field families capable of modelling various lipid types have been developed in recent years. To model membranes in a biologically realistic lipid composition, simulation systems containing multiple different lipids must be assembled.'),
(79222, 2015, 'bio', 'PsyGeNET: a knowledge platform on psychiatric disorders and their genes', 'Gutiérrez-Sacristán, Alba|Grosdidier, Solène|Valverde, Olga|Torrens, Marta|Bravo, Àlex|Piñero, Janet|Sanz, Ferran|Furlong, Laura I', 'Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.|Research Group on Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Neurobiology of Behaviour Research Group (GReNeC), IMIM, DCEXS, UPF and Institute of Neuropsychiatry and Addiction, Parc de Salut Mar, Universitat Autònoma de Barcelona, Barcelona 08003, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 28, 'Alcoholism|Algorithms|Animals|Biomarkers|Chromosome Mapping|Cocaine-Related Disorders|Data Mining|Databases, Factual|Depressive Disorder, Major|Disease Models, Animal|Gene Regulatory Networks|Humans|Knowledge Bases|Mice|Publications|Rats|Software', 'Journal Article', '10.1093/bioinformatics/btv301', '25964630', 'PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities.'),
(79223, 2015, 'bio', 'GOplot: an R package for visually combining expression data with functional analysis', 'Walter, Wencke|Sánchez-Cabo, Fátima|Ricote, Mercedes', 'Department of Cardiovascular Development and Repair and.|Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.|Department of Cardiovascular Development and Repair and.', '-|Spain|-', 277, 'Computational Biology|Computer Graphics|Data Interpretation, Statistical|Gene Expression Profiling|Gene Regulatory Networks|Humans|Programming Languages|Sequence Analysis, DNA|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv300', '25964631', 'Despite the plethora of methods available for the functional analysis of omics data, obtaining comprehensive-yet detailed understanding of the results remains challenging. This is mainly due to the lack of publicly available tools for the visualization of this type of information. Here we present an R package called GOplot, based on ggplot2, for enhanced graphical representation. Our package takes the output of any general enrichment analysis and generates plots at different levels of detail: from a general overview to identify the most enriched categories (bar plot, bubble plot) to a more detailed view displaying different types of information for molecules in a given set of categories (circle plot, chord plot, cluster plot). The package provides a deeper insight into omics data and allows scientists to generate insightful plots with only a few lines of code to easily communicate the findings.'),
(79224, 2015, 'bio', 'ADME SARfari: comparative genomics of drug metabolizing systems', 'Davies, Mark|Dedman, Nathan|Hersey, Anne|Papadatos, George|Hall, Matthew D|Cucurull-Sanchez, Lourdes|Jeffrey, Phil|Hasan, Samiul|Eddershaw, Peter J|Overington, John P', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage SG1 2NY and Pfizer Ltd., Granta Park, Great Abington, Cambridge CB21 6GP, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Animals|Computer Simulation|Dogs|Genomics|Humans|Internet|Pharmacogenetics|Pharmacokinetics|Polymorphism, Single Nucleotide|Proteins|Software|Tissue Distribution', 'Journal Article', '10.1093/bioinformatics/btv010', '25964657', 'ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules, available pharmacokinetic (PK) data, protein sequences of ADME-related molecular targets for pre-clinical model species and human, alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.'),
(79225, 2015, 'bio', 'CAPRI: efficient inference of cancer progression models from cross-sectional data', 'Ramazzotti, Daniele|Caravagna, Giulio|Olde Loohuis, Loes|Graudenzi, Alex|Korsunsky, Ilya|Mauri, Giancarlo|Antoniotti, Marco|Mishra, Bud', 'Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|Cross-Sectional Studies|Databases, Genetic|Disease Progression|Humans|Leukemia, Myelogenous, Chronic, BCR-ABL Positive|Models, Theoretical|Mutation|Probability|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv296', '25971740', 'We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems.'),
(79226, 2015, 'bio', 'Fast parametric time warping of peak lists', 'Wehrens, Ron|Bloemberg, Tom G|Eilers, Paul H C', 'Biometris, Wageningen UR, Wageningen, The Netherlands, Educational Institute for Molecular Sciences and Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.|Biometris, Wageningen UR, Wageningen, The Netherlands, Educational Institute for Molecular Sciences and Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands Biometris, Wageningen UR, Wageningen, The Netherlands, Educational Institute for Molecular Sciences and Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.|Biometris, Wageningen UR, Wageningen, The Netherlands, Educational Institute for Molecular Sciences and Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Carotenoids|Chromatography, Liquid|Mass Spectrometry|Metabolomics|Proteomics|Vitis', 'Journal Article', '10.1093/bioinformatics/btv299', '25971741', 'Alignment of peaks across samples is a difficult but unavoidable step in the data analysis for all analytical techniques containing a separation step like chromatography. Important application examples are the fields of metabolomics and proteomics. Parametric time warping (PTW) has already shown to be very useful in these fields because of the highly restricted form of the warping functions, avoiding overfitting. Here, we describe a new formulation of PTW, working on peak-picked features rather than on complete profiles. Not only does this allow for a much more smooth integration in existing pipelines, it also speeds up the (already among the fastest) algorithm by orders of magnitude. Using two publicly available datasets we show the potential of the new approach. The first set is a LC-DAD dataset of grape samples, and the second an LC-MS dataset of apple extracts.'),
(79227, 2015, 'bio', 'GEO2Enrichr: browser extension and server app to extract gene sets from GEO and analyze them for biological functions', 'Gundersen, Gregory W|Jones, Matthew R|Rouillard, Andrew D|Kou, Yan|Monteiro, Caroline D|Feldmann, Axel S|Hu, Kevin S|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and 367 Airport Sector, Goiania, Goias 74075, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 23, '3T3 Cells|Animals|Computational Biology|Databases, Genetic|Electronic Data Processing|Gene Expression Profiling|Gene Expression Regulation|Gene Library|Internet|Mice|Microarray Analysis|TRPV Cation Channels|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv297', '25971742', 'Identification of differentially expressed genes is an important step in extracting knowledge from gene expression profiling studies. The raw expression data from microarray and other high-throughput technologies is deposited into the Gene Expression Omnibus (GEO) and served as Simple Omnibus Format in Text (SOFT) files. However, to extract and analyze differentially expressed genes from GEO requires significant computational skills.'),
(79228, 2015, 'bio', 'GLASS: a comprehensive database for experimentally validated GPCR-ligand associations', 'Chan, Wallace K B|Zhang, Hongjiu|Yang, Jianyi|Brender, Jeffrey R|Hur, Junguk|xc3x96zgxc3xbcr, Arzucan|Zhang, Yang', 'Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey|Turkey|Turkey|Turkey|Turkey', 32, 'Algorithms|Data Mining|Databases, Protein|Humans|Internet|Ligands|Models, Molecular|Protein Binding|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/btv302', '25971743', 'G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations.'),
(79229, 2015, 'bio', 'Repeat- and error-aware comparison of deletions', 'Wittler, Roland|Marschall, Tobias|Schönhuth, Alexander|Mäkinen, Veli', 'Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland.|Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland.|Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland.|Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland.', 'Germany|Germany|Germany|Germany', 7, 'Algorithms|Computational Biology|Databases, Factual|Genetic Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Sequence Deletion|Software', 'Comparative Study', '10.1093/bioinformatics/btv304', '25979471', 'The number of reported genetic variants is rapidly growing, empowered by ever faster accumulation of next-generation sequencing data. A major issue is comparability. Standards that address the combined problem of inaccurately predicted breakpoints and repeat-induced ambiguities are missing. This decisively lowers the quality of consensus callsets and hampers the removal of duplicate entries in variant databases, which can have deleterious effects in downstream analyses.'),
(79230, 2015, 'bio', 'Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data', 'Wang, Bin|Cunningham, John M|Yang, Xinan Holly', 'Section of Hematology/Oncology, Department of Pediatrics, The University of Chicago, Chicago, IL, USA.|Section of Hematology/Oncology, Department of Pediatrics, The University of Chicago, Chicago, IL, USA.|Section of Hematology/Oncology, Department of Pediatrics, The University of Chicago, Chicago, IL, USA.', 'United States|United States|United States', 11, 'Computational Biology|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv289', '25979472', 'Seq2pathway is an R/Python wrapper for pathway (or functional gene-set) analysis of genomic loci, adapted for advances in genome research. Seq2pathway associates the biological significance of genomic loci with their target transcripts and then summarizes the quantified values on the gene-level into pathway scores. It is designed to isolate systematic disturbances and common biological underpinnings from next-generation sequencing (NGS) data. Seq2pathway offers Bioconductor users enhanced capability in discovering collective pathway effects caused by both coding genes and cis-regulation of non-coding elements.'),
(79231, 2015, 'bio', 'SpeeDB: fast structural protein searches', 'Robillard, David E|Mpangase, Phelelani T|Hazelhurst, Scott|Dehne, Frank', 'School of Computer Science, Carleton University, Ottawa, Ontario, Canada, Sydney Brenner Institute for Molecular Bioscience and School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.|School of Computer Science, Carleton University, Ottawa, Ontario, Canada, Sydney Brenner Institute for Molecular Bioscience and School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.|School of Computer Science, Carleton University, Ottawa, Ontario, Canada, Sydney Brenner Institute for Molecular Bioscience and School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa School of Computer Science, Carleton University, Ottawa, Ontario, Canada, Sydney Brenner Institute for Molecular Bioscience and School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.|School of Computer Science, Carleton University, Ottawa, Ontario, Canada, Sydney Brenner Institute for Molecular Bioscience and School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Humans|Hydrogen Bonding|Models, Molecular|Peptide Hydrolases|Protein Conformation|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btv274', '25979473', 'Interactions between amino acids are important determinants of the structure, stability and function of proteins. Several tools have been developed for the identification and analysis of such interactions in proteins based on the extensive studies carried out on high-resolution structures from Protein Data Bank (PDB). Although these tools allow users to identify and analyze interactions, analysis can only be performed on one structure at a time. This makes it difficult and time consuming to study the significance of these interactions on a large scale.'),
(79232, 2015, 'bio', 'WGE: a CRISPR database for genome engineering', 'Hodgkins, Alex|Farne, Anna|Perera, Sajith|Grego, Tiago|Parry-Smith, David J|Skarnes, William C|Iyer, Vivek', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 76, 'Animals|CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Databases, Factual|Gene Expression Regulation|Genetic Vectors|Genome|Humans|Mice|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btv308', '25979474', 'The rapid development of CRISPR-Cas9 mediated genome editing techniques has given rise to a number of online and stand-alone tools to find and score CRISPR sites for whole genomes. Here we describe the Wellcome Trust Sanger Institute Genome Editing database (WGE), which uses novel methods to compute, visualize and select optimal CRISPR sites in a genome browser environment. The WGE database currently stores single and paired CRISPR sites and pre-calculated off-target information for CRISPRs located in the mouse and human exomes. Scoring and display of off-target sites is simple, and intuitive, and filters can be applied to identify high-quality CRISPR sites rapidly. WGE also provides a tool for the design and display of gene targeting vectors in the same genome browser, along with gene models, protein translation and variation tracks. WGE is open, extensible and can be set up to compute and present CRISPR sites for any genome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79233, 2015, 'bio', 'Likelihood-based complex trait association testing for arbitrary depth sequencing data', 'Yan, Song|Yuan, Shuai|Xu, Zheng|Zhang, Baqun|Zhang, Bo|Kang, Guolian|Byrnes, Andrea|Li, Yun', 'Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, Peoples Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Childrens Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.', 'China|China|China|China|China|China|China|China', 0, 'Computer Simulation|Gene Frequency|Genetic Association Studies|Genetic Markers|Genetic Variation|Genotype|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Phenotype|Quantitative Trait Loci|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv307', '25979475', 'In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment, while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to xe2x88xbc60% when the causal variant is of low frequency (MAFxe2x80x89<xe2x80x890.01).'),
(79234, 2015, 'bio', 'CoRegNet: reconstruction and integrated analysis of co-regulatory networks', 'Nicolle, Rémy|Radvanyi, François|Elati, Mohamed', 'iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France.|iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France.|iSSB, CNRS, University of Evry, Genopole, 91030 Evry Cedex, France, Institut Curie, PSL Research University, 75248 Cedex 05, France and CNRS UMR144, 75248 Cedex 05, France.', 'France|France|France', 0, 'Algorithms|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|Databases, Genetic|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Software|Transcription Factors|Urinary Bladder Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv305', '25979476', 'CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer.'),
(79235, 2015, 'bio', 'Message from ISCB: Outstanding contributions to ISCB award', 'Fogg, Christiana N|Kovats, Diane E', 'Freelance Science Writer, Kensington, MD, USA and Executive Director, International Society of Computational Biology, La Jolla, CA.|Freelance Science Writer, Kensington, MD, USA and Executive Director, International Society of Computational Biology, La Jolla, CA.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btv206', '25979477', '-'),
(79236, 2015, 'bio', 'IMSEQ--a fast and error aware approach to immunogenetic sequence analysis', 'Kuchenbecker, Leon|Nienen, Mikalai|Hecht, Jochen|Neumann, Avidan U|Babel, Nina|Reinert, Knut|Robinson, Peter N', 'Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.|Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies, Charité Universitätsmedizin, Berlin, Department of Computer Science, Freie Universität, Berlin, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel, Marien Hospital Herne, Ruhr University Bochum, Bochum and Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Simulation|Genes, T-Cell Receptor beta|High-Throughput Nucleotide Sequencing|Humans|Immunogenetics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv309', '25987567', 'Recombined T- and B-cell receptor repertoires are increasingly being studied using next generation sequencing (NGS) in order to interrogate the repertoire composition as well as changes in the distribution of receptor clones under different physiological and disease states. This type of analysis requires efficient and unambiguous clonotype assignment to a large number of NGS read sequences, including the identification of the incorporated V and J gene segments and the CDR3 sequence. Current tools have deficits with respect to performance, accuracy and documentation of their underlying algorithms and usage.'),
(79237, 2015, 'bio', 'TRAL: tandem repeat annotation library', 'Schaper, Elke|Korsunsky, Alexander|Pexc4x8derska, Jxc5xablija|Messina, Antonio|Murri, Riccardo|Stockinger, Heinz|Zoller, Stefan|Xenarios, Ioannis|Anisimova, Maria', 'Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4de|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4de|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.|Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland, Department of Computer Science, ETH Zxc3xbcrich, 8092 Zxc3xbcrich, Switzerland, Graz University of Technology, Institute of Molecular Biotechnology, 8010 Graz, Austria, Department of Biosystems Science and Engineering, ETH Zxc3xbcrich, 4058 Basel, Switzerland, Services and Support for Science IT, University of Zxc3xbcrich, 8057 Zxc3xbcrich, Switzerland and Institute of Applied Simulations, School of Life Sciences und Facility Management, Zxc3xbcrich University of Applied Sciences, 8820 Wxc3xa4denswil, Switzerland.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 4, 'Amino Acid Sequence|Cluster Analysis|Databases, Protein|Documentation|Gene Library|Humans|Knowledge Bases|Molecular Sequence Annotation|Molecular Sequence Data|Software|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btv306', '25987568', 'Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results.'),
(79238, 2015, 'bio', 'DSigDB: drug signatures database for gene set analysis', 'Yoo, Minjae|Shin, Jimin|Kim, Jihye|Ryall, Karen A|Lee, Kyubum|Lee, Sunwon|Jeon, Minji|Kang, Jaewoo|Tan, Aik Choon', 'Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.|Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.', '-|-|-|-|-|-|-|-|-', 0, 'Carcinoma, Non-Small-Cell Lung|Computational Biology|Databases, Pharmaceutical|Drug Repositioning|ErbB Receptors|Gene Expression Profiling|Gene Expression Regulation|Humans|Lung Neoplasms|Mutation|Protein Kinase Inhibitors|Software', 'Journal Article', '10.1093/bioinformatics/btv313', '25990557', 'We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22xe2x80x89527 gene sets, consists of 17xe2x80x89389 unique compounds covering 19xe2x80x89531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research.'),
(79239, 2015, 'bio', 'Computer vision-based automated peak picking applied to protein NMR spectra', 'Klukowski, Piotr|Walczak, Michal J|Gonczarek, Adam|Boudet, Julien|Wider, Gerhard', 'Department of Computer Science, Wroclaw University of Technology, Wroclaw, Poland and.|Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland.|Department of Computer Science, Wroclaw University of Technology, Wroclaw, Poland and.|Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland.|Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland.', 'Poland|Switzerland|Poland|Switzerland|Switzerland', 14, 'Algorithms|Humans|Image Processing, Computer-Assisted|Nuclear Magnetic Resonance, Biomolecular|Pattern Recognition, Visual|Proteins', 'Journal Article', '10.1093/bioinformatics/btv318', '25995228', 'A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a blind algorithm.'),
(79240, 2015, 'bio', 'agplus: a rapid and flexible tool for aggregation plots', 'Maehara, Kazumitsu|Ohkawa, Yasuyuki', 'Department of Advanced Medical Initiatives, JST-CREST, Faculty of Medicine, Kyushu University, Fukuoka 812-8582, Japan.|Department of Advanced Medical Initiatives, JST-CREST, Faculty of Medicine, Kyushu University, Fukuoka 812-8582, Japan.', 'Japan|Japan', 0, 'Binding Sites|Chromatin Immunoprecipitation|High-Throughput Nucleotide Sequencing|Humans|Regulatory Sequences, Nucleic Acid|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv322', '25995229', 'Aggregation plots are frequently used to evaluate signal distributions at user-interested points in ChIP-Seq data analysis. agplus, a new and simple command-line tool, enables rapid and flexible generation of text tables tailored for aggregation plots from which users can easily design multiple groups based on user-definitions such as regulatory regions or transcription initiation sites.'),
(79241, 2015, 'bio', 'Trans-species learning of cellular signaling systems with bimodal deep belief networks', 'Chen, Lujia|Cai, Chunhui|Chen, Vicky|Lu, Xinghua', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.', 'United States|United States|United States|United States', 19, 'Algorithms|Animals|Bronchi|Humans|Models, Theoretical|Phosphorylation|Protein Interaction Maps|Proteomics|Rats|Signal Transduction|Software|Species Specificity|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv315', '25995230', 'Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.'),
(79242, 2015, 'bio', 'ABC: a tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments', 'Bailey, Swneke D|Virtanen, Carl|Haibe-Kains, Benjamin|Lupien, Mathieu', 'Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Alleles|Breast Neoplasms|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Female|Hepatocyte Nuclear Factor 3-alpha|High-Throughput Nucleotide Sequencing|Humans|Mutation|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv321', '25995231', 'Detection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele-specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs.'),
(79243, 2015, 'bio', 'When less is more: slicing sequencing data improves read decoding accuracy and de novo assembly quality', 'Lonardi, Stefano|Mirebrahim, Hamid|Wanamaker, Steve|Alpert, Matthew|Ciardo, Gianfranco|Duma, Denisa|Close, Timothy J', 'Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.|Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Algorithms|Chromosomes, Artificial, Bacterial|Computational Biology|Fabaceae|High-Throughput Nucleotide Sequencing|Hordeum|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv311', '25995232', 'As the invention of DNA sequencing in the 70s, computational biologists have had to deal with the problem of de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing.'),
(79244, 2015, 'bio', 'Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach', 'Henriques, David|Rocha, Miguel|Saez-Rodriguez, Julio|Banga, Julio R', 'Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'Spain|Spain|Spain|Spain', 15, 'Algorithms|Bacteria|Computer Simulation|Gene Expression Regulation|Gene Regulatory Networks|Humans|Liver Neoplasms|Logic|Models, Biological|Models, Statistical|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv314', '26002881', 'Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79245, 2015, 'bio', 'PopGeV: a web-based large-scale population genome browser', 'Shi, Xinyi|Peng, Jing|Yu, Xiaohan|Zhang, Xiaohong|Li, Dongye|Liu, Baohui|Kong, Fanjiang|Yuan, Xiaohui', 'The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China, University of Chinese Academy of Sciences, Beijing, China, School of Computer Science and Technology, Heilongjiang University, Harbin, China, College of Electronic and Information, Northeast Agricultural University, Harbin, China and School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.', 'China|China|China|China|China|China|China|China', 0, 'Computational Biology|Databases, Factual|Genome, Human|Humans|INDEL Mutation|Internet|Metagenomics|Polymorphism, Single Nucleotide|User-Computer Interface|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv324', '26002882', 'The development of high-throughput sequencing technology has made it possible for more and more researchers to use population sequencing data to mine genes associated with specific traits. However, the massive amounts of sequencing data have also brought new challenges to the researchers. The question of how to browse population genomic data in an easy and intuitive manner must be addressed. Web-based genome browsers allow user to conveniently view the results of genomic analyses, but heavy usage can reduce the response speed of the webpage, which limits its usefulness in the display of large-scale genome data. IndexedDB technology is a good solution to this problem, it supports web browsers and so creates local databases. In this way, data can be read from the local storage, achieving a smooth display of population genomic data.'),
(79246, 2015, 'bio', 'pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge', 'Wachter, Astrid|Beißbarth, Tim', 'Department of Medical Statistics, Georg-August-University Göttingen, Germany.|Department of Medical Statistics, Georg-August-University Göttingen, Germany.', 'Germany|Germany', 21, 'Bayes Theorem|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Databases, Factual|Genomics|Humans|Proteomics|Signal Transduction|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btv323', '26002883', 'Characterization of biological processes is progressively enabled with the increased generation of omics data on different signaling levels. Here we present a straightforward approach for the integrative analysis of data from different high-throughput technologies based on pathway and interaction models from public databases. pwOmics performs pathway-based level-specific data comparison of coupled human proteomic and genomic/transcriptomic datasets based on their log fold changes. Separate downstream and upstream analyses results on the functional levels of pathways, transcription factors and genes/transcripts are performed in the cross-platform consensus analysis. These provide a basis for the combined interpretation of regulatory effects over time. Via network reconstruction and inference methods (Steiner tree, dynamic Bayesian network inference) consensus graphical networks can be generated for further analyses and visualization.'),
(79247, 2015, 'bio', 'tmlenpvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation', 'Chambaz, Antoine|Neuvial, Pierre', 'ModalX, Université Paris Ouest Nanterre, Nanterre, France and Laboratoire de Mathématiques et Modélisation dÉvry, Université dÉvry val dEssonne, UMR CNRS 8071, ENSIIE, USC INRA, Évry, France.|ModalX, Université Paris Ouest Nanterre, Nanterre, France and Laboratoire de Mathématiques et Modélisation dÉvry, Université dÉvry val dEssonne, UMR CNRS 8071, ENSIIE, USC INRA, Évry, France.', 'France|France', 0, 'Algorithms|Breast Neoplasms|Computational Biology|DNA Copy Number Variations|DNA Methylation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genome, Human|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv320', '26002884', 'We describe the implementation of the method introduced by Chambaz et al. in 2012. We also demonstrate its genome-wide application to the integrative search of new regions with strong association between DNA copy number and gene expression accounting for DNA methylation in breast cancers.'),
(79248, 2015, 'bio', 'Bayesian mixture analysis for metagenomic community profiling', 'Morfopoulou, Sofia|Plagnol, Vincent', 'UCL Genetics Institute, University College London, London WC1E 6BT, UK.|UCL Genetics Institute, University College London, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Bayes Theorem|Biosurveillance|Computational Biology|DNA, Bacterial|DNA, Viral|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Metagenomics|Mice|Monte Carlo Method|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv317', '26002885', 'Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture.'),
(79249, 2015, 'bio', 'Diffusion maps for high-dimensional single-cell analysis of differentiation data', 'Haghverdi, Laleh|Buettner, Florian|Theis, Fabian J', 'Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany.|Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany.|Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Animals|Blastocyst|Cell Differentiation|Cluster Analysis|Diffusion|Embryonic Stem Cells|Gene Expression Regulation|Hematopoietic Stem Cells|High-Throughput Nucleotide Sequencing|Humans|Mice|Principal Component Analysis|Probability|Real-Time Polymerase Chain Reaction|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btv325', '26002886', 'Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.'),
(79250, 2015, 'bio', 'Message from the ISCB: 2015 ISCB Accomplishment by a Senior Scientist Award: Cyrus Chothia', 'Fogg, Christiana N|Kovats, Diane E', 'Freelance Science Writer, Kensington, MD, USA and Executive Director, International Society for Computational Biology, La Jolla, CA, USA.|Freelance Science Writer, Kensington, MD, USA and Executive Director, International Society for Computational Biology, La Jolla, CA, USA.', 'United States|United States', 0, 'Awards and Prizes|Biomedical Research|Computational Biology|History, 20th Century|History, 21st Century|Humans|Molecular Biology|Societies, Scientific', 'Biography', '10.1093/bioinformatics/btv218', '26002905', 'The International Society for Computational Biology (ISCB, http://www.iscb.org) honors a senior scientist annually for his or her outstanding achievements with the ISCB Accomplishment by a Senior Scientist Award. This award recognizes a leader in the field of computational biology for his or her significant contributions to the community through research, service and education. Cyrus Chothia, an emeritus scientist at the Medical Research Council Laboratory of Molecular Biology and emeritus fellow of Wolfson College at Cambridge University, England, is the 2015 ISCB Accomplishment by a Senior Scientist Award winner.Chothia was selected by the Awards Committee, which is chaired by Dr Bonnie Berger of the Massachusetts Institute of Technology. He will receive his award and deliver a keynote presentation at 2015 Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Dublin, Ireland, in July 2015.'),
(79251, 2015, 'bio', 'A novel essential domain perspective for exploring gene essentiality', 'Lu, Yao|Lu, Yulan|Deng, Jingyuan|Peng, Hai|Lu, Hui|Lu, Long Jason', 'Shanghai Institute of Medical Genetics, Shanghai Childrens Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, Peoples Republic of China.|State Key Laboratory of Genetic Engineering Institute of Biostatistics, School of Life Science, Fudan University, Shanghai 200433, Peoples Republic of China.|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH 45229, USA.|Institute for Systems Biology, Jianghan University, Wuhan, Hubei, Peoples Republic of China.|Shanghai Institute of Medical Genetics, Shanghai Childrens Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, Peoples Republic of China, Department of Bioengineering (MC 063), University of Illinois at Chicago, Chicago, IL 60607-7052, USA and Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China.|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH 45229, USA, Institute for Systems Biology, Jianghan University, Wuhan, Hubei, Peoples Republic of China.', 'China|China|United States|China|China|China', 4, 'Bacteria|Computer Simulation|Fungi|Genes, Bacterial|Genes, Essential|Genes, Fungal|Models, Theoretical|Protein Structure, Tertiary|RNA, Ribosomal', 'Journal Article', '10.1093/bioinformatics/btv312', '26002906', 'Genes with indispensable functions are identified as essential, however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain.'),
(79252, 2015, 'bio', 'IgSimulator: a versatile immunosequencing simulator', 'Safonova, Yana|Lapidus, Alla|Lill, Jennie', 'Center of Algorithmic biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia and.|Center of Algorithmic biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia and.|Department of Protein Chemistry, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA.', 'United States|United States|United States', 0, 'Algorithms|Antibodies|Computational Biology|Computer Simulation|Databases, Factual|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv326', '26007226', 'The recent introduction of next-generation sequencing technologies to antibody studies have resulted in a growing number of immunoinformatics tools for antibody repertoire analysis. However, benchmarking these newly emerging tools remains problematic since the gold standard datasets that are needed to validate these tools are typically not available.'),
(79253, 2015, 'bio', 'Epigenomic k-mer dictionaries: shedding light on how sequence composition influences in vivo nucleosome positioning', 'Giancarlo, Raffaele|Rombo, Simona E|Utro, Filippo', 'Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, 90123 Palermo, Italy and Computational Biology Center, IBM T. J. Watson Research, NY 10598, USA.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, 90123 Palermo, Italy and Computational Biology Center, IBM T. J. Watson Research, NY 10598, USA.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, 90123 Palermo, Italy and Computational Biology Center, IBM T. J. Watson Research, NY 10598, USA.', 'Italy|Italy|Italy', 15, 'Animals|Chromatin Assembly and Disassembly|Epigenomics|Genome|Humans|Nucleosomes|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv295', '26007227', 'Information-theoretic and compositional analysis of biological sequences, in terms of k-mer dictionaries, has a well established role in genomic and proteomic studies. Much less so in epigenomics, although the role of k-mers in chromatin organization and nucleosome positioning is particularly relevant. Fundamental questions concerning the informational content and compositional structure of nucleosome favouring and disfavoring sequences with respect to their basic building blocks still remain open.'),
(79254, 2015, 'bio', 'GC3-biased gene domains in mammalian genomes', 'Shen, Wenlong|Wang, Dong|Ye, Bingyu|Shi, Minglei|Ma, Lei|Zhang, Yan|Zhao, Zhihu', 'Beijing Institute of Biotechnology, Beijing 100071, China.|Beijing Institute of Biotechnology, Beijing 100071, China.|Beijing Institute of Biotechnology, Beijing 100071, China, College of Life Sciences, Capital Normal University, Beijing 100048, China and.|Beijing Institute of Biotechnology, Beijing 100071, China.|College of Life Sciences, Shihezi University, Shihezi 832003, China.|Beijing Institute of Biotechnology, Beijing 100071, China.|Beijing Institute of Biotechnology, Beijing 100071, China.', 'China|China|China|China|China|China|China', 0, 'Animals|Base Composition|Codon|Genes|Humans|Mammals|Mice|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btv329', '26019240', 'Synonymous codon usage bias has been shown to be correlated with many genomic features among different organisms. However, the biological significance of codon bias with respect to gene function and genome organization remains unclear.'),
(79255, 2015, 'bio', 'xHeinz: an algorithm for mining cross-species network modules under a flexible conservation model', 'El-Kebir, Mohammed|Soueidan, Hayssam|Hume, Thomas|Beisser, Daniela|Dittrich, Marcus|Müller, Tobias|Blin, Guillaume|Heringa, Jaap|Nikolski, Macha|Wessels, Lodewyk F A|Klau, Gunnar W', 'Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Computational Cancer Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Univ. Bordeaux, CBiB, 33000 Bordeaux, France, Univ. Bordeaux, CNRS/LaBRI, 33405 Talence, France.|Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen.|Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany, Institute of Human Genetics, University of Würzburg, Würzburg, Germany and.|Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.|Univ. Bordeaux, CNRS/LaBRI, 33405 Talence, France.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands.|Univ. Bordeaux, CBiB, 33000 Bordeaux, France, Univ. Bordeaux, CNRS/LaBRI, 33405 Talence, France.|Computational Cancer Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands, Erable Team, INRIA, Lyon, France.', 'Netherlands|Netherlands|France|United States|Germany|Germany|France|Netherlands|France|Netherlands|France', 1, 'Algorithms|Animals|Data Mining|Gene Expression Profiling|Humans|Infant, Newborn|Mice|Models, Biological|Protein Interaction Maps|Species Specificity|Th17 Cells', 'Journal Article', '10.1093/bioinformatics/btv316', '26023104', 'Integrative network analysis methods provide robust interpretations of differential high-throughput molecular profile measurements. They are often used in a biomedical context-to generate novel hypotheses about the underlying cellular processes or to derive biomarkers for classification and subtyping. The underlying molecular profiles are frequently measured and validated on animal or cellular models. Therefore the results are not immediately transferable to human. In particular, this is also the case in a study of the recently discovered interleukin-17 producing helper T cells (Th17), which are fundamental for anti-microbial immunity but also known to contribute to autoimmune diseases.'),
(79256, 2015, 'bio', 'antaRNA: ant colony-based RNA sequence design', 'Kleinkauf, Robert|Mann, Martin|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany, Center for Biological Signaling Studies (BIOSS), University of Freiburg, Germany, Center for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.', 'Germany|Germany|Germany', 0, 'Animals|Ants|Base Composition|Base Sequence|Databases, Genetic|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv319', '26023105', 'RNA sequence design is studied at least as long as the classical folding problem. Although for the latter the functional fold of an RNA molecule is to be found ,: inverse folding tries to identify RNA sequences that fold into a function-specific target structure. In combination with RNA-based biotechnology and synthetic biology ,: reliable RNA sequence design becomes a crucial step to generate novel biochemical components.'),
(79257, 2015, 'bio', 'Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis', 'Gibb, Sebastian|Strimmer, Korbinian', 'Anesthesiology and Intensive Care Medicine, University Hospital Greifswald, Ferdinand-Sauerbruch-Straße, D-17475 Greifswald, Germany and.|Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK.', 'Germany|United Kingdom', 0, 'Biomarkers, Tumor|Data Interpretation, Statistical|Discriminant Analysis|Humans|Mass Spectrometry|Pancreatic Neoplasms|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv334', '26026136', 'Proteomic mass spectrometry analysis is becoming routine in clinical diagnostics, for example to monitor cancer biomarkers using blood samples. However, differential proteomics and identification of peaks relevant for class separation remains challenging.'),
(79258, 2015, 'bio', 'ACE: accurate correction of errors using K-mer tries', 'Sheikhizadeh, Siavash|de Ridder, Dick', 'Bioinformatics Group, Wageningen University, 6700 AP Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, 6700 AP Wageningen, The Netherlands.', 'Netherlands|Netherlands', 7, 'Algorithms|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv332', '26026137', 'The quality of high-throughput next-generation sequencing data significantly influences the performance and memory consumption of assembly and mapping algorithms. The most ubiquitous platform, Illumina, mainly suffers from substitution errors. We have developed a tool, ACE, based on K-mer tries to correct such errors. On real MiSeq and HiSeq Illumina archives, ACE yields higher gains in terms of coverage depth, outperforming state-of-the-art competitors in the majority of cases.'),
(79259, 2015, 'bio', 'QVZ: lossy compression of quality values', 'Malysa, Greg|Hernaez, Mikel|Ochoa, Idoia|Rao, Milind|Ganesan, Karthik|Weissman, Tsachy', 'Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Data Compression|Databases, Genetic|Genotype|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv330', '26026138', 'Recent advancements in sequencing technology have led to a drastic reduction in the cost of sequencing a genome. This has generated an unprecedented amount of genomic data that must be stored, processed and transmitted. To facilitate this effort, we propose a new lossy compressor for the quality values presented in genomic data files (e.g. FASTQ and SAM files), which comprise roughly half of the storage space (in the uncompressed domain). Lossy compression allows for compression of data beyond its lossless limit.'),
(79260, 2015, 'bio', 'RPdb: a database of experimentally verified cellular reprogramming records', 'Shen, Yi|Gao, Fan|Wang, Minghui|Li, Ao', 'School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.|School of Life Science and.|School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, AH230037, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, AH230037, China.', 'China|-|China|China', 1, 'Cell Differentiation|Cells, Cultured|Cellular Reprogramming|Databases, Factual|Humans|Induced Pluripotent Stem Cells|MicroRNAs|Pharmaceutical Preparations|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv331', '26026167', 'Many cell lines can be reprogrammed to other cell lines by forced expression of a few transcription factors or by specifically designed culture methods, which have attracted a great interest in the field of regenerative medicine and stem cell research. Plenty of cell lines have been used to generate induced pluripotent stem cells (IPSCs) by expressing a group of genes and microRNAs. These IPSCs can differentiate into somatic cells to promote tissue regeneration. Similarly, many somatic cells can be directly reprogrammed to other cells without a stem cell state. All these findings are helpful in searching for new reprogramming methods and understanding the biological mechanism inside. However, to the best of our knowledge, there is still no database dedicated to integrating the reprogramming records. We built RPdb (cellular reprogramming database) to collect cellular reprogramming information and make it easy to access. All entries in RPdb are manually extracted from more than 2000 published articles, which is helpful for researchers in regenerative medicine and cell biology.'),
(79261, 2015, 'bio', 'lpNet: a linear programming approach to reconstruct signal transduction networks', 'Matos, Marta R A|Knapp, Bettina|Kaderali, Lars', 'Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and.|Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.|Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and.', 'Germany|Germany|Germany', 3, 'Computer Graphics|Genome, Human|Genomics|Humans|Metabolic Networks and Pathways|Programming, Linear|Signal Transduction|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btv327', '26026168', 'With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into diseases mechanisms of action. We have implemented the approach as an R package available through bioconductor.'),
(79262, 2015, 'bio', 'DisVis: quantifying and visualizing accessible interaction space of distance-restrained biomolecular complexes', 'van Zundert, G C P|Bonvin, A M J J', 'Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3584CH, The Netherlands.|Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3584CH, The Netherlands.', 'Netherlands|Netherlands', 0, 'Computer Graphics|Data Interpretation, Statistical|Proteasome Endopeptidase Complex|Protein Interaction Maps|Protein Subunits|RNA Polymerase II|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btv333', '26026169', 'We present DisVis, a Python package and command line tool to calculate the reduced accessible interaction space of distance-restrained binary protein complexes, allowing for direct visualization and quantification of the information content of the distance restraints. The approach is general and can also be used as a knowledge-based distance energy term in FFT-based docking directly during the sampling stage.'),
(79263, 2015, 'bio', 'Mango: a bias-correcting ChIA-PET analysis pipeline', 'Phanstiel, Douglas H|Boyle, Alan P|Heidari, Nastaran|Snyder, Michael P', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 and.|Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 and.|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 and.', 'United States|United States|United States|United States', 48, 'Chromatin|Chromatin Immunoprecipitation|Humans|K562 Cells|Nucleic Acid Conformation|Sequence Analysis, DNA|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv336', '26034063', 'Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity.'),
(79264, 2015, 'bio', 'FourCSeq: analysis of 4C sequencing data', 'Klein, Felix A|Pakozdi, Tibor|Anders, Simon|Ghavi-Helm, Yad|Furlong, Eileen E M|Huber, Wolfgang', 'European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 46, 'Chromosomes|Genome|High-Throughput Nucleotide Sequencing|Nucleic Acid Conformation|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv335', '26034064', 'Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the viewpoint with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions.'),
(79265, 2015, 'bio', 'FinisherSC: a repeat-aware tool for upgrading de novo assembly using long reads', 'Lam, Ka-Kit|LaButti, Kurt|Khalak, Asif|Tse, David', 'Department of Electrical Engineering and Computer Sciences, UC Berkeley.|U.S. Department of Energy Joint Genome Institute, Walnut Creek.|Pacific Biosciences, Menlo Park, and.|Department of Electrical Engineering and Computer Sciences, UC Berkeley, Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA.', 'United States|United States|United States|United States', 34, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|Drosophila|Genomics|High-Throughput Nucleotide Sequencing|Models, Statistical|Repetitive Sequences, Nucleic Acid|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv280', '26040454', 'We introduce FinisherSC, a repeat-aware and scalable tool for upgrading de novo assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.'),
(79266, 2015, 'bio', 'TiQuant: software for tissue analysis, quantification and surface reconstruction', 'Friebel, Adrian|Neitsch, Johannes|Johann, Tim|Hammad, Seddik|Hengstler, Jan G|Drasdo, Dirk|Hoehme, Stefan', 'Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany.|Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany.|Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany.|Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany, Department of Forensic Medicine and Veterinary Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt and.|Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany.|Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany, Institut National de Recherche en Informatique et en Automatique (INRIA) Unit Rocquencourt, Le Chesnay Cedex, France.|Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany, Institut National de Recherche en Informatique et en Automatique (INRIA) Unit Rocquencourt, Le Chesnay Cedex, France.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 24, 'Algorithms|Computer Graphics|Humans|Image Processing, Computer-Assisted|Organ Specificity|Software|Systems Biology|Tomography, X-Ray Computed', 'Journal Article', '10.1093/bioinformatics/btv346', '26040455', 'TiQuant is a modular software tool for efficient quantification of biological tissues based on volume data obtained by biomedical image modalities. It includes a number of versatile image and volume processing chains tailored to the analysis of different tissue types which have been experimentally verified. TiQuant implements a novel method for the reconstruction of three-dimensional surfaces of biological systems, data that often cannot be obtained experimentally but which is of utmost importance for tissue modelling in systems biology.'),
(79267, 2015, 'bio', 'Assembling short reads from jumping libraries with large insert sizes', 'Vasilinetc, Irina|Prjibelski, Andrey D|Gurevich, Alexey|Korobeynikov, Anton|Pevzner, Pavel A', 'Algorithmic Biology Lab, St. Petersburg Academic University 194021.|Algorithmic Biology Lab, St. Petersburg Academic University 194021, Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St.Petersburg State University, 199004.|Algorithmic Biology Lab, St. Petersburg Academic University 194021, Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St.Petersburg State University, 199004.|Algorithmic Biology Lab, St. Petersburg Academic University 194021, Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St.Petersburg State University, 199004, Department of Mathematics and Mechanics, St. Petersburg State University, St. Petersburg, 198504, Russia and.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St.Petersburg State University, 199004, Department of Computer Science and Engineering, University of California, San Diego, CA 92093-0404, USA.', 'United States|United States|United States|United States|United States', 17, 'Algorithms|Gene Library|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv337', '26040456', 'Advances in Next-Generation Sequencing technologies and sample preparation recently enabled generation of high-quality jumping libraries that have a potential to significantly improve short read assemblies. However, assembly algorithms have to catch up with experimental innovations to benefit from them and to produce high-quality assemblies.'),
(79268, 2015, 'bio', 'A web application for the unspecific detection of differentially expressed DNA regions in strand-specific expression data', 'Juanes, José M|Miguel, Ana|Morales, Lucas J|Pérez-Ortín, José E|Arnau, Vicente', 'Departamento de Informática, Escola Tècnica Superior dEnginyeria.|Departamento de Bioquímica y Biología Molecular, Facultad de Biología and E.R.I. Biotecmed, Universitat de València, Burjassot, Spain.|Departamento de Bioquímica y Biología Molecular, Facultad de Biología and.|Departamento de Bioquímica y Biología Molecular, Facultad de Biología and E.R.I. Biotecmed, Universitat de València, Burjassot, Spain.|Departamento de Informática, Escola Tècnica Superior dEnginyeria.', '-|Spain|-|Spain|-', 0, 'Algorithms|Computational Biology|Computer Graphics|DNA|Gene Expression Profiling|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv343', '26040457', 'Genomic technologies allow laboratories to produce large-scale data sets, either through the use of next-generation sequencing or microarray platforms. To explore these data sets and obtain maximum value from the data, researchers view their results alongside all the known features of a given reference genome. To study transcriptional changes that occur under a given condition, researchers search for regions of the genome that are differentially expressed between different experimental conditions. In order to identify these regions several algorithms have been developed over the years, along with some bioinformatic platforms that enable their use. However, currently available applications for comparative microarray analysis exclusively focus on changes in gene expression within known transcribed regions of predicted protein-coding genes, the changes that occur in non-predictable genetic elements, such as non-coding RNAs. Here, we present a web application for the visualization of strand-specific tiling microarray or next-generation sequencing data that allows customized detection of differentially expressed regions all along the genome in an unspecific manner, that allows identification of all RNA sequences, predictable or not.'),
(79269, 2015, 'bio', 'CCLasso: correlation inference for compositional data through Lasso', 'Fang, Huaying|Huang, Chengcheng|Zhao, Hongyu|Deng, Minghua', 'LMAN, School of Mathematical Sciences, Beijing International Center for Mathematical Research, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.|College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.|Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA and.|LMAN, School of Mathematical Sciences, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China, Center for Statistical Science, Peking University, Beijing 100871, China.', 'China|China|-|China', 48, 'Algorithms|Bacteria|Computational Biology|Computer Simulation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bioinformatics/btv349', '26048598', 'Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis treating the observed data as absolute abundances of the microbes may lead to spurious results because the data only represent relative abundances. Special care and appropriate methods are required prior to correlation analysis for these compositional data.'),
(79270, 2015, 'bio', 'Sparse multi-view matrix factorization: a multivariate approach to multiple tissue comparisons', 'Wang, Zi|Yuan, Wei|Montana, Giovanni', 'Department of Mathematics, Imperial College London, London SW7 2AZ.|Department of Twin Research and Genetic Epidemiology, Kings College London, St Thomas Hospital, London SE1 7EH and.|Department of Mathematics, Imperial College London, London SW7 2AZ, Department of Biomedical Engineering, Kings College London, St Thomas Hospital, London SE1 7EH, UK.', 'United Kingdom|United Kingdom|United Kingdom', 3, 'Adipose Tissue|Adult|Algorithms|Cells, Cultured|Cohort Studies|Gene Expression Profiling|Gene Expression Regulation|Humans|Lymphocytes|Organ Specificity|Phenotype|Skin|Twin Studies as Topic', 'Comparative Study', '10.1093/bioinformatics/btv344', '26048599', 'Within any given tissue, gene expression levels can vary extensively among individuals. Such heterogeneity can be caused by genetic and epigenetic variability and may contribute to disease. The abundance of experimental data now enables the identification of features of gene expression profiles that are shared across tissues and those that are tissue-specific. While most current research is concerned with characterizing differential expression by comparing mean expression profiles across tissues, it is believed that a significant difference in a gene expressions variance across tissues may also be associated with molecular mechanisms that are important for tissue development and function.'),
(79271, 2015, 'bio', 'Quantitative frame analysis and the annotation of GC-rich (and other) prokaryotic genomes An application to Anaeromyxobacter dehalogenans', 'Oden, Steve|Brocchieri, Luciano', 'Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32610, USA and Genetics Institute, University of Florida, Gainesville, FL 32610, USA.|Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32610, USA and Genetics Institute, University of Florida, Gainesville, FL 32610, USA.', 'United States|United States', 1, 'Codon|GC Rich Sequence|Genome, Bacterial|Genomics|Molecular Sequence Annotation|Myxococcales|Open Reading Frames|Software', 'Journal Article', '10.1093/bioinformatics/btv339', '26048600', 'Graphical representations of contrasts in GC usage among codon frame positions (frame analysis) provide evidence of genes missing from the annotations of prokaryotic genomes of high GC content but the qualitative approach of visual frame analysis prevents its applicability on a genomic scale.'),
(79272, 2015, 'bio', 'GeIST: a pipeline for mapping integrated DNA elements', 'LaFave, Matthew C|Varshney, Gaurav K|Burgess, Shawn M', 'Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-8004, USA.|Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-8004, USA.|Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-8004, USA.', 'United States|United States|United States', 4, 'Animals|Chromosome Mapping|Computational Biology|DNA Transposable Elements|Databases, Genetic|Dependovirus|Genetic Markers|Genomics|Humans|Leukemia Virus, Murine|Mice|Mutagenesis, Insertional|Workflow', 'Journal Article', '10.1093/bioinformatics/btv350', '26049161', 'There are several experimental contexts in which it is important to identify DNA integration sites, such as insertional mutagenesis screens, gene and enhancer trap applications, and gene therapy. We previously developed an assay to identify millions of integrations in multiplexed barcoded samples at base-pair resolution. The sheer amount of data produced by this approach makes the mapping of individual sites non-trivial without bioinformatics support. This article presents the Genomic Integration Site Tracker (GeIST), a command-line pipeline designed to map the integration sites produced by this assay and identify the samples from which they came. GeIST version 2.1.0, a more adaptable version of our original pipeline, can identify integrations of murine leukemia virus, adeno-associated virus, Tol2 transposons or Ac/Ds transposons, and can be adapted for other inserted elements. It has been tested on experimental data for each of these delivery vectors and fine-tuned to account for sequencing and cloning artifacts.');
