INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77860, 2013, 'bio', 'Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge', 'Tarca, Adi L|Lauria, Mario|Unger, Michael|Bilal, Erhan|Boue, Stephanie|Kumar Dey, Kushal|Hoeng, Julia|Koeppl, Heinz|Martin, Florian|Meyer, Pablo|Nandy, Preetam|Norel, Raquel|Peitsch, Manuel|Rice, Jeremy J|Romero, Roberto|Stolovitzky, Gustavo|Talikka, Marja|Xiang, Yang|Zechner, Christoph|IMPROVER DSC Collaborators', 'Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.|Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 81, 'Disease|Gene Expression Profiling|Humans|Lung Neoplasms|Molecular Diagnostic Techniques|Multiple Sclerosis|Oligonucleotide Array Sequence Analysis|Phenotype|Psoriasis|Pulmonary Disease, Chronic Obstructive', 'Journal Article', '10.1093/bioinformatics/btt492', '23966112', 'After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein.'),
(77861, 2013, 'bio', 'MLML: consistent simultaneous estimates of DNA methylation and hydroxymethylation', 'Qu, Jianghan|Zhou, Meng|Song, Qiang|Hong, Elizabeth E|Smith, Andrew D', 'Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States|United States|United States', 18, '5-Methylcytosine|Animals|Cytosine|DNA|DNA Methylation|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt459', '23969133', 'The two major epigenetic modifications of cytosines, 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), coexist with each other in a range of mammalian cell populations. Increasing evidence points to important roles of 5-hmC in demethylation of 5-mC and epigenomic regulation in development. Recently developed experimental methods allow direct single-base profiling of either 5-hmC or 5-mC. Meaningful analyses seem to require combining these experiments with bisulfite sequencing, but doing so naively produces inconsistent estimates of 5-mC or 5-hmC levels.'),
(77862, 2013, 'bio', 'Bayesian Network Webserver: a comprehensive tool for biological network modeling', 'Ziebarth, Jesse D|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.|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, 'Bayes Theorem|Gene Regulatory Networks|Internet|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt472', '23969134', 'The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships.'),
(77863, 2013, 'bio', 'DNorm: disease name normalization with pairwise learning to rank', 'Leaman, Robert|Islamaj Dogan, Rezarta|Lu, Zhiyong', 'National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA and Department of Biomedical Informatics, Arizona State University, 13212 East Shea Blvd, Scottsdale, AZ 85259, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA and Department of Biomedical Informatics, Arizona State University, 13212 East Shea Blvd, Scottsdale, AZ 85259, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA and Department of Biomedical Informatics, Arizona State University, 13212 East Shea Blvd, Scottsdale, AZ 85259, USA.', 'United States|United States|United States', 183, 'Algorithms|Artificial Intelligence|Data Mining|Disease|Humans|Internet|PubMed|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btt474', '23969135', 'Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research.'),
(77864, 2013, 'bio', 'Genome compression: a novel approach for large collections', 'Deorowicz, Sebastian|Danek, Agnieszka|Grabowski, Szymon', 'Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland and Computer Engineering Department, Technical University of xc5x81xc3xb3dxc5xba, Al. Politechniki 11, 90-924 xc5x81xc3xb3dxc5xba, Poland.|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland and Computer Engineering Department, Technical University of xc5x81xc3xb3dxc5xba, Al. Politechniki 11, 90-924 xc5x81xc3xb3dxc5xba, Poland.|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland and Computer Engineering Department, Technical University of xc5x81xc3xb3dxc5xba, Al. Politechniki 11, 90-924 xc5x81xc3xb3dxc5xba, Poland.', 'Poland|Poland|Poland', 0, 'Algorithms|Arabidopsis|Base Sequence|Data Compression|Databases, Genetic|Genome, Human|Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt460', '23969136', 'Genomic repositories are rapidly growing, as witnessed by the 1000 Genomes or the UK10K projects. Hence, compression of multiple genomes of the same species has become an active research area in the past years. The well-known large redundancy in human sequences is not easy to exploit because of huge memory requirements from traditional compression algorithms.'),
(77865, 2013, 'bio', 'Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment', 'Yang, Jianyi|Roy, Ambrish|Zhang, Yang', 'Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.|Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.|Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.', 'United States|United States|United States', 339, 'Algorithms|Binding Sites|Databases, Protein|Humans|Ligands|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt447', '23975762', 'Identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem.'),
(77866, 2013, 'bio', 'RNAfbinv: an interactive Java application for fragment-based design of RNA sequences', 'Weinbrand, Lina|Avihoo, Assaf|Barash, Danny', 'Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel and Microsoft Research Israel, Herzliya 46733, Israel.|Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel and Microsoft Research Israel, Herzliya 46733, Israel.|Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel and Microsoft Research Israel, Herzliya 46733, Israel.', 'Israel|Israel|Israel', 11, 'Internet|Nucleic Acid Conformation|RNA|RNA Folding|Sequence Analysis, RNA|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btt494', '23975763', 'In RNA design problems, it is plausible to assume that the user would be interested in preserving a particular RNA secondary structure motif, or fragment, for biological reasons. The preservation could be in structure or sequence, or both. Thus, the inverse RNA folding problem could benefit from considering fragment constraints. We have developed a new interactive Java application called RNA fragment-based inverse that allows users to insert an RNA secondary structure in dot-bracket notation. It then performs sequence design that conforms to the shape of the input secondary structure, the specified thermodynamic stability, the specified mutational robustness and the user-selected fragment after shape decomposition. In this shape-based design approach, specific RNA structural motifs with known biological functions are strictly enforced, while others can possess more flexibility in their structure in favor of preserving physical attributes and additional constraints.'),
(77867, 2013, 'bio', 'NextGenMap: fast and accurate read mapping in highly polymorphic genomes', 'Sedlazeck, Fritz J|Rescheneder, Philipp|von Haeseler, Arndt', 'Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, A-1030 Vienna, Austria and Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Waehringerstrasse 17, A-1090 Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, A-1030 Vienna, Austria and Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Waehringerstrasse 17, A-1090 Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, A-1030 Vienna, Austria and Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Waehringerstrasse 17, A-1090 Vienna, Austria.', 'Austria|Austria|Austria', 0, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|Polymorphism, Genetic|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt468', '23975764', 'When choosing a read mapper, one faces the trade off between speed and the ability to map reads in highly polymorphic regions. Here, we report NextGenMap, a fast and accurate read mapper, which reduces this dilemma. NextGenMap aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome. At the same time, NextGenMap outperforms current mapping methods with respect to runtime and to the number of correctly mapped reads. NextGenMap efficiently uses the available hardware by exploiting multi-core CPUs as well as graphic cards (GPUs), if available. In addition, NextGenMap handles automatically any read data independent of read length and sequencing technology.'),
(77868, 2013, 'bio', 'GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data', 'Schmidt, Brian J|Ebrahim, Ali|Metz, Thomas O|Adkins, Joshua N|Palsson, Bernhard Ø|Hyduke, Daniel R', 'Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Genome|Metabolome|Metabolomics|Models, Biological|Salmonella typhimurium|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btt493', '23975765', 'Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed.'),
(77869, 2013, 'bio', 'RNA secondary structure diagrams for very large molecules: RNAfdl', 'Hecker, Nikolai|Wiegels, Tim|Torda, Andrew E', 'Centre for non-coding RNA in Technology and Health and Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, 1870 Frederiksberg, Denmark, European Molecular Biology Laboratory-Hamburg Outstation, c/o DESY, 22603 Hamburg, Germany and Centre for Bioinformatics, Institute for Biochemistry and Molecular Biology, University of Hamburg, 20146 Hamburg, Germany.|Centre for non-coding RNA in Technology and Health and Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, 1870 Frederiksberg, Denmark, European Molecular Biology Laboratory-Hamburg Outstation, c/o DESY, 22603 Hamburg, Germany and Centre for Bioinformatics, Institute for Biochemistry and Molecular Biology, University of Hamburg, 20146 Hamburg, Germany.|Centre for non-coding RNA in Technology and Health and Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, 1870 Frederiksberg, Denmark, European Molecular Biology Laboratory-Hamburg Outstation, c/o DESY, 22603 Hamburg, Germany and Centre for Bioinformatics, Institute for Biochemistry and Molecular Biology, University of Hamburg, 20146 Hamburg, Germany.', 'Germany|Germany|Germany', 0, 'Nucleic Acid Conformation|RNA|RNA, Ribosomal, 23S|Software', 'Journal Article', '10.1093/bioinformatics/btt496', '23975766', 'There are many programs that can read the secondary structure of an RNA molecule and draw a diagram, but hardly any that can cope with 10(3) bases. RNAfdl is slow but capable of producing intersection-free diagrams for ribosome-sized structures, has a graphical user interface for adjustments and produces output in common formats.'),
(77870, 2013, 'bio', 'catRAPID omics: a web server for large-scale prediction of protein-RNA interactions', 'Agostini, Federico|Zanzoni, Andreas|Klus, Petr|Marchese, Domenica|Cirillo, Davide|Tartaglia, Gian Gaetano', 'Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 97, '3 Untranslated Regions|Algorithms|Animals|Caenorhabditis elegans|Gene Expression Profiling|Humans|Internet|Mice|Nucleotide Motifs|Protein Structure, Tertiary|Proteomics|RNA|RNA-Binding Proteins|Rats|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt495', '23975767', 'Here we introduce catRAPID omics, a server for large-scale calculations of protein-RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level, (ii) use of protein and RNA sequences without size restriction, (iii) analysis of nucleic acid binding regions in proteins, and (iv) detection of RNA motifs involved in protein recognition.'),
(77871, 2013, 'bio', 'Identification of transcription factor binding sites from ChIP-seq data at high resolution', 'Bardet, Anaïs F|Steinmann, Jonas|Bafna, Sangeeta|Knoblich, Juergen A|Zeitlinger, Julia|Stark, Alexander', 'Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.|Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.|Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.|Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.|Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.|Research Institute of Molecular Pathology (IMP), Institute of Molecular Biotechnology (IMBA), Vienna, Austria and Stowers Institute for Medical Research, Kansas City, MO, USA.', 'Austria|Austria|Austria|Austria|Austria|Austria', 32, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|Drosophila|Drosophila Proteins|Enhancer Elements, Genetic|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, DNA|Transcription Factors|Twist-Related Protein 1', 'Evaluation Studies', '10.1093/bioinformatics/btt470', '23980024', 'Chromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) is widely used to study the in vivo binding sites of transcription factors (TFs) and their regulatory targets. Recent improvements to ChIP-seq, such as increased resolution, promise deeper insights into transcriptional regulation, yet require novel computational tools to fully leverage their advantages.'),
(77872, 2013, 'bio', 'MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples', 'Behr, Jonas|Kahles, André|Zhong, Yi|Sreedharan, Vipin T|Drewe, Philipp|Rätsch, Gunnar', 'Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.|Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.|Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.|Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.|Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.|Computational Biology Center, Sloan-Kettering Institute, 1275 York Avenue, New York, NY 10065, USA and Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Drosophila melanogaster|High-Throughput Nucleotide Sequencing|Humans|Internet|RNA|Sequence Analysis, RNA|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btt442', '23980025', 'High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction.'),
(77873, 2013, 'bio', 'Inferring nucleosome positions with their histone mark annotation from ChIP data', 'Mammana, Alessandro|Vingron, Martin|Chung, Ho-Ryun', 'Otto-Warburg-Laboratories, Epigenomics and Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.|Otto-Warburg-Laboratories, Epigenomics and Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.|Otto-Warburg-Laboratories, Epigenomics and Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.', 'Germany|Germany|Germany', 11, 'Algorithms|Chromatin Immunoprecipitation|High-Throughput Nucleotide Sequencing|Histones|Humans|Internet|K562 Cells|Nucleosomes|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btt449', '23981350', 'The nucleosome is the basic repeating unit of chromatin. It contains two copies each of the four core histones H2A, H2B, H3 and H4 and about 147 bp of DNA. The residues of the histone proteins are subject to numerous post-translational modifications, such as methylation or acetylation. Chromatin immunoprecipitiation followed by sequencing (ChIP-seq) is a technique that provides genome-wide occupancy data of these modified histone proteins, and it requires appropriate computational methods.'),
(77874, 2013, 'bio', 'A combined omics study on activated macrophages--enhanced role of STATs in apoptosis, immunity and lipid metabolism', 'Dinasarapu, Ashok Reddy|Gupta, Shakti|Ram Maurya, Mano|Fahy, Eoin|Min, Jun|Sud, Manish|Gersten, Merril J|Glass, Christopher K|Subramaniam, Shankar', 'Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.|Department of Bioengineering, San Diego Super Computer Center, Department of Cellular and Molecular Medicine and Department of Chemistry and Biochemistry, University of California San Diego, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 20, 'Animals|Apoptosis|Cell Cycle|Cytokines|Gene Expression Profiling|Immunity|Lipid Metabolism|Lipopolysaccharides|Macrophage Activation|Macrophages|Mice|NF-kappa B|Proteomics|STAT1 Transcription Factor|STAT3 Transcription Factor|Signal Transduction|Toll-Like Receptors', 'Journal Article', '10.1093/bioinformatics/btt469', '23981351', 'Macrophage activation by lipopolysaccharide and adenosine triphosphate (ATP) has been studied extensively because this model system mimics the physiological context of bacterial infection and subsequent inflammatory responses. Previous studies on macrophages elucidated the biological roles of caspase-1 in post-translational activation of interleukin-1xcexb2 and interleukin-18 in inflammation and apoptosis. However, the results from these studies focused only on a small number of factors. To better understand the host response, we have performed a high-throughput study of Kdo2-lipid A (KLA)-primed macrophages stimulated with ATP.'),
(77875, 2013, 'bio', 'Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools', 'Artigaud, Sébastien|Gauthier, Olivier|Pichereau, Vianney', 'Laboratoire des Sciences de lEnvironnement Marin, LEMAR UMR 6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, 29280 Plouzané, France.|Laboratoire des Sciences de lEnvironnement Marin, LEMAR UMR 6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, 29280 Plouzané, France.|Laboratoire des Sciences de lEnvironnement Marin, LEMAR UMR 6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, 29280 Plouzané, France.', 'France|France|France', 0, 'Animals|Data Interpretation, Statistical|Electrophoresis, Gel, Two-Dimensional|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Proteins|Proteomics|Software', 'Evaluation Studies', '10.1093/bioinformatics/btt464', '23986565', 'Two-dimensional electrophoresis is a crucial method in proteomics that allows the characterization of proteins function and expression. This usually implies the identification of proteins that are differentially expressed between two contrasting conditions, for example, healthy versus diseased in human proteomics biomarker discovery and stressful conditions versus control in animal experimentation. The statistical procedures that lead to such identifications are critical steps in the 2-DE analysis workflow. They include a normalization step and a test and probability correction for multiple testing. Statistical issues caused by the high dimensionality of the data and large-scale multiple testing have been a more active topic in transcriptomics than proteomics, especially in microarray analysis. We thus propose to adapt innovative statistical tools developed for microarray analysis and incorporate them in the 2-DE analysis pipeline.'),
(77876, 2013, 'bio', 'Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)', 'Paull, Evan O|Carlin, Daniel E|Niepel, Mario|Sorger, Peter K|Haussler, David|Stuart, Joshua M', 'Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, Department of Systems Biology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115 and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States|United States|United States', 82, 'Breast Neoplasms|Cell Line, Tumor|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genomics|Humans|Neoplasms|Protein Interaction Mapping|Signal Transduction|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt471', '23986566', 'Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations.'),
(77877, 2013, 'bio', 'The code structure of the p53 DNA-binding domain and the prognosis of breast cancer patients', 'Sato, Keiko|Hara, Toshihide|Ohya, Masanori', 'Department of Information Science, Tokyo University of Science, Noda, Chiba 278-8510, Japan.|Department of Information Science, Tokyo University of Science, Noda, Chiba 278-8510, Japan.|Department of Information Science, Tokyo University of Science, Noda, Chiba 278-8510, Japan.', 'Japan|Japan|Japan', 3, 'Breast Neoplasms|Female|Genes, p53|Humans|Kaplan-Meier Estimate|Mutation|Prognosis|Protein Structure, Tertiary|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btt497', '23986567', 'The tumor-suppressor gene TP53 mutations are diverse in the central region encoding the DNA-binding domain. It has not been clear whether the prognostic significance for survival in breast cancer patients is the same for all types of mutations. Are there specific types of mutations carrying a worse prognosis? To understand the correlation between the mutations in the gene encoding the DNA-binding domain and the prognosis of breast cancer, we studied the code structure of the DNA-binding domain of breast cancer patients by using various artificial codes in information transmission.'),
(77878, 2013, 'bio', 'lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests', 'Mariani, Valerio|Biasini, Marco|Barbato, Alessandro|Schwede, Torsten', 'Biozentrum, Universitxc3xa4t Basel, Klingelbergstrasse 50-70 and Computational Structural Biology, SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.|Biozentrum, Universitxc3xa4t Basel, Klingelbergstrasse 50-70 and Computational Structural Biology, SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.|Biozentrum, Universitxc3xa4t Basel, Klingelbergstrasse 50-70 and Computational Structural Biology, SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.|Biozentrum, Universitxc3xa4t Basel, Klingelbergstrasse 50-70 and Computational Structural Biology, SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 73, 'Computational Biology|Models, Molecular|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Software|Stereoisomerism', 'Journal Article', '10.1093/bioinformatics/btt473', '23986568', 'The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon xcexb1 atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model.'),
(77879, 2013, 'bio', 'A non-independent energy-based multiple sequence alignment improves prediction of transcription factor binding sites', 'Salama, Rafik A|Stekel, Dov J', 'Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK and School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, UK.|Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK and School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, UK.', 'United Kingdom|United Kingdom', 4, 'Algorithms|Binding Sites|Data Interpretation, Statistical|Sequence Alignment|Sequence Analysis, DNA|Software|Thermodynamics|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt463', '23990411', 'Multiple sequence alignments (MSAs) are usually scored under the assumption that the sequences being aligned have evolved by common descent. Consequently, the differences between sequences reflect the impact of insertions, deletions and mutations. However, non-coding DNA binding sequences, such as transcription factor binding sites (TFBSs), are frequently not related by common descent, and so the existing alignment scoring methods are not well suited for aligning such sequences.'),
(77880, 2013, 'bio', 'Bayesian consensus clustering', 'Lock, Eric F|Dunson, David B', 'Department of Statistical Science, Duke University, Durham, NC 27708, USA and Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|Department of Statistical Science, Duke University, Durham, NC 27708, USA and Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.', 'United States|United States', 85, 'Algorithms|Bayes Theorem|Cluster Analysis|Gene Dosage|Genomics|Humans|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btt425', '23990412', 'In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multisource clustering either independently determine a separate clustering for each data source or determine a single joint clustering for all data sources. There is a need for more flexible approaches that simultaneously model the dependence and the heterogeneity of the data sources.'),
(77881, 2013, 'bio', 'DIST: direct imputation of summary statistics for unmeasured SNPs', 'Lee, Donghyung|Bigdeli, T Bernard|Riley, Brien P|Fanous, Ayman H|Bacanu, Silviu-Alin', 'Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.', 'United States|United States|United States|United States|United States', 33, 'Data Interpretation, Statistical|Genome, Human|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt500', '23990413', 'Genotype imputation methods are used to enhance the resolution of genome-wide association studies, and thus increase the detection rate for genetic signals. Although most studies report all univariate summary statistics, many of them limit the access to subject-level genotypes. Because such an access is required by all genotype imputation methods, it is helpful to develop methods that impute summary statistics without going through the interim step of imputing genotypes. Even when subject-level genotypes are available, due to the substantial computational cost of the typical genotype imputation, there is a need for faster imputation methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77882, 2013, 'bio', 'Identification of active transcription factor and miRNA regulatory pathways in Alzheimers disease', 'Jiang, Wei|Zhang, Yan|Meng, Fanlin|Lian, Baofeng|Chen, Xiaowen|Yu, Xuexin|Dai, Enyu|Wang, Shuyuan|Liu, Xinyi|Li, Xiang|Wang, Lihong|Li, Xia', 'College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Department of Internal Neurology, Fourth Affiliated Hospital and Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin 150081, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 47, 'Algorithms|Alzheimer Disease|Gene Expression Profiling|Gene Expression Regulation|Humans|MicroRNAs|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt423', '23990414', 'Alzheimers disease (AD) is a severe neurodegenerative disease of the central nervous system that may be caused by perturbation of regulatory pathways rather than the dysfunction of a single gene. However, the pathology of AD has yet to be fully elucidated.'),
(77883, 2013, 'bio', 'A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure', 'Sofer, Tamar|Schifano, Elizabeth D|Hoppin, Jane A|Hou, Lifang|Baccarelli, Andrea A', 'Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, SPH2, 4th floor, Boston, MA 02115, USA, Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA, NIEHS, Epidemiology Branch, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Drive, Suite 1400 Chicago, IL 60611, USA, Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, 401 Park Drive, Landmark Ctr Room 415E, Boston, MA 02215, USA.', 'United States|United States|United States|United States|United States', 47, 'Algorithms|Cluster Analysis|CpG Islands|DNA Methylation|Environmental Exposure|Humans', 'Journal Article', '10.1093/bioinformatics/btt498', '23990415', 'DNA methylation is a heritable modifiable chemical process that affects gene transcription and is associated with other molecular markers (e.g. gene expression) and biomarkers (e.g. cancer or other diseases). Current technology measures methylation in hundred of thousands, or millions of CpG sites throughout the genome. It is evident that neighboring CpG sites are often highly correlated with each other, and current literature suggests that clusters of adjacent CpG sites are co-regulated.'),
(77884, 2013, 'bio', 'The MaSuRCA genome assembler', 'Zimin, Aleksey V|Marçais, Guillaume|Puiu, Daniela|Roberts, Michael|Salzberg, Steven L|Yorke, James A', 'Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.|Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.|Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.|Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.|Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.|Institute for Physical Sciences and Technology, University of Maryland, College Park, MD 20742, USA, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Department of Physics, University of Maryland, College Park, MD 20742, USA.', 'United States|United States|United States|United States|United States|United States', 416, 'Algorithms|Animals|Genome, Bacterial|Genomics|Mice|Rhodobacter sphaeroides|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btt476', '23990416', 'Second-generation sequencing technologies produce high coverage of the genome by short reads at a low cost, which has prompted development of new assembly methods. In particular, multiple algorithms based on de Bruijn graphs have been shown to be effective for the assembly problem. In this article, we describe a new hybrid approach that has the computational efficiency of de Bruijn graph methods and the flexibility of overlap-based assembly strategies, and which allows variable read lengths while tolerating a significant level of sequencing error. Our method transforms large numbers of paired-end reads into a much smaller number of longer super-reads. The use of super-reads allows us to assemble combinations of Illumina reads of differing lengths together with longer reads from 454 and Sanger sequencing technologies, making it one of the few assemblers capable of handling such mixtures. We call our system the Maryland Super-Read Celera Assembler (abbreviated MaSuRCA and pronounced mazurka).'),
(77885, 2013, 'bio', 'A general species delimitation method with applications to phylogenetic placements', 'Zhang, Jiajie|Kapli, Paschalia|Pavlidis, Pavlos|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-68159 Heidelberg, Germany, Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck, Germany, Natural History Museum of Crete, University of Crete, GR-71409 Irakleio, Crete, Greece and Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas-FORTH, GR-70013 Heraklion, Crete, Greece.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-68159 Heidelberg, Germany, Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck, Germany, Natural History Museum of Crete, University of Crete, GR-71409 Irakleio, Crete, Greece and Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas-FORTH, GR-70013 Heraklion, Crete, Greece.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-68159 Heidelberg, Germany, Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck, Germany, Natural History Museum of Crete, University of Crete, GR-71409 Irakleio, Crete, Greece and Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas-FORTH, GR-70013 Heraklion, Crete, Greece.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-68159 Heidelberg, Germany, Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck, Germany, Natural History Museum of Crete, University of Crete, GR-71409 Irakleio, Crete, Greece and Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas-FORTH, GR-70013 Heraklion, Crete, Greece.', 'Germany|Germany|Germany|Germany', 937, 'Algorithms|Animals|Phylogeny|Poisson Distribution|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt499', '23990417', 'Sequence-based methods to delimit species are central to DNA taxonomy, microbial community surveys and DNA metabarcoding studies. Current approaches either rely on simple sequence similarity thresholds (OTU-picking) or on complex and compute-intensive evolutionary models. The OTU-picking methods scale well on large datasets, but the results are highly sensitive to the similarity threshold. Coalescent-based species delimitation approaches often rely on Bayesian statistics and Markov Chain Monte Carlo sampling, and can therefore only be applied to small datasets.'),
(77886, 2013, 'bio', 'Multiple alignment-free sequence comparison', 'Ren, Jie|Song, Kai|Sun, Fengzhu|Deng, Minghua|Reinert, Gesine', 'School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.|School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.|School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.|School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.|School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.', 'China|China|China|China|China', 9, 'Animals|Binding Sites|Data Interpretation, Statistical|Mice|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt462', '23990418', 'Recently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences.'),
(77887, 2013, 'bio', 'TALENoffer: genome-wide TALEN off-target prediction', 'Grau, Jan|Boch, Jens|Posch, Stefan', 'Institute of Computer Science and Department of Genetics, Institute of Biology, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany.|Institute of Computer Science and Department of Genetics, Institute of Biology, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany.|Institute of Computer Science and Department of Genetics, Institute of Biology, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany.', 'Germany|Germany|Germany', 60, 'Animals|DNA-Binding Proteins|Endodeoxyribonucleases|Genome|Models, Statistical|Mutagenesis|Protein Engineering|Software', 'Journal Article', '10.1093/bioinformatics/btt501', '23995255', 'Transcription activator-like effector nucleases (TALENs) have become an accepted tool for targeted mutagenesis, but undesired off-targets remain an important issue. We present TALENoffer, a novel tool for the genome-wide prediction of TALEN off-targets. We show that TALENoffer successfully predicts known off-targets of engineered TALENs and yields a competitive runtime, scanning complete mammalian genomes within a few minutes.'),
(77888, 2013, 'bio', 'Adjusting scoring matrices to correct overextended alignments', 'Mills, Lauren J|Pearson, William R', 'Department of Molecular, Cell and Developmental Biology and Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.|Department of Molecular, Cell and Developmental Biology and Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States', 8, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Molecular Sequence Data|Position-Specific Scoring Matrices|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btt517', '23995390', 'Sequence similarity searches performed with BLAST, SSEARCH and FASTA achieve high sensitivity by using scoring matrices (e.g. BLOSUM62) that target low identity (<33%) alignments. Although such scoring matrices can effectively identify distant homologs, they can also produce local alignments that extend beyond the homologous regions.'),
(77889, 2013, 'bio', 'Consed: a graphical editor for next-generation sequencing', 'Gordon, David|Green, Phil', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.', 'United States|United States', 122, 'High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt515', '23995391', 'The rapid growth of DNA sequencing throughput in recent years implies that graphical interfaces for viewing and correcting errors must now handle large numbers of reads, efficiently pinpoint regions of interest and automate as many tasks as possible. We have adapted consed to reflect this. To allow full-feature editing of large datasets while keeping memory requirements low, we developed a viewer, bamScape, that reads billion-read BAM files, identifies and displays problem areas for user review and launches the consed graphical editor on user-selected regions, allowing, in addition to longstanding consed capabilities such as assembly editing, a variety of new features including direct editing of the reference sequence, variant and error detection, display of annotation tracks and the ability to simultaneously process a group of reads. Many batch processing capabilities have been added.'),
(77890, 2013, 'bio', 'PyroHMMvar: a sensitive and accurate method to call short indels and SNPs for Ion Torrent and 454 data', 'Zeng, Feng|Jiang, Rui|Chen, Ting', 'Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Computational Biology and Bioinformatics Program, University of Southern California, Los Angeles, CA 90089, USA.|Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Computational Biology and Bioinformatics Program, University of Southern California, Los Angeles, CA 90089, USA.|Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Computational Biology and Bioinformatics Program, University of Southern California, Los Angeles, CA 90089, USA.', 'China|China|China', 7, 'Diploidy|Genome, Human|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt512', '23995392', 'The identification of short insertions and deletions (indels) and single nucleotide polymorphisms (SNPs) from Ion Torrent and 454 reads is a challenging problem, essentially because these techniques are prone to sequence erroneously at homopolymers and can, therefore, raise indels in reads. Most of the existing mapping programs do not model homopolymer errors when aligning reads against the reference. The resulting alignments will then contain various kinds of mismatches and indels that confound the accurate determination of variant loci and alleles.'),
(77891, 2013, 'bio', 'SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization', 'Katayama, Shintaro|Töhönen, Virpi|Linnarsson, Sten|Kere, Juha', 'Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden, Science for Life Laboratory, Karolinska Institutet Science Park, 171 21 Solna, Sweden and Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden, Science for Life Laboratory, Karolinska Institutet Science Park, 171 21 Solna, Sweden and Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden, Science for Life Laboratory, Karolinska Institutet Science Park, 171 21 Solna, Sweden and Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden, Science for Life Laboratory, Karolinska Institutet Science Park, 171 21 Solna, Sweden and Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 65, 'Data Interpretation, Statistical|Gene Expression Profiling|Humans|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btt511', '23995393', 'Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition, therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods.'),
(77892, 2013, 'bio', 'Quantifying the complexity of medical research', 'Rodriguez-Esteban, Raul|Loging, William T', 'Computational Biology, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA.|Computational Biology, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA.', 'United States|United States', 9, 'Biomedical Research|Data Mining|Disease|Humans|Models, Biological|Publications', 'Journal Article', '10.1093/bioinformatics/btt505', '23995394', 'A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature.'),
(77893, 2013, 'bio', 'Metriculator: quality assessment for mass spectrometry-based proteomics', 'Taylor, Ryan M|Dance, Jamison|Taylor, Russ J|Prince, John T', 'Department of Chemistry and Biochemistry, Department of Computer Science and Department of Information Systems, Brigham Young University, Provo, UT 84602, USA.|Department of Chemistry and Biochemistry, Department of Computer Science and Department of Information Systems, Brigham Young University, Provo, UT 84602, USA.|Department of Chemistry and Biochemistry, Department of Computer Science and Department of Information Systems, Brigham Young University, Provo, UT 84602, USA.|Department of Chemistry and Biochemistry, Department of Computer Science and Department of Information Systems, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States', 0, 'Humans|Internet|Mass Spectrometry|Proteomics|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btt510', '24002108', 'Quality control in mass spectrometry-based proteomics remains subjective, labor-intensive and inconsistent between laboratories. We introduce Metriculator, a software designed to facilitate long-term storage of extensive performance metrics as introduced by NIST in 2010. Metriculator features a web interface that generates interactive comparison plots for contextual understanding of metric values and an automated metric generation toolkit. The comparison plots are designed for at-a-glance determination of outliers and trends in the datasets, together with relevant statistical comparisons. Easy-to-use quantitative comparisons and a framework for integration plugins will encourage a culture of quality assurance within the proteomics community.'),
(77894, 2013, 'bio', 'Novel human lncRNA-disease association inference based on lncRNA expression profiles', 'Chen, Xing|Yan, Gui-Ying', 'National Center for Mathematics and Interdisciplinary Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P.R. China.|National Center for Mathematics and Interdisciplinary Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P.R. China.', 'China|China', 269, 'Gene Expression Profiling|Gene Expression Regulation|Humans|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btt426', '24002109', 'More and more evidences have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention.'),
(77895, 2013, 'bio', 'Exploiting disjointness axioms to improve semantic similarity measures', 'Ferreira, João D|Hastings, Janna|Couto, Francisco M', 'Department of Informatics, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisboa, Portugal, Cheminformatics and Metabolism, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Swiss Center for Affective Sciences, University of Geneva, 7, rue des Battoirs, 1205 Geneva, Switzerland and Evolutionary Bioinformatics Group, Swiss Institute of Bioinformatics, Biophore - CH-1015 Lausanne, Switzerland.|Department of Informatics, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisboa, Portugal, Cheminformatics and Metabolism, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Swiss Center for Affective Sciences, University of Geneva, 7, rue des Battoirs, 1205 Geneva, Switzerland and Evolutionary Bioinformatics Group, Swiss Institute of Bioinformatics, Biophore - CH-1015 Lausanne, Switzerland.|Department of Informatics, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisboa, Portugal, Cheminformatics and Metabolism, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Swiss Center for Affective Sciences, University of Geneva, 7, rue des Battoirs, 1205 Geneva, Switzerland and Evolutionary Bioinformatics Group, Swiss Institute of Bioinformatics, Biophore - CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 8, 'Data Interpretation, Statistical|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btt491', '24002110', 'Representing domain knowledge in biology has traditionally been accomplished by creating simple hierarchies of classes with textual annotations. Recently, expressive ontology languages, such as Web Ontology Language, have become more widely adopted, supporting axioms that express logical relationships other than class-subclass, e.g. disjointness. This is improving the coverage and validity of the knowledge contained in biological ontologies. However, current semantic tools still need to adapt to this more expressive information. In this article, we propose a method to integrate disjointness axioms, which are being incorporated in real-world ontologies, such as the Gene Ontology and the chemical entities of biological interest ontology, into semantic similarity, the measure that estimates the closeness in meaning between classes.'),
(77896, 2013, 'bio', 'A statistical variant calling approach from pedigree information and local haplotyping with phase informative reads', 'Kojima, Kaname|Nariai, Naoki|Mimori, Takahiro|Takahashi, Mamoru|Yamaguchi-Kabata, Yumi|Sato, Yukuto|Nagasaki, Masao', 'Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.|Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 9, 'Genetic Variation|Genomics|Genotyping Techniques|Haplotypes|Humans|Models, Statistical|Pedigree|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btt503', '24002111', 'Variant calling from genome-wide sequencing data is essential for the analysis of disease-causing mutations and elucidation of disease mechanisms. However, variant calling in low coverage regions is difficult due to sequence read errors and mapping errors. Hence, variant calling approaches that are robust to low coverage data are demanded.'),
(77897, 2013, 'bio', 'M2SG: mapping human disease-related genetic variants to protein sequences and genomic loci', 'Ji, Renkai|Cong, Qian|Li, Wenlin|Grishin, Nick V', 'Departments of biophysics and biochemistry, University of Texas Southwestern Medical Center and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Departments of biophysics and biochemistry, University of Texas Southwestern Medical Center and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Departments of biophysics and biochemistry, University of Texas Southwestern Medical Center and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Departments of biophysics and biochemistry, University of Texas Southwestern Medical Center and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.', 'United States|United States|United States|United States', 0, 'Amino Acid Substitution|Chromosome Mapping|Databases, Genetic|Databases, Protein|Disease|Genetic Loci|Genome, Human|Genomics|Humans|Mutation|Phenotype|Polymorphism, Single Nucleotide|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt507', '24002112', 'Online Mendelian Inheritance in Man (OMIM) is a manually curated compendium of human genetic variants and the corresponding phenotypes, mostly human diseases. Instead of directly documenting the native sequences for gene entries, OMIM links its entries to protein and DNA sequences in other databases. However, because of the existence of gene isoforms and errors in OMIM records, mapping a specific OMIM mutation to its corresponding protein sequence is not trivial. Combining computer programs and extensive manual curation of OMIM full-text descriptions and original literature, we mapped 98% of OMIM amino acid substitutions (AASs) and all SwissProt Variant (SwissVar) disease-related AASs to reference sequences and confidently mapped 99.96% of all AASs to the genomic loci. Based on the results, we developed an online database and interactive web server (M2SG) to (i) retrieve the mapped OMIM and SwissVar variants for a given protein sequence, and (ii) obtain related proteins and mutations for an input disease phenotype. This database will be useful for analyzing sequences, understanding the effect of mutations, identifying important genetic variations and designing experiments on a protein of interest.'),
(77898, 2013, 'bio', 'PAVIS: a tool for Peak Annotation and Visualization', 'Huang, Weichun|Loganantharaj, Rasiah|Schroeder, Bryce|Fargo, David|Li, Leping', 'Biostatistics Branch and the Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, NC 27709, USA.|Biostatistics Branch and the Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, NC 27709, USA.|Biostatistics Branch and the Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, NC 27709, USA.|Biostatistics Branch and the Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, NC 27709, USA.|Biostatistics Branch and the Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, NC 27709, USA.', 'United States|United States|United States|United States|United States', 78, 'Chromatin Assembly and Disassembly|Chromatin Immunoprecipitation|Gene Expression Regulation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt520', '24008416', 'We introduce a web-based tool, Peak Annotation and Visualization (PAVIS), for annotating and visualizing ChIP-seq peak data. PAVIS is designed with non-bioinformaticians in mind and presents a straightforward user interface to facilitate biological interpretation of ChIP-seq peak or other genomic enrichment data. PAVIS, through association with annotation, provides relevant genomic context for each peak, such as peak location relative to genomic features including transcription start site, intron, exon or 5/3-untranslated region. PAVIS reports the relative enrichment P-values of peaks in these functionally distinct categories, and provides a summary plot of the relative proportion of peaks in each category. PAVIS, unlike many other resources, provides a peak-oriented annotation and visualization system, allowing dynamic visualization of tens to hundreds of loci from one or more ChIP-seq experiments, simultaneously. PAVIS enables rapid, and easy examination and cross-comparison of the genomic context and potential functions of the underlying genomic elements, thus supporting downstream hypothesis generation.'),
(77899, 2013, 'bio', 'NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data', 'McClellan, Elizabeth A|Moerland, Perry D|van der Spek, Peter J|Stubbs, Andrew P', 'Department of Bioinformatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands, Department of Mathematics and Computer Science, Metropolitan State University of Denver, Colorado, USA, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands, Netherlands Proteomics Centre, H.R. Kruytgebouw, Padualaan 8, 3584CH Utrecht, The Netherlands and Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525GA Nijmegen, The Netherlands.|Department of Bioinformatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands, Department of Mathematics and Computer Science, Metropolitan State University of Denver, Colorado, USA, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands, Netherlands Proteomics Centre, H.R. Kruytgebouw, Padualaan 8, 3584CH Utrecht, The Netherlands and Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525GA Nijmegen, The Netherlands.|Department of Bioinformatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands, Department of Mathematics and Computer Science, Metropolitan State University of Denver, Colorado, USA, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands, Netherlands Proteomics Centre, H.R. Kruytgebouw, Padualaan 8, 3584CH Utrecht, The Netherlands and Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525GA Nijmegen, The Netherlands.|Department of Bioinformatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands, Department of Mathematics and Computer Science, Metropolitan State University of Denver, Colorado, USA, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands, Netherlands Proteomics Centre, H.R. Kruytgebouw, Padualaan 8, 3584CH Utrecht, The Netherlands and Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525GA Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Algorithms|Data Interpretation, Statistical|Humans|Mass Spectrometry|Peptides|Protein Interaction Mapping|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt513', '24008417', 'The discovery of functionally related groups in a set of significantly abundant proteins from a mass spectrometry experiment is an important step in a proteomics analysis pipeline. Here we describe NetWeAvers (Network Weighted Averages) for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers is an R package that provides a novel method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks.'),
(77900, 2013, 'bio', 'INSECT: IN-silico SEarch for Co-occurring Transcription factors', 'Rohr, Cristian O|Parra, R Gonzalo|Yankilevich, Patricio|Perez-Castro, Carolina', 'Instituto de Ecología, Genética y Evolución (IEGEBA)-CONICET, Protein Physiology Laboratory, Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, C1428EGA, Buenos Aires, Argentina and Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) -CONICET- Partner Institute of the Max Planck Society, C1425FQD, Buenos Aires, Argentina.|Instituto de Ecología, Genética y Evolución (IEGEBA)-CONICET, Protein Physiology Laboratory, Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, C1428EGA, Buenos Aires, Argentina and Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) -CONICET- Partner Institute of the Max Planck Society, C1425FQD, Buenos Aires, Argentina.|Instituto de Ecología, Genética y Evolución (IEGEBA)-CONICET, Protein Physiology Laboratory, Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, C1428EGA, Buenos Aires, Argentina and Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) -CONICET- Partner Institute of the Max Planck Society, C1425FQD, Buenos Aires, Argentina.|Instituto de Ecología, Genética y Evolución (IEGEBA)-CONICET, Protein Physiology Laboratory, Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, C1428EGA, Buenos Aires, Argentina and Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) -CONICET- Partner Institute of the Max Planck Society, C1425FQD, Buenos Aires, Argentina.', 'Argentina|Argentina|Argentina|Argentina', 0, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Computer Simulation|Internet|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt506', '24008418', 'Transcriptional regulation occurs through the concerted actions of multiple transcription factors (TFs) that bind cooperatively to cis-regulatory modules (CRMs) of genes. These CRMs usually contain a variable number of transcription factor-binding sites (TFBSs) involved in related cellular and physiological processes. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has been effective in detecting TFBSs and nucleosome location to identify potential CRMs in genome-wide studies. Although several attempts were previously reported to predict the potential binding of TFs at TFBSs within CRMs by comparing different ChIP-seq data, these have been hampered by excessive background, usually emerging as a consequence of experimental conditions. To understand these complex regulatory circuits, it would be helpful to have reliable and updated user-friendly tools to assist in the identification of TFBSs and CRMs for gene(s) of interest.'),
(77901, 2013, 'bio', 'Infernal 11: 100-fold faster RNA homology searches', 'Nawrocki, Eric P|Eddy, Sean R', 'HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA.|HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA.', 'United States|United States', 707, 'Algorithms|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btt509', '24008419', 'Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables xe2x88xbc100-fold acceleration over the previous version and xe2x88xbc10 000-fold acceleration over exhaustive non-filtered CM searches.'),
(77902, 2013, 'bio', 'OntoQuery: easy-to-use web-based OWL querying', 'Tudose, Ilinca|Hastings, Janna|Muthukrishnan, Venkatesh|Owen, Gareth|Turner, Steve|Dekker, Adriano|Kale, Namrata|Ennis, Marcus|Steinbeck, Christoph', 'Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.|Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 0, 'Biological Ontologies|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt514', '24008420', 'The Web Ontology Language (OWL) provides a sophisticated language for building complex domain ontologies and is widely used in bio-ontologies such as the Gene Ontology. The Protxc3xa9gxc3xa9-OWL ontology editing tool provides a query facility that allows composition and execution of queries with the human-readable Manchester OWL syntax, with syntax checking and entity label lookup. No equivalent query facility such as the Protxc3xa9gxc3xa9 Description Logics (DL) query yet exists in web form. However, many users interact with bio-ontologies such as chemical entities of biological interest and the Gene Ontology using their online Web sites, within which DL-based querying functionality is not available. To address this gap, we introduce the OntoQuery web-based query utility.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77903, 2013, 'bio', 'LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets', 'Crowell, Kevin L|Slysz, Gordon W|Baker, Erin S|LaMarche, Brian L|Monroe, Matthew E|Ibrahim, Yehia M|Payne, Samuel H|Anderson, Gordon A|Smith, Richard D', 'Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 21, 'Algorithms|Chromatography, Liquid|Cluster Analysis|Ions|Mass Spectrometry|Software|Spectrum Analysis', 'Journal Article', '10.1093/bioinformatics/btt465', '24008421', 'The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing.'),
(77904, 2013, 'bio', 'targetHub: a programmable interface for miRNA-gene interactions', 'Manyam, Ganiraju|Ivan, Cristina|Calin, George A|Coombes, Kevin R', 'Department of Bioinformatics and Computational Biology, Department of Gynecologic Oncology and Reproductive Medicine and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Gynecologic Oncology and Reproductive Medicine and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Gynecologic Oncology and Reproductive Medicine and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, Department of Gynecologic Oncology and Reproductive Medicine and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Software', 'Journal Article', '10.1093/bioinformatics/btt439', '24013925', 'With the expansion of high-throughput technologies, understanding different kinds of genome-level data is a common task. MicroRNA (miRNA) is increasingly profiled using high-throughput technologies (microarrays or next-generation sequencing). The downstream analysis of miRNA targets can be difficult. Although there are many databases and algorithms to predict miRNA targets, there are few tools to integrate miRNA-gene interaction data into high-throughput genomic analyses.'),
(77905, 2013, 'bio', 'The Xeno-glycomics database (XDB): a relational database of qualitative and quantitative pig glycome repertoire', 'Park, Hae-Min|Park, Ju-Hyeong|Kim, Yoon-Woo|Kim, Kyoung-Jin|Jeong, Hee-Jin|Jang, Kyoung-Soon|Kim, Byung-Gee|Kim, Yun-Gon', 'School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.|School of Chemical and Biological Engineering and Department of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea Department of Chemical Engineering, Soongsil University, Seoul 156-743, Korea Institute of Molecular Biology and Genetics and Institute of Bioengineering, Seoul National University, Seoul 151-742, Korea.', '-|-|-|-|-|-|-|-', 2, 'Animals|Databases, Chemical|Glycomics|Internet|Mass Spectrometry|Polysaccharides|Software|Swine', 'Journal Article', '10.1093/bioinformatics/btt504', '24013926', 'In recent years, the improvement of mass spectrometry-based glycomics techniques (i.e. highly sensitive, quantitative and high-throughput analytical tools) has enabled us to obtain a large dataset of glycans. Here we present a database named Xeno-glycomics database (XDB) that contains cell- or tissue-specific pig glycomes analyzed with mass spectrometry-based techniques, including a comprehensive pig glycan information on chemical structures, mass values, types and relative quantities. It was designed as a user-friendly web-based interface that allows users to query the database according to pig tissue/cell types or glycan masses. This database will contribute in providing qualitative and quantitative information on glycomes characterized from various pig cells/organs in xenotransplantation and might eventually provide new targets in the xcexb11,3-galactosyltransferase gene-knock out pigs era.'),
(77906, 2013, 'bio', 'Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards', 'Tsai, Tsung-Heng|Tadesse, Mahlet G|Di Poto, Cristina|Pannell, Lewis K|Mechref, Yehia|Wang, Yue|Ressom, Habtom W', 'Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Algorithms|Bayes Theorem|Chromatography, Liquid|Glycomics|Humans|Mass Spectrometry|Metabolomics|Models, Statistical|Proteomics|Reference Standards', 'Journal Article', '10.1093/bioinformatics/btt461', '24013927', 'Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various -omic studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data.'),
(77907, 2013, 'bio', 'eSBMTools 10: enhanced native structure-based modeling tools', 'Lutz, Benjamin|Sinner, Claude|Heuermann, Geertje|Verma, Abhinav|Schug, Alexander', 'Department of Physics and Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.|Department of Physics and Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.|Department of Physics and Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.|Department of Physics and Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.|Department of Physics and Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Molecular Dynamics Simulation|Protein Folding|Proteins|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt478', '24021379', 'Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints.'),
(77908, 2013, 'bio', 'Precise inference of copy number alterations in tumor samples from SNP arrays', 'Chen, Gary K|Chang, Xiao|Curtis, Christina|Wang, Kai', 'Department of Preventive Medicine, Zilkha Neurogenetic Institute and Department of Psychiatry, University of Southern California, Los Angeles, CA 90089, USA.|Department of Preventive Medicine, Zilkha Neurogenetic Institute and Department of Psychiatry, University of Southern California, Los Angeles, CA 90089, USA.|Department of Preventive Medicine, Zilkha Neurogenetic Institute and Department of Psychiatry, University of Southern California, Los Angeles, CA 90089, USA.|Department of Preventive Medicine, Zilkha Neurogenetic Institute and Department of Psychiatry, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States|United States', 7, 'Aneuploidy|Breast Neoplasms|Cell Line, Tumor|Chromosome Aberrations|Computational Biology|DNA Copy Number Variations|Female|Genome, Human|Genomics|Genotype|Humans|Likelihood Functions|Markov Chains|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software|Stromal Cells', 'Journal Article', '10.1093/bioinformatics/btt521', '24021380', 'The accurate detection of copy number alterations (CNAs) in human genomes is important for understanding susceptibility to cancer and mechanisms of tumor progression. CNA detection in tumors from single nucleotide polymorphism (SNP) genotyping arrays is a challenging problem due to phenomena such as aneuploidy, stromal contamination, genomic waves and intra-tumor heterogeneity, issues that leading methods do not optimally address.'),
(77909, 2013, 'bio', 'HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data', 'Ashoor, Haitham|Hérault, Aurélie|Kamoun, Aurélie|Radvanyi, François|Bajic, Vladimir B|Barillot, Emmanuel|Boeva, Valentina', 'Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.', 'France|France|France|France|France|France|France', 0, 'Base Composition|Chromatin Assembly and Disassembly|Chromatin Immunoprecipitation|Computer Simulation|DNA Copy Number Variations|Epigenesis, Genetic|Genome, Human|Histones|Humans|Markov Chains|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Protein Processing, Post-Translational|Software|Urinary Bladder Neoplasms', 'Journal Article', '10.1093/bioinformatics/btt524', '24021381', 'Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain.'),
(77910, 2013, 'bio', 'A simulator for spatially extended kappa models', 'Sorokina, Oksana|Sorokin, Anatoly|Douglas Armstrong, J|Danos, Vincent', 'School of Informatics, University of Edinburgh, Edinburgh, UK and Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino 142290, Russia.|School of Informatics, University of Edinburgh, Edinburgh, UK and Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino 142290, Russia.|School of Informatics, University of Edinburgh, Edinburgh, UK and Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino 142290, Russia.|School of Informatics, University of Edinburgh, Edinburgh, UK and Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino 142290, Russia.', '-|-|-|-', 15, 'Computer Simulation|Diffusion|Humans|Models, Theoretical|Programming Languages|Receptors, AMPA|Synaptic Membranes', 'Journal Article', '10.1093/bioinformatics/btt523', '24021382', 'Spatial Kappa is a simulator of models written in a variant of the rule-based stochastic modelling language Kappa, with spatial extensions.'),
(77911, 2013, 'bio', 'Accounting for epistatic interactions improves the functional analysis of protein structures', 'Wilkins, Angela D|Venner, Eric|Marciano, David C|Erdin, Serkan|Atri, Benu|Lua, Rhonald C|Lichtarge, Olivier', 'Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.', 'United States|United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Bacterial Proteins|Epistasis, Genetic|Evolution, Molecular|Molecular Sequence Annotation|Mutation|Protein Conformation|Proteins|Proteome|Sequence Analysis, Protein|Serine Endopeptidases', 'Journal Article', '10.1093/bioinformatics/btt489', '24021383', 'The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure.'),
(77912, 2013, 'bio', 'BioPig: a Hadoop-based analytic toolkit for large-scale sequence data', 'Nordberg, Henrik|Bhatia, Karan|Wang, Kai|Wang, Zhong', 'Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA and Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA and Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA and Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA and Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btt528', '24021384', 'The recent revolution in sequencing technologies has led to an exponential growth of sequence data. As a result, most of the current bioinformatics tools become obsolete as they fail to scale with data. To tackle this data deluge, here we introduce the BioPig sequence analysis toolkit as one of the solutions that scale to data and computation.'),
(77913, 2013, 'bio', 'Toward a statistically explicit understanding of de novo sequence assembly', 'Howison, Mark|Zapata, Felipe|Dunn, Casey W', 'Center for Computation and Visualization and Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA.|Center for Computation and Visualization and Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA.|Center for Computation and Visualization and Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA.', 'United States|United States|United States', 17, 'Computational Biology|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt525', '24021385', 'Draft de novo genome assemblies are now available for many organisms. These assemblies are point estimates of the true genome sequences. Each is a specific hypothesis, drawn from among many alternative hypotheses, of the sequence of a genome. Assembly uncertainty, the inability to distinguish between multiple alternative assembly hypotheses, can be due to real variation between copies of the genome in the sample, errors and ambiguities in the sequenced data and assumptions and heuristics of the assemblers. Most assemblers select a single assembly according to ad hoc criteria, and do not yet report and quantify the uncertainty of their outputs. Those assemblers that do report uncertainty take different approaches to describing multiple assembly hypotheses and the support for each.'),
(77914, 2013, 'bio', 'Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data', 'Robertson, Charles E|Harris, J Kirk|Wagner, Brandie D|Granger, David|Browne, Kathy|Tatem, Beth|Feazel, Leah M|Park, Kristin|Pace, Norman R|Frank, Daniel N', 'Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA, University of Colorado Microbiome Research Consortium, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA, Incubix Incorporated, Boulder, CO 80301, USA and Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 136, 'Computational Biology|Computer Graphics|Database Management Systems|High-Throughput Nucleotide Sequencing|Humans|Metagenome|Microbiota|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt526', '24021386', 'Studies of the human microbiome, and microbial community ecology in general, have blossomed of late and are now a burgeoning source of exciting research findings. Along with the advent of next-generation sequencing platforms, which have dramatically increased the scope of microbiome-related projects, several high-performance sequence analysis pipelines (e.g. QIIME, MOTHUR, VAMPS) are now available to investigators for microbiome analysis. The subject of our manuscript, the graphical user interface-based Explicet software package, fills a previously unmet need for a robust, yet intuitive means of integrating the outputs of the software pipelines with user-specified metadata and then visualizing the combined data.'),
(77915, 2013, 'bio', 'CruzDB: software for annotation of genomic intervals with UCSC genome-browser database', 'Pedersen, Brent S|Yang, Ivana V|De, Subhajyoti', 'Department of Medicine, University of Colorado Denver, School of Medicine, Denver, CO, USA and Molecular Oncology Program, University of Colorado Cancer Center, Aurora, CO, USA.|Department of Medicine, University of Colorado Denver, School of Medicine, Denver, CO, USA and Molecular Oncology Program, University of Colorado Cancer Center, Aurora, CO, USA.|Department of Medicine, University of Colorado Denver, School of Medicine, Denver, CO, USA and Molecular Oncology Program, University of Colorado Cancer Center, Aurora, CO, USA.', 'United States|United States|United States', 15, 'DNA Replication|Databases, Genetic|Exons|Genome, Human|Genomics|Humans|Introns|MicroRNAs|RNA, Long Noncoding|Software|Untranslated Regions', 'Journal Article', '10.1093/bioinformatics/btt534', '24037212', 'The biological significance of genomic features is often context dependent. Annotating a particular dataset with existing external data can provide insight into function.'),
(77916, 2013, 'bio', 'Protein evolution along phylogenetic histories under structurally constrained substitution models', 'Arenas, Miguel|Dos Santos, Helena G|Posada, David|Bastolla, Ugo', 'Centre for Molecular Biology Severo Ochoa, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain and Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Centre for Molecular Biology Severo Ochoa, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain and Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Centre for Molecular Biology Severo Ochoa, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain and Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Centre for Molecular Biology Severo Ochoa, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain and Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Amino Acid Substitution|Computer Simulation|Evolution, Molecular|Models, Statistical|Phylogeny|Proteins', 'Journal Article', '10.1093/bioinformatics/btt530', '24037213', 'Models of molecular evolution aim at describing the evolutionary processes at the molecular level. However, current models rarely incorporate information from protein structure. Conversely, structure-based models of protein evolution have not been commonly applied to simulate sequence evolution in a phylogenetic framework, and they often ignore relevant evolutionary processes such as recombination. A simulation evolutionary framework that integrates substitution models that account for protein structure stability should be able to generate more realistic in silico evolved proteins for a variety of purposes.'),
(77917, 2013, 'bio', 'Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies', 'Lee, Young-suk|Krishnan, Arjun|Zhu, Qian|Troyanskaya, Olga G', 'Department of Computer Science, Princeton University, Princeton, NJ 08544, USA and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08544, USA and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08544, USA and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08544, USA and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.', 'United States|United States|United States|United States', 16, 'Bayes Theorem|Cells|Computational Biology|Databases, Factual|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Organ Specificity', 'Journal Article', '10.1093/bioinformatics/btt529', '24037214', 'Leveraging gene expression data through large-scale integrative analyses for multicellular organisms is challenging because most samples are not fully annotated to their tissue/cell-type of origin. A computational method to classify samples using their entire gene expression profiles is needed. Such a method must be applicable across thousands of independent studies, hundreds of gene expression technologies and hundreds of diverse human tissues and cell-types.'),
(77918, 2013, 'bio', 'Discovering combinatorial interactions in survival data', 'Duverle, David A|Takeuchi, Ichiro|Murakami-Tonami, Yuko|Kadomatsu, Kenji|Tsuda, Koji', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.', 'Japan|Japan|Japan|Japan|Japan', 4, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Likelihood Functions|Logistic Models|Models, Biological|Neoplasm Proteins|Neuroblastoma|Proportional Hazards Models|Risk Factors|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btt532', '24037215', 'Although several methods exist to relate high-dimensional gene expression data to various clinical phenotypes, finding combinations of features in such input remains a challenge, particularly when fitting complex statistical models such as those used for survival studies.'),
(77919, 2013, 'bio', 'Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes', 'Gunawardana, Yawwani|Niranjan, Mahesan', 'School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.|School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.', 'United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Codon|Computational Biology|Gene Expression Regulation, Fungal|Protein Processing, Post-Translational|Proteome|RNA, Messenger|RNA, Transfer|Ribosomes|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt537', '24045772', 'Despite much dynamical cellular behaviour being achieved by accurate regulation of protein concentrations, messenger RNA abundances, measured by microarray technology, and more recently by deep sequencing techniques, are widely used as proxies for protein measurements. Although for some species and under some conditions, there is good correlation between transcriptome and proteome level measurements, such correlation is by no means universal due to post-transcriptional and post-translational regulation, both of which are highly prevalent in cells. Here, we seek to develop a data-driven machine learning approach to bridging the gap between these two levels of high-throughput omic measurements on Saccharomyces cerevisiae and deploy the model in a novel way to uncover mRNA-protein pairs that are candidates for post-translational regulation.'),
(77920, 2013, 'bio', 'Integrated profiling of three dimensional cell culture models and 3D microscopy', 'Bilgin, Cemal Cagatay|Kim, Sun|Leung, Elle|Chang, Hang|Parvin, Bahram', 'Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States|United States', 0, 'Breast|Breast Neoplasms|Cell Culture Techniques|Cell Nucleus|Cells, Cultured|Female|Humans|Imaging, Three-Dimensional|Microscopy, Confocal', 'Journal Article', '10.1093/bioinformatics/btt535', '24045773', 'Our goal is to develop a screening platform for quantitative profiling of colony organizations in 3D cell culture models. The 3D cell culture models, which are also imaged in 3D, are functional assays that mimic the in vivo characteristics of the tissue architecture more faithfully than the 2D cultures. However, they also introduce significant computational challenges, with the main barriers being the effects of growth conditions, fixations and inherent complexities in segmentation that need to be resolved in the 3D volume.'),
(77921, 2013, 'bio', 'RNA-Pareto: interactive analysis of Pareto-optimal RNA sequence-structure alignments', 'Schnattinger, Thomas|Schöning, Uwe|Marchfelder, Anita|Kestler, Hans A', 'Institute of Theoretical Computer Science, Medical Systems Biology and Biology II, Ulm University, D-89069 Ulm, Germany.|Institute of Theoretical Computer Science, Medical Systems Biology and Biology II, Ulm University, D-89069 Ulm, Germany.|Institute of Theoretical Computer Science, Medical Systems Biology and Biology II, Ulm University, D-89069 Ulm, Germany.|Institute of Theoretical Computer Science, Medical Systems Biology and Biology II, Ulm University, D-89069 Ulm, Germany.', 'Germany|Germany|Germany|Germany', 6, 'Algorithms|Base Sequence|Computational Biology|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btt536', '24045774', 'Incorporating secondary structure information into the alignment process improves the quality of RNA sequence alignments. Instead of using fixed weighting parameters, sequence and structure components can be treated as different objectives and optimized simultaneously. The result is not a single, but a Pareto-set of equally optimal solutions, which all represent different possible weighting parameters. We now provide the interactive graphical software tool RNA-Pareto, which allows a direct inspection of all feasible results to the pairwise RNA sequence-structure alignment problem and greatly facilitates the exploration of the optimal solution set.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77922, 2014, 'bio', 'Pattern search in BioPAX models', 'Babur, Özgün|Aksoy, Bülent Arman|Rodchenkov, Igor|Sümer, Selçuk Onur|Sander, Chris|Demir, Emek', 'Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 10, 'Cell Physiological Phenomena|Databases, Factual|Metabolic Networks and Pathways|Models, Biological|Phosphorylation|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btt539', '24045775', 'BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult.'),
(77923, 2013, 'bio', 'Use of autocorrelation scanning in DNA copy number analysis', 'Zhang, Liangcai|Zhang, Li', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA and Department of Biophysics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA and Department of Biophysics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.', 'China|China', 4, 'DNA Copy Number Variations|Data Interpretation, Statistical|Genome|Genomics', 'Journal Article', '10.1093/bioinformatics/btt479', '24045776', 'Data quality is a critical issue in the analyses of DNA copy number alterations obtained from microarrays. It is commonly assumed that copy number alteration data can be modeled as piecewise constant and the measurement errors of different probes are independent. However, these assumptions do not always hold in practice. In some published datasets, we find that measurement errors are highly correlated between probes that interrogate nearby genomic loci, and the piecewise-constant model does not fit the data well. The correlated errors cause problems in downstream analysis, leading to a large number of DNA segments falsely identified as having copy number gains and losses.'),
(77924, 2013, 'bio', 'Optimizing a global alignment of protein interaction networks', 'Chindelevitch, Leonid|Ma, Cheng-Yu|Liao, Chung-Shou|Berger, Bonnie', 'Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Computer Science and Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan.|Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Computer Science and Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan.|Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Computer Science and Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan.|Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Computer Science and Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan.', '-|-|-|-', 32, 'Algorithms|Animals|Biological Evolution|Humans|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt486', '24048352', 'The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species evolution.'),
(77925, 2013, 'bio', 'Assessing the validity and reproducibility of genome-scale predictions', 'Sugden, Lauren A|Tackett, Michael R|Savva, Yiannis A|Thompson, William A|Lawrence, Charles E', 'Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.', 'United States|United States|United States|United States|United States', 5, 'Adenosine Deaminase|Animals|Drosophila|Genome|Genomics|Models, Statistical|RNA Editing|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btt508', '24048353', 'Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.'),
(77926, 2013, 'bio', 'Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform', 'Nyström-Persson, Johan|Igarashi, Yoshinobu|Ito, Maori|Morita, Mizuki|Nakatsu, Noriyuki|Yamada, Hiroshi|Mizuguchi, Kenji', 'National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.|National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan and Centre for Knowledge Structuring, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 21, 'Animals|Biomarkers|Databases, Factual|Dose-Response Relationship, Drug|Gene Expression Regulation|Glutathione|Hepatocytes|Humans|Kidney|Liver|Oligonucleotide Array Sequence Analysis|Rats|Software|Toxicogenetics|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt531', '24048354', 'In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data.'),
(77927, 2013, 'bio', 'Shaping the interaction landscape of bioactive molecules', 'Gfeller, David|Michielin, Olivier|Zoete, Vincent', 'Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, CH-1015 Lausanne, Switzerland, Ludwig Institute for Cancer Research and Pluridisciplinary Center for Clinical Oncology, Centre Hospitalier Universitaire Vaudois, CH-1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, CH-1015 Lausanne, Switzerland, Ludwig Institute for Cancer Research and Pluridisciplinary Center for Clinical Oncology, Centre Hospitalier Universitaire Vaudois, CH-1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, CH-1015 Lausanne, Switzerland, Ludwig Institute for Cancer Research and Pluridisciplinary Center for Clinical Oncology, Centre Hospitalier Universitaire Vaudois, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 96, 'Binding Sites|Computational Biology|Computer Simulation|Databases, Factual|Drug Design|Humans|Ligands|Protein Binding|Proteins|Quantitative Structure-Activity Relationship|Small Molecule Libraries', 'Journal Article', '10.1093/bioinformatics/btt540', '24048355', 'Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules.'),
(77928, 2013, 'bio', 'Assessing gene-level translational control from ribosome profiling', 'Olshen, Adam B|Hsieh, Andrew C|Stumpf, Craig R|Olshen, Richard A|Ruggero, Davide|Taylor, Barry S', 'Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 53, 'Algorithms|Animals|Codon|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Protein Biosynthesis|RNA, Messenger|Ribosomes|Software|TOR Serine-Threonine Kinases', 'Journal Article', '10.1093/bioinformatics/btt533', '24048356', 'The translational landscape of diverse cellular systems remains largely uncharacterized. A detailed understanding of the control of gene expression at the level of messenger RNA translation is vital to elucidating a systems-level view of complex molecular programs in the cell. Establishing the degree to which such post-transcriptional regulation can mediate specific phenotypes is similarly critical to elucidating the molecular pathogenesis of diseases such as cancer. Recently, methods for massively parallel sequencing of ribosome-bound fragments of messenger RNA have begun to uncover genome-wide translational control at codon resolution. Despite its promise for deeply characterizing mammalian proteomes, few analytical methods exist for the comprehensive analysis of this paired RNA and ribosome data.'),
(77929, 2013, 'bio', 'miREval 20: a web tool for simple microRNA prediction in genome sequences', 'Gao, Dadi|Middleton, Robert|Rasko, John E J|Ritchie, William', 'Bioinformatics Laboratory, Centenary Institute, Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, New South Wales, Australia and Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia.|Bioinformatics Laboratory, Centenary Institute, Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, New South Wales, Australia and Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia.|Bioinformatics Laboratory, Centenary Institute, Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, New South Wales, Australia and Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia.|Bioinformatics Laboratory, Centenary Institute, Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, New South Wales, Australia and Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia.', 'Australia|Australia|Australia|Australia', 19, 'Genome|Internet|MicroRNAs|Sequence Analysis, RNA|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt545', '24048357', 'We have developed miREval 2.0, an online tool that can simultaneously search up to 100 sequences for novel microRNAs (miRNAs) in multiple organisms. miREval 2.0 uses multiple published in silico approaches to detect miRNAs in sequences of interest. This tool can be used to discover miRNAs from DNA sequences or to validate candidates from sequencing data.'),
(77930, 2013, 'bio', 'CLIPS-4D: a classifier that distinguishes structurally and functionally important residue-positions based on sequence and 3D data', 'Janda, Jan-Oliver|Meier, Andreas|Merkl, Rainer', 'Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040 Regensburg, Germany and Faculty of Mathematics and Computer Science, University of Hagen, D-58084 Hagen, Germany.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040 Regensburg, Germany and Faculty of Mathematics and Computer Science, University of Hagen, D-58084 Hagen, Germany.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040 Regensburg, Germany and Faculty of Mathematics and Computer Science, University of Hagen, D-58084 Hagen, Germany.', 'Germany|Germany|Germany', 7, 'Algorithms|Binding Sites|Catalysis|Computational Biology|Protein Binding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt519', '24048358', 'The precise identification of functionally and structurally important residues of a protein is still an open problem, and state-of-the-art classifiers predict only one or at most two different categories.'),
(77931, 2013, 'bio', 'customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search', 'Wang, Xiaojing|Zhang, Bing', 'Department of Biomedical Informatics, Vanderbilt-Ingram Cancer Center and Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Department of Biomedical Informatics, Vanderbilt-Ingram Cancer Center and Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.', 'United States|United States', 81, 'Biomarkers, Tumor|Colonic Neoplasms|Databases, Protein|Genomics|High-Throughput Nucleotide Sequencing|Humans|Peptide Fragments|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt543', '24058055', 'Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration.'),
(77932, 2013, 'bio', 'Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data', 'Deckard, Anastasia|Anafi, Ron C|Hogenesch, John B|Haase, Steven B|Harer, John', 'Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States', 45, 'Algorithms|Animals|Arabidopsis|Cell Cycle|Circadian Clocks|Circadian Rhythm|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Metabolic Networks and Pathways|Mice|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Plant Roots|Saccharomyces cerevisiae', 'Comparative Study', '10.1093/bioinformatics/btt541', '24058056', 'To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana.'),
(77933, 2013, 'bio', 'Single assay-wide variance experimental (SAVE) design for high-throughput screening', 'Murie, Carl|Barette, Caroline|Lafanechère, Laurence|Nadon, Robert', 'McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1, Canada, McGill Department of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada, Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble Cedex 09, France, INSERM, U823, Université Joseph Fourier-Grenoble 1 and Institut Albert Bonniot, Grenoble F-38706, France.|McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1, Canada, McGill Department of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada, Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble Cedex 09, France, INSERM, U823, Université Joseph Fourier-Grenoble 1 and Institut Albert Bonniot, Grenoble F-38706, France.|McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1, Canada, McGill Department of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada, Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble Cedex 09, France, INSERM, U823, Université Joseph Fourier-Grenoble 1 and Institut Albert Bonniot, Grenoble F-38706, France.|McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1, Canada, McGill Department of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada, Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble Cedex 09, France, INSERM, U823, Université Joseph Fourier-Grenoble 1 and Institut Albert Bonniot, Grenoble F-38706, France.', 'Canada|Canada|Canada|Canada', 6, 'Bayes Theorem|Computer Simulation|High-Throughput Screening Assays|Models, Theoretical|Pharmaceutical Preparations|Research Design', 'Journal Article', '10.1093/bioinformatics/btt538', '24058057', 'Advantages of statistical testing of high-throughput screens include P-values, which provide objective benchmarks of compound activity, and false discovery rate estimation. The cost of replication required for statistical testing, however, may often be prohibitive. We introduce the single assay-wide variance experimental (SAVE) design whereby a small replicated subset of an entire screen is used to derive empirical Bayes random error estimates, which are applied to the remaining majority of unreplicated measurements.'),
(77934, 2013, 'bio', 'Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold', 'Nijkamp, Jurgen F|Pop, Mihai|Reinders, Marcel J T|de Ridder, Dick', 'Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.|Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.|Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.|Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.', 'Netherlands|Netherlands|Netherlands|Netherlands', 18, 'Algorithms|Alleles|Contig Mapping|Genetic Variation|Humans|Infant|Metagenome|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btt502', '24058058', 'Although many tools are available to study variation and its impact in single genomes, there is a lack of algorithms for finding such variation in metagenomes. This hampers the interpretation of metagenomics sequencing datasets, which are increasingly acquired in research on the (human) microbiome, in environmental studies and in the study of processes in the production of foods and beverages. Existing algorithms often depend on the use of reference genomes, which pose a problem when a metagenome of a priori unknown strain composition is studied. In this article, we develop a method to perform reference-free detection and visual exploration of genomic variation, both within a single metagenome and between metagenomes.'),
(77935, 2013, 'bio', 'BioServices: a common Python package to access biological Web Services programmatically', 'Cokelaer, Thomas|Pultz, Dennis|Harder, Lea M|Serra-Musach, Jordi|Saez-Rodriguez, Julio', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense 5230, Denmark, Translational Research Laboratory, Breast Cancer Unit, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, LHospitalet del Llobregat, Barcelona 08908, Catalonia, Spain and Biomedical Research Institute of Girona, Girona 17007, Catalonia, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense 5230, Denmark, Translational Research Laboratory, Breast Cancer Unit, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, LHospitalet del Llobregat, Barcelona 08908, Catalonia, Spain and Biomedical Research Institute of Girona, Girona 17007, Catalonia, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense 5230, Denmark, Translational Research Laboratory, Breast Cancer Unit, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, LHospitalet del Llobregat, Barcelona 08908, Catalonia, Spain and Biomedical Research Institute of Girona, Girona 17007, Catalonia, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense 5230, Denmark, Translational Research Laboratory, Breast Cancer Unit, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, LHospitalet del Llobregat, Barcelona 08908, Catalonia, Spain and Biomedical Research Institute of Girona, Girona 17007, Catalonia, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense 5230, Denmark, Translational Research Laboratory, Breast Cancer Unit, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, LHospitalet del Llobregat, Barcelona 08908, Catalonia, Spain and Biomedical Research Institute of Girona, Girona 17007, Catalonia, Spain.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 40, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Programming Languages|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btt547', '24064416', 'Web interfaces provide access to numerous biological databases. Many can be accessed to in a programmatic way thanks to Web Services. Building applications that combine several of them would benefit from a single framework.'),
(77936, 2013, 'bio', 'BSeQC: quality control of bisulfite sequencing experiments', 'Lin, Xueqiu|Sun, Deqiang|Rodriguez, Benjamin|Zhao, Qian|Sun, Hanfei|Zhang, Yong|Li, Wei', 'Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.', 'China|China|China|China|China|China|China', 22, 'Animals|DNA Methylation|Databases, Factual|Embryonic Stem Cells|Genome|High-Throughput Nucleotide Sequencing|Humans|Mice|Neurons|Quality Control|Software|Stem Cells|Sulfites', 'Journal Article', '10.1093/bioinformatics/btt548', '24064417', 'Bisulfite sequencing (BS-seq) has emerged as the gold standard to study genome-wide DNA methylation at single-nucleotide resolution. Quality control (QC) is a critical step in the analysis pipeline to ensure that BS-seq data are of high quality and suitable for subsequent analysis. Although several QC tools are available for next-generation sequencing data, most of them were not designed to handle QC issues specific to BS-seq protocols. Therefore, there is a strong need for a dedicated QC tool to evaluate and remove potential technical biases in BS-seq experiments.'),
(77937, 2013, 'bio', 'CPPpred: prediction of cell penetrating peptides', 'Holton, Thérèse A|Pollastri, Gianluca|Shields, Denis C|Mooney, Catherine', 'Complex and Adaptive Systems Laboratory, Conway Institute of Biomolecular and Biomedical Science, School of Medicine and Medical Science, Food For Health Ireland and School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland.|Complex and Adaptive Systems Laboratory, Conway Institute of Biomolecular and Biomedical Science, School of Medicine and Medical Science, Food For Health Ireland and School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland.|Complex and Adaptive Systems Laboratory, Conway Institute of Biomolecular and Biomedical Science, School of Medicine and Medical Science, Food For Health Ireland and School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland.|Complex and Adaptive Systems Laboratory, Conway Institute of Biomolecular and Biomedical Science, School of Medicine and Medical Science, Food For Health Ireland and School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 65, 'Cell-Penetrating Peptides|Computational Biology|Databases, Protein|Humans|Internet|Neural Networks (Computer)|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btt518', '24064418', 'Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.'),
(77938, 2013, 'bio', 'An alignment-free test for recombination', 'Haubold, Bernhard|Krause, Linda|Horn, Thomas|Pfaffelhuber, Peter', 'Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Institute for Neuro- and Bioinformatics, Lübeck University, 23562 Lübeck and Mathematical Stochastics, Mathematical Institute, Freiburg University, 79104 Freiburg, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Institute for Neuro- and Bioinformatics, Lübeck University, 23562 Lübeck and Mathematical Stochastics, Mathematical Institute, Freiburg University, 79104 Freiburg, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Institute for Neuro- and Bioinformatics, Lübeck University, 23562 Lübeck and Mathematical Stochastics, Mathematical Institute, Freiburg University, 79104 Freiburg, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Institute for Neuro- and Bioinformatics, Lübeck University, 23562 Lübeck and Mathematical Stochastics, Mathematical Institute, Freiburg University, 79104 Freiburg, Germany.', 'Germany|Germany|Germany|Germany', 5, 'Algorithms|Computational Biology|Computer Simulation|Escherichia coli|Genome, Bacterial|Phylogeny|Recombination, Genetic|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btt550', '24064419', 'Why recombination? is one of the central questions in biology. This has led to a host of methods for quantifying recombination from sequence data. These methods are usually based on aligned DNA sequences. Here, we propose an efficient alignment-free alternative.'),
(77939, 2013, 'bio', 'QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells', 'Fisher, Ciarán P|Plant, Nicholas J|Moore, J Bernadette|Kierzek, Andrzej M', 'Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, Surrey GU2 7XH, UK.|Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, Surrey GU2 7XH, UK.|Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, Surrey GU2 7XH, UK.|Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, Surrey GU2 7XH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 24, 'Algorithms|Bile Acids and Salts|Cholesterol|Computational Biology|Computer Simulation|Feasibility Studies|Gene Expression Regulation|Gene Regulatory Networks|Genetic Association Studies|Genome, Human|Hepatocytes|Humans|Metabolic Networks and Pathways|Models, Biological|Monte Carlo Method|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btt552', '24064420', 'Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype-phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation.'),
(77940, 2013, 'bio', 'Methods and challenges in timing chromosomal abnormalities within cancer samples', 'Purdom, Elizabeth|Ho, Christine|Grasso, Catherine S|Quist, Michael J|Cho, Raymond J|Spellman, Paul', 'Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.|Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.|Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.|Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.|Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.|Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.', 'United States|United States|United States|United States|United States|United States', 17, 'Bayes Theorem|Chromosome Aberrations|Computer Simulation|Female|Humans|Likelihood Functions|Ovarian Neoplasms|Skin Neoplasms|Time Factors', 'Journal Article', '10.1093/bioinformatics/btt546', '24064421', 'Tumors acquire many chromosomal amplifications, and those acquired early in the lifespan of the tumor may be not only important for tumor growth but also can be used for diagnostic purposes. Many methods infer the order of the accumulation of abnormalities based on their occurrence in a large cohort of patients. Recently, Durinck et al. (2011) and Greenman et al. (2012) developed methods to order a single tumors chromosomal amplifications based on the patterns of mutations accumulated within those regions. This method offers an unprecedented opportunity to assess the etiology of a single tumor sample, but has not been widely evaluated.'),
(77941, 2013, 'bio', 'BCov: a method for predicting xcexb2-sheet topology using sparse inverse covariance estimation and integer programming', 'Savojardo, Castrense|Fariselli, Piero|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, CIRI-Health Science and Technology/Department of Biology, University of Bologna, 40126 Bologna, Italy and Department of Computer Science and Engineering, Via Mura Anteo Zamboni 7, 40127 Bologna, Italy.|Biocomputing Group, CIRI-Health Science and Technology/Department of Biology, University of Bologna, 40126 Bologna, Italy and Department of Computer Science and Engineering, Via Mura Anteo Zamboni 7, 40127 Bologna, Italy.|Biocomputing Group, CIRI-Health Science and Technology/Department of Biology, University of Bologna, 40126 Bologna, Italy and Department of Computer Science and Engineering, Via Mura Anteo Zamboni 7, 40127 Bologna, Italy.|Biocomputing Group, CIRI-Health Science and Technology/Department of Biology, University of Bologna, 40126 Bologna, Italy and Department of Computer Science and Engineering, Via Mura Anteo Zamboni 7, 40127 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 16, 'Algorithms|DNA Mutational Analysis|Models, Statistical|Mutation|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btt555', '24064422', 'Prediction of protein residue contacts, even at the coarse-grain level, can help in finding solutions to the protein structure prediction problem. Unlike xcexb1-helices that are locally stabilized, xcexb2-sheets result from pairwise hydrogen bonding of two or more disjoint regions of the protein backbone. The problem of predicting contacts among xcexb2-strands in proteins has been addressed by several supervised computational approaches. Recently, prediction of residue contacts based on correlated mutations has been greatly improved and finally allows the prediction of 3D structures of the proteins.'),
(77942, 2013, 'bio', 'Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles', 'Zou, Lingyun|Nan, Chonghan|Hu, Fuquan', 'Department of Microbiology, College of Basic Medical Sciences, Third Military Medical University (TMMU), Chongqing 40038, China and Department of Tuberculosis, Institute of Infectious TB Prevention, Third Hospital of PLA, Baoji, Shanxi 721006, China.|Department of Microbiology, College of Basic Medical Sciences, Third Military Medical University (TMMU), Chongqing 40038, China and Department of Tuberculosis, Institute of Infectious TB Prevention, Third Hospital of PLA, Baoji, Shanxi 721006, China.|Department of Microbiology, College of Basic Medical Sciences, Third Military Medical University (TMMU), Chongqing 40038, China and Department of Tuberculosis, Institute of Infectious TB Prevention, Third Hospital of PLA, Baoji, Shanxi 721006, China.', 'China|China|China', 0, 'Amino Acids|Bacterial Proteins|Bacterial Secretion Systems|Bartonella Infections|Bartonella henselae|Computational Biology|Gene Expression Regulation, Bacterial|Genome, Bacterial|Position-Specific Scoring Matrices|Support Vector Machine|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btt554', '24064423', 'Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species, however, widely applicable prediction approaches are still unavailable'),
(77943, 2013, 'bio', 'FPSAC: fast phylogenetic scaffolding of ancient contigs', 'Rajaraman, Ashok|Tannier, Eric|Chauve, Cedric', 'Department of Mathematics, Simon Fraser University, Burnaby (BC) V5A1S6, Canada, International Graduate Training Center in Mathematical Biology, Pacific Institute for the Mathematical Sciences, Vancouver (BC), Canada, INRIA Grenoble Rhône-Alpes, Montbonnot 38334, France, Université de Lyon 1, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR5558 F-69622 Villeurbanne, France and LaBRI, Université Bordeaux I, 33405 Talence, France.|Department of Mathematics, Simon Fraser University, Burnaby (BC) V5A1S6, Canada, International Graduate Training Center in Mathematical Biology, Pacific Institute for the Mathematical Sciences, Vancouver (BC), Canada, INRIA Grenoble Rhône-Alpes, Montbonnot 38334, France, Université de Lyon 1, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR5558 F-69622 Villeurbanne, France and LaBRI, Université Bordeaux I, 33405 Talence, France.|Department of Mathematics, Simon Fraser University, Burnaby (BC) V5A1S6, Canada, International Graduate Training Center in Mathematical Biology, Pacific Institute for the Mathematical Sciences, Vancouver (BC), Canada, INRIA Grenoble Rhône-Alpes, Montbonnot 38334, France, Université de Lyon 1, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR5558 F-69622 Villeurbanne, France and LaBRI, Université Bordeaux I, 33405 Talence, France.', 'Canada|Canada|Canada', 23, 'Computer Simulation|Contig Mapping|Evolution, Molecular|Genome, Bacterial|Phylogeny|Plague|Sequence Analysis, DNA|Software|Yersinia pestis', 'Journal Article', '10.1093/bioinformatics/btt527', '24068034', 'Recent progress in ancient DNA sequencing technologies and protocols has lead to the sequencing of whole ancient bacterial genomes, as illustrated by the recent sequence of the Yersinia pestis strain that caused the Black Death pandemic. However, sequencing ancient genomes raises specific problems, because of the decay and fragmentation of ancient DNA among others, making the scaffolding of ancient contigs challenging.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77944, 2013, 'bio', 'BlastGraph: a comparative genomics tool based on BLAST and graph algorithms', 'Ye, Yanbo|Wei, Bo|Wen, Lei|Rayner, Simon', 'Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, Hubei, China, 430071, Graduate School of Chinese Academy of Sciences, Beijing, China and Exiqon A/S, Vedbaek, Denmark.|Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, Hubei, China, 430071, Graduate School of Chinese Academy of Sciences, Beijing, China and Exiqon A/S, Vedbaek, Denmark.|Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, Hubei, China, 430071, Graduate School of Chinese Academy of Sciences, Beijing, China and Exiqon A/S, Vedbaek, Denmark.|Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, Hubei, China, 430071, Graduate School of Chinese Academy of Sciences, Beijing, China and Exiqon A/S, Vedbaek, Denmark.', 'China|China|China|China', 7, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Genomics|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt553', '24068035', 'BlastGraph is an interactive Java program for comparative genome analysis based on Basic Local Alignment Search Tool (BLAST), graph clustering and data visualization. The software generates clusters of sequences of multiple genomes from all-to-all BLAST results and visualizes the results in graph plots together with related information such as sequence features, gene conservation and similarity relationships. Pruning algorithms are used to reduce results to more meaningful subclusters. Subsequent analyses can then be conducted based on the predicted clusters, including gene content, genome phylogenetics and gene gain and loss.'),
(77945, 2013, 'bio', 'WebGLORE: a web service for Grid LOgistic REgression', 'Jiang, Wenchao|Li, Pinghao|Wang, Shuang|Wu, Yuan|Xue, Meng|Ohno-Machado, Lucila|Jiang, Xiaoqian', 'Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA and Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.', 'China|China|China|China|China|China|China', 17, 'Area Under Curve|Biomedical Research|Computer Communication Networks|Confidentiality|Cooperative Behavior|Humans|Information Dissemination|Internet|Logistic Models|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btt559', '24072732', 'WebGLORE is a free web service that enables privacy-preserving construction of a global logistic regression model from distributed datasets that are sensitive. It only transfers aggregated local statistics (from participants) through Hypertext Transfer Protocol Secure to a trusted server, where the global model is synthesized. WebGLORE seamlessly integrates AJAX, JAVA Applet/Servlet and PHP technologies to provide an easy-to-use web service for biomedical researchers to break down policy barriers during information exchange.'),
(77946, 2013, 'bio', 'MATE-CLEVER: Mendelian-inheritance-aware discovery and genotyping of midsize and long indels', 'Marschall, Tobias|Hajirasouliha, Iman|Schxc3xb6nhuth, Alexander', 'Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Science Park 123, Amsterdam 1098 XG, The Netherlands and Department of Computer Science and Brown University, Providence, Rhode Island 02906, USA.|Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Science Park 123, Amsterdam 1098 XG, The Netherlands and Department of Computer Science and Brown University, Providence, Rhode Island 02906, USA.|Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Science Park 123, Amsterdam 1098 XG, The Netherlands and Department of Computer Science and Brown University, Providence, Rhode Island 02906, USA.', 'Netherlands|Netherlands|Netherlands', 29, 'Algorithms|Computer Simulation|Genetic Variation|Genome, Human|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Inheritance Patterns|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btt556', '24072733', 'Accurately predicting and genotyping indels longer than 30 bp has remained a central challenge in next-generation sequencing (NGS) studies. While indels of up to 30 bp are reliably processed by standard read aligners and the Genome Analysis Toolkit (GATK), longer indels have still resisted proper treatment. Also, discovering and genotyping longer indels has become particularly relevant owing to the increasing attention in globally concerted projects.'),
(77947, 2013, 'bio', 'Molecular causes of transcriptional response: a Bayesian prior knowledge approach', 'Zarringhalam, Kourosh|Enayetallah, Ahmed|Gutteridge, Alex|Sidders, Ben|Ziemek, Daniel', 'Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.|Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.|Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.|Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.|Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Animals|Bayes Theorem|Cell Differentiation|Cells, Cultured|Computer Simulation|Dexamethasone|Gene Expression Profiling|Gene Expression Regulation|Humans|Insulin-Secreting Cells|Keratinocytes|Markov Chains|Mice|Models, Biological|Pain|Protein Processing, Post-Translational|Rats|Regulatory Elements, Transcriptional|Signal Transduction|Stem Cells', 'Journal Article', '10.1093/bioinformatics/btt557', '24078682', 'The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.'),
(77948, 2013, 'bio', 'DockAFM: benchmarking protein structures by docking under AFM topographs', 'Chaves, Rui C|Pellequer, Jean-Luc', 'CEA, iBEB, Service de Biochimie et Toxicologie Nucléaire, F-30207 Bagnols sur Cèze, France.|CEA, iBEB, Service de Biochimie et Toxicologie Nucléaire, F-30207 Bagnols sur Cèze, France.', 'France|France', 7, 'Factor V|Humans|Immunoglobulin G|Microscopy, Atomic Force|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btt561', '24078683', 'Proteins can adopt a variety of conformations. We present a simple server for scoring the agreement between 3D atomic structures and experimental envelopes obtained by atomic force microscopy. Three different structures of immunoglobulins (IgG) or blood coagulation factor V activated were tested and their agreement with several topographical surfaces was computed. This approach can be used to test structural variability within a family of proteins.'),
(77949, 2013, 'bio', 'INVEX--a web-based tool for integrative visualization of expression data', 'Xia, Jianguo|Lyle, Ngan H|Mayer, Matthew L|Pena, Olga M|Hancock, Robert E W', 'Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3, Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada V6T 1Z4 and Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3, Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada V6T 1Z4 and Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3, Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada V6T 1Z4 and Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3, Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada V6T 1Z4 and Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3, Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada V6T 1Z4 and Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.', 'Canada|Canada|Canada|Canada|Canada', 26, 'Animals|Computer Graphics|Gene Expression Profiling|Humans|Internet|Metabolomics|Molecular Sequence Annotation|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt562', '24078684', 'Gene expression or metabolomics data generated from clinical settings are often associated with multiple metadata (i.e. diagnosis, genotype, gender, etc.). It is of great interest to analyze and to visualize the data in these contexts. Here, we introduce INVEX-a novel web-based tool that integrates the server-side capabilities for data analysis with the browse-based technology for data visualization. INVEX has two key features: (i) flexible differential expression analysis for a wide variety of experimental designs, and (ii) interactive visualization within the context of metadata and biological annotations. INVEX has built-in support for gene/metabolite annotation and a fully functional heatmap builder.'),
(77950, 2013, 'bio', 'Lep-MAP: fast and accurate linkage map construction for large SNP datasets', 'Rastas, Pasi|Paulin, Lars|Hanski, Ilkka|Lehtonen, Rainer|Auvinen, Petri', 'Department of Biosciences, Metapopulation Research Group, University of Helsinki, P.O. Box 65, FI-00014, Finland and Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Finland.|Department of Biosciences, Metapopulation Research Group, University of Helsinki, P.O. Box 65, FI-00014, Finland and Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Finland.|Department of Biosciences, Metapopulation Research Group, University of Helsinki, P.O. Box 65, FI-00014, Finland and Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Finland.|Department of Biosciences, Metapopulation Research Group, University of Helsinki, P.O. Box 65, FI-00014, Finland and Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Finland.|Department of Biosciences, Metapopulation Research Group, University of Helsinki, P.O. Box 65, FI-00014, Finland and Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Finland.', 'Finland|Finland|Finland|Finland|Finland', 74, 'Animals|Chromosome Mapping|Computational Biology|Databases, Genetic|Female|Genetic Linkage|Genome|Haplotypes|Insect Proteins|Lepidoptera|Lod Score|Meiosis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt563', '24078685', 'Current high-throughput sequencing technologies allow cost-efficient genotyping of millions of single nucleotide polymorphisms (SNPs) for hundreds of samples. However, the tools that are currently available for constructing linkage maps are not well suited for large datasets. Linkage maps of large datasets would be helpful in de novo genome assembly by facilitating comprehensive genome validation and refinement by enabling chimeric scaffold detection, as well as in family-based linkage and association studies, quantitative trait locus mapping, analysis of genome synteny and other complex genomic data analyses.'),
(77951, 2013, 'bio', 'Fuzzy optimization for detecting enzyme targets of human uric acid metabolism', 'Hsu, Kai-Cheng|Wang, Feng-Sheng', 'Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan.|Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan.', '-|-', 8, 'Algorithms|Diet|Drug Design|Drug Discovery|Fuzzy Logic|Humans|Hyperuricemia|Kinetics|Lesch-Nyhan Syndrome|Metabolic Networks and Pathways|Models, Biological|Probenecid|Purine-Pyrimidine Metabolism, Inborn Errors|Purines|Ribose-Phosphate Pyrophosphokinase|Ribosemonophosphates|Uric Acid|Uricosuric Agents', 'Journal Article', '10.1093/bioinformatics/btt564', '24078686', 'Mathematical modeling and optimization have been used for detecting enzyme targets in human metabolic disorders. Such optimal drug design methods are generally differentiated as two stages, identification and decision-making, to find optimal targets. We developed a unified method named fuzzy equal metabolic adjustment to formulate an optimal enzyme target design problem for drug discovery. The optimization framework combines the identification of enzyme targets and a decision-making strategy simultaneously. The objectives of this algorithm include evaluations of the therapeutic effect of target enzymes, the adverse effects of drugs and the minimum effective dose (MED).'),
(77952, 2013, 'bio', 'STAR: an integrated solution to management and visualization of sequencing data', 'Wang, Tao|Liu, Jie|Shen, Li|Tonti-Filippini, Julian|Zhu, Yun|Jia, Haiyang|Lister, Ryan|Whitaker, John W|Ecker, Joseph R|Millar, A Harvey|Ren, Bing|Wang, Wei', 'Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, Western Australia 6009, Australia, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China, Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093, USA and Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 7, 'Computer Graphics|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt558', '24078702', 'Easily visualization of complex data features is a necessary step to conduct studies on next-generation sequencing (NGS) data. We developed STAR, an integrated web application that enables online management, visualization and track-based analysis of NGS data.'),
(77953, 2013, 'bio', 'MS2PIP: a tool for MS/MS peak intensity prediction', 'Degroeve, Sven|Martens, Lennart', 'Department of Medical Protein Research, VIB, Ghent 9000, Belgium and Department of Biochemistry, Ghent University, Ghent 9000, Belgium.|Department of Medical Protein Research, VIB, Ghent 9000, Belgium and Department of Biochemistry, Ghent University, Ghent 9000, Belgium.', 'Belgium|Belgium', 41, 'Algorithms|Animals|Humans|Peptide Fragments|Protein Processing, Post-Translational|Proteins|Proteomics|Regression Analysis|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt544', '24078703', 'Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research.'),
(77954, 2013, 'bio', 'Optimized atomic statistical potentials: assessment of protein interfaces and loops', 'Dong, Guang Qiang|Fan, Hao|Schneidman-Duhovny, Dina|Webb, Ben|Sali, Andrej', 'Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.|Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.|Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.|Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.|Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.', 'United States|United States|United States|United States|United States', 50, 'Bayes Theorem|Computational Biology|Hydrogen Bonding|Models, Statistical|Molecular Docking Simulation|Protein Conformation|Protein Interaction Domains and Motifs|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt560', '24078704', 'Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state.'),
(77955, 2014, 'bio', 'A user-oriented web crawler for selectively acquiring online content in e-health research', 'Xu, Songhua|Yoon, Hong-Jun|Tourassi, Georgia', 'Biomedical Science and Engineering Center, Health Data Sciences Institute, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37830, USA.|Biomedical Science and Engineering Center, Health Data Sciences Institute, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37830, USA.|Biomedical Science and Engineering Center, Health Data Sciences Institute, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37830, USA.', 'United States|United States|United States', 16, 'Computational Biology|Humans|Internet|Neoplasms|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt571', '24078710', 'Life stories of diseased and healthy individuals are abundantly available on the Internet. Collecting and mining such online content can offer many valuable insights into patients physical and emotional states throughout the pre-diagnosis, diagnosis, treatment and post-treatment stages of the disease compared with those of healthy subjects. However, such content is widely dispersed across the web. Using traditional query-based search engines to manually collect relevant materials is rather labor intensive and often incomplete due to resource constraints in terms of human query composition and result parsing efforts. The alternative option, blindly crawling the whole web, has proven inefficient and unaffordable for e-health researchers.'),
(77956, 2013, 'bio', 'GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework', 'Przydzial, Magdalena J|Bhhatarai, Barun|Koleti, Amar|Vempati, Uma|Schürer, Stephan C', 'Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.', 'United States|United States|United States|United States|United States', 30, 'Computational Biology|Databases, Factual|Drug Design|Drug Evaluation, Preclinical|High-Throughput Screening Assays|Humans|Knowledge Bases|Ligands|Pharmaceutical Preparations|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/btt565', '24078711', 'Novel tools need to be developed to help scientists analyze large amounts of available screening data with the goal to identify entry points for the development of novel chemical probes and drugs. As the largest class of drug targets, G protein-coupled receptors (GPCRs) remain of particular interest and are pursued by numerous academic and industrial research projects.'),
(77957, 2014, 'bio', 'BooleSim: an interactive Boolean network simulator', 'Bock, Matthias|Scharp, Till|Talnikar, Chaitanya|Klipp, Edda', 'Department of Biology, Theoretical Biophysics, Humboldt-Universität zu Berlin, 10115 Berlin, Germany and Chemical Engineering Department, Indian Institute of Technology Bombay, Mumbai 400 076, India.|Department of Biology, Theoretical Biophysics, Humboldt-Universität zu Berlin, 10115 Berlin, Germany and Chemical Engineering Department, Indian Institute of Technology Bombay, Mumbai 400 076, India.|Department of Biology, Theoretical Biophysics, Humboldt-Universität zu Berlin, 10115 Berlin, Germany and Chemical Engineering Department, Indian Institute of Technology Bombay, Mumbai 400 076, India.|Department of Biology, Theoretical Biophysics, Humboldt-Universität zu Berlin, 10115 Berlin, Germany and Chemical Engineering Department, Indian Institute of Technology Bombay, Mumbai 400 076, India.', 'Germany|Germany|Germany|Germany', 16, 'Algorithms|Computer Simulation|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btt568', '24078712', 'BooleSim (Boolean network simulator) is an open-source in-browser tool for simulation and manipulation of Boolean networks. It was developed mainly during Googles Summer of Code 2012 and uses the biographer project for network visualization. It can be used specifically for the modeling of gene regulatory or signal transduction networks.'),
(77958, 2013, 'bio', 'ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses', 'Huntley, Melanie A|Larson, Jessica L|Chaivorapol, Christina|Becker, Gabriel|Lawrence, Michael|Hackney, Jason A|Kaminker, Joshua S', 'Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA and Department of Statistics, University of California at Davis, Davis, CA 95616, USA.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btt551', '24078713', 'It is common for computational analyses to generate large amounts of complex data that are difficult to process and share with collaborators. Standard methods are needed to transform such data into a more useful and intuitive format. We present ReportingTools, a Bioconductor package, that automatically recognizes and transforms the output of many common Bioconductor packages into rich, interactive, HTML-based reports. Reports are not generic, but have been individually designed to reflect content specific to the result type detected. Tabular output included in reports is sortable, filterable and searchable and contains context-relevant hyperlinks to external databases. Additionally, in-line graphics have been developed for specific analysis types and are embedded by default within table rows, providing a useful visual summary of underlying raw data. ReportingTools is highly flexible and reports can be easily customized for specific applications using the well-defined API.'),
(77959, 2014, 'bio', 'A C library for retrieving specific reactions from the BioModels database', 'Neal, M L|Galdzicki, M|Gallimore, J T|Sauro, H M', 'Department of Bioengineering, University of Washington, Seattle, WA 98195, USA and Department of Computer Science, Wofford College, Spartanburg, SC 29303, USA.|Department of Bioengineering, University of Washington, Seattle, WA 98195, USA and Department of Computer Science, Wofford College, Spartanburg, SC 29303, USA.|Department of Bioengineering, University of Washington, Seattle, WA 98195, USA and Department of Computer Science, Wofford College, Spartanburg, SC 29303, USA.|Department of Bioengineering, University of Washington, Seattle, WA 98195, USA and Department of Computer Science, Wofford College, Spartanburg, SC 29303, USA.', 'United States|United States|United States|United States', 4, 'Biochemical Phenomena|Databases, Factual|Internet|Programming Languages|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt567', '24078714', 'We describe libSBMLReactionFinder, a C library for retrieving specific biochemical reactions from the curated systems biology markup language models contained in the BioModels database. The library leverages semantic annotations in the database to associate reactions with human-readable descriptions, making the reactions retrievable through simple string searches. Our goal is to provide a useful tool for quantitative modelers who seek to accelerate modeling efforts through the reuse of previously published representations of specific chemical reactions.'),
(77960, 2014, 'bio', 'MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples', 'Liebner, David A|Huang, Kun|Parvin, Jeffrey D', 'Division of Medical Oncology, Department of Internal Medicine, Department of Biomedical Informatics and Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA.|Division of Medical Oncology, Department of Internal Medicine, Department of Biomedical Informatics and Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA.|Division of Medical Oncology, Department of Internal Medicine, Department of Biomedical Informatics and Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA.', 'United States|United States|United States', 33, 'Algorithms|Animals|Computer Simulation|Gene Expression Profiling|Microdissection|Oligonucleotide Array Sequence Analysis|Rats|Software|Tissue Array Analysis', 'Journal Article', '10.1093/bioinformatics/btt566', '24085566', 'One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types.'),
(77961, 2014, 'bio', 'Functional module identification in protein interaction networks by interaction patterns', 'Wang, Yijie|Qian, Xiaoning', 'Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 27, 'Algorithms|Cluster Analysis|Humans|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btt569', '24085567', 'Identifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of higher than expected connectivity, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.'),
(77962, 2014, 'bio', 'Spatial regulation dominates gene function in the ganglia chain', 'Hibsh, Dror|Schori, Hadas|Efroni, Sol|Shefi, Orit', 'Faculty of Life Sciences, Faculty of Engineering and Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel 52900.|Faculty of Life Sciences, Faculty of Engineering and Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel 52900.|Faculty of Life Sciences, Faculty of Engineering and Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel 52900.|Faculty of Life Sciences, Faculty of Engineering and Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel 52900.', 'Israel|Israel|Israel|Israel', 0, 'Animals|Ganglia|Gene Expression Profiling|Gene Expression Regulation|Genomics|Hirudo medicinalis|RNA Isoforms|RNA, Messenger|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt570', '24085568', 'To understand the molecular mechanisms of neurons, it is imperative to identify genomic dissimilarities within the heterogeneity of the neural system. This is especially true for neuronal disorders in which spatial considerations are of critical nature. For this purpose, Hirudo medicinalis provides here an ideal system in which we are able to follow gene expression along the central nervous system, to affiliate location with gene behavior.'),
(77963, 2013, 'bio', 'Compound cis-regulatory elements with both boundary and enhancer sequences in the human genome', 'Jjingo, Daudi|Wang, Jianrong|Conley, Andrew B|Lunyak, Victoria V|Jordan, I King', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA, Buck Institute for Research on Aging, Novato, CA 94945, USA and PanAmerican Bioinformatics Institute, Santa Marta, Magdalena, Colombia.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA, Buck Institute for Research on Aging, Novato, CA 94945, USA and PanAmerican Bioinformatics Institute, Santa Marta, Magdalena, Colombia.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA, Buck Institute for Research on Aging, Novato, CA 94945, USA and PanAmerican Bioinformatics Institute, Santa Marta, Magdalena, Colombia.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA, Buck Institute for Research on Aging, Novato, CA 94945, USA and PanAmerican Bioinformatics Institute, Santa Marta, Magdalena, Colombia.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA, Buck Institute for Research on Aging, Novato, CA 94945, USA and PanAmerican Bioinformatics Institute, Santa Marta, Magdalena, Colombia.', 'Colombia|Colombia|Colombia|Colombia|Colombia', 0, 'CD4-Positive T-Lymphocytes|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Gene Expression Regulation|Genome, Human|Humans|Inflammation|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btt542', '24085569', 'It has been suggested that presumably distinct classes of genomic regulatory elements may actually share common sets of features and mechanisms. However, there has been no genome-wide assessment of the prevalence of this phenomenon.'),
(77964, 2014, 'bio', 'iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis', 'Sun, Haoqi|Wang, Haiping|Zhu, Ruixin|Tang, Kailin|Gong, Qin|Cui, Juan|Cao, Zhiwei|Liu, Qi', 'Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Siping Rd. No. 1239, Shanghai 200092, Department of Computer Science, Hefei University of Technology, Tunxi Rd. No. 193, Hefei 230009, China and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602-7229, USA.', 'China|China|China|China|China|China|China|China', 25, 'Algorithms|Cell Line, Tumor|Gene Expression Profiling|Genome-Wide Association Study|Humans|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt576', '24092766', 'A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in high-throughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77965, 2014, 'bio', 'A wavelet-based method to exploit epigenomic language in the regulatory region', 'Nguyen, Nha|Vo, An|Won, Kyoung-Jae', 'Department of Genetics, Institute for Diabetes, Obesity and Metabolism, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.|Department of Genetics, Institute for Diabetes, Obesity and Metabolism, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.|Department of Genetics, Institute for Diabetes, Obesity and Metabolism, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.', 'United States|United States|United States', 5, 'Algorithms|Animals|Cell Line|Cluster Analysis|Embryonic Stem Cells|Epigenomics|Fibroblasts|Gene Expression Regulation|Histones|Humans|Lung|Mice|Software Design', 'Journal Article', '10.1093/bioinformatics/btt467', '24096080', 'Epigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed.'),
(77966, 2014, 'bio', 'Robust adjustment of sequence tag abundance', 'Baumann, Douglas D|Doerge, Rebecca W', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States', 2, 'Arabidopsis|Expressed Sequence Tags|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt575', '24108185', 'The majority of next-generation sequencing technologies effectively sample small amounts of DNA or RNA that are amplified (i.e. copied) before sequencing. The amplification process is not perfect, leading to extreme bias in sequenced read counts. We present a novel procedure to account for amplification bias and demonstrate its effectiveness in mitigating gene length dependence when estimating true gene expression.'),
(77967, 2014, 'bio', 'The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies', 'Harispe, Sébastien|Ranwez, Sylvie|Janaqi, Stefan|Montmain, Jacky', 'LGI2P/EMA Research Centre, Site EERIE, Parc Scientifique G. Besse, 30035 Nîmes cedex 1, France.|LGI2P/EMA Research Centre, Site EERIE, Parc Scientifique G. Besse, 30035 Nîmes cedex 1, France.|LGI2P/EMA Research Centre, Site EERIE, Parc Scientifique G. Besse, 30035 Nîmes cedex 1, France.|LGI2P/EMA Research Centre, Site EERIE, Parc Scientifique G. Besse, 30035 Nîmes cedex 1, France.', 'France|France|France|France', 60, 'Biological Ontologies|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btt581', '24108186', 'The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters.'),
(77968, 2014, 'bio', 'orthAgogue: an agile tool for the rapid prediction of orthology relations', 'Ekseth, Ole Kristian|Kuiper, Martin|Mironov, Vladimir', 'Semantic Systems Biology Group, Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.|Semantic Systems Biology Group, Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.|Semantic Systems Biology Group, Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.', 'Norway|Norway|Norway', 0, 'Algorithms|Sequence Alignment|Sequence Homology|Software', 'Journal Article', '10.1093/bioinformatics/btt582', '24115168', 'The comparison of genes and gene products across species depends on high-quality tools to determine the relationships between gene or protein sequences from various species. Although some excellent applications are available and widely used, their performance leaves room for improvement.'),
(77969, 2014, 'bio', 'A penalized Bayesian approach to predicting sparse protein-DNA binding landscapes', 'Levinson, Matthew|Zhou, Qing', 'Department of Statistics, University of California, Los Angeles, CA 90095, USA.|Department of Statistics, University of California, Los Angeles, CA 90095, USA.', 'United States|United States', 3, 'Algorithms|Animals|Bayes Theorem|Binding Sites|Chromatin Immunoprecipitation|DNA-Binding Proteins|Embryonic Stem Cells|Genomics|Mice|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt585', '24115169', 'Cellular processes are controlled, directly or indirectly, by the binding of hundreds of different DNA binding factors (DBFs) to the genome. One key to deeper understanding of the cell is discovering where, when and how strongly these DBFs bind to the DNA sequence. Direct measurement of DBF binding sites (BSs, e.g. through ChIP-Chip or ChIP-Seq experiments) is expensive, noisy and not available for every DBF in every cell type. Naive and most existing computational approaches to detecting which DBFs bind in a set of genomic regions of interest often perform poorly, due to the high false discovery rates and restrictive requirements for prior knowledge.'),
(77970, 2013, 'bio', 'An integrated computational approach for metabolic flux analysis coupled with inference of tandem-MS collisional fragments', 'Tepper, Naama|Shlomi, Tomer', 'Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.|Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.', 'Israel|Israel', 4, 'Algorithms|Bacillus subtilis|Carbon Isotopes|Computational Biology|Computer Simulation|Gluconeogenesis|Isotope Labeling|Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Theoretical|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt516', '24123514', 'Metabolic flux analysis (MFA) is a commonly used approach for quantifying metabolic fluxes based on tracking isotope labeling of metabolite within cells. Tandem mass-spectrometry (MS/MS) has been recently shown to be especially useful for MFA by providing rich information on metabolite positional labeling, measuring isotopic labeling patterns of collisional fragments. However, a major limitation in this approach is the requirement that the positional origin of atoms in a collisional fragment would be known a priori, which in many cases is difficult to determine.'),
(77971, 2013, 'bio', 'SOAPfusion: a robust and effective computational fusion discovery tool for RNA-seq reads', 'Wu, Jikun|Zhang, Wenqian|Huang, Songbo|He, Zengquan|Cheng, Yanbing|Wang, Jun|Lam, Tak-Wah|Peng, Zhiyu|Yiu, Siu-Ming', 'Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, China, BGI-Shenzhen, Shenzhen, China, Department of Biology, University of Copenhagen, Copenhagen, Denmark, King Abdulaziz University, Jeddah, Saudi Arabia, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and Guangzhou Key Laboratory of Cancer Trans-Omics Research, BGI-Guangzhou, Guangzhou, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Base Sequence|Computational Biology|Gene Fusion|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Data|Neoplasms|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btt522', '24123671', 'RNA-Seq provides a powerful approach to carry out ab initio investigation of fusion transcripts representing critical translocation and post-transcriptional events that recode hereditary information. Most of the existing computational fusion detection tools are challenged by the issues of accuracy and how to handle multiple mappings.'),
(77972, 2014, 'bio', 'Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets', 'Rodriguez-R, Luis M|Konstantinidis, Konstantinos T', 'Center for Bioinformatics and Computational Genomics, School of Biology and School of Civil and Environmental Engineering, Georgia Institute of Technology, 311 Ferst Drive, Ford ES&T Building, Suite 3224, Atlanta, GA 30332, USA.|Center for Bioinformatics and Computational Genomics, School of Biology and School of Civil and Environmental Engineering, Georgia Institute of Technology, 311 Ferst Drive, Ford ES&T Building, Suite 3224, Atlanta, GA 30332, USA.', 'Georgia|Georgia', 0, 'Algorithms|Metagenome|Metagenomics|Microbiota|Soil Microbiology', 'Journal Article', '10.1093/bioinformatics/btt584', '24123672', 'Determining the fraction of the diversity within a microbial community sampled and the amount of sequencing required to cover the total diversity represent challenging issues for metagenomics studies. Owing to these limitations, central ecological questions with respect to the global distribution of microbes and the functional diversity of their communities cannot be robustly assessed.'),
(77973, 2014, 'bio', 'Two novel pathway analysis methods based on a hierarchical model', 'Evangelou, Marina|Dudbridge, Frank|Wernisch, Lorenz', 'Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrookes Hospital, Cambridge, CB2 0XY, UK and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.|Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrookes Hospital, Cambridge, CB2 0XY, UK and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.|Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrookes Hospital, Cambridge, CB2 0XY, UK and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Algorithms|Bayes Theorem|Blood Platelets|Body Mass Index|Computer Simulation|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btt583', '24123673', 'Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date.'),
(77974, 2014, 'bio', 'PLIDA: cross-platform gene expression normalization using perturbed topic models', 'Deshwar, Amit G|Morris, Quaid', 'Edward S. Rogers Sr. Department of Electrical and Computer Engineering, Department of Molecular Genetics, Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 1A1, Canada.|Edward S. Rogers Sr. Department of Electrical and Computer Engineering, Department of Molecular Genetics, Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 1A1, Canada.', 'Canada|Canada', 0, 'Breast Neoplasms|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btt574', '24123674', 'Gene expression data are currently collected on a wide range of platforms. Differences between platforms make it challenging to combine and compare data collected on different platforms. We propose a new method of cross-platform normalization that uses topic models to summarize the expression patterns in each dataset before normalizing the topics learned from each dataset using per-gene multiplicative weights.'),
(77975, 2014, 'bio', 'GIIRA--RNA-Seq driven gene finding incorporating ambiguous reads', 'Zickmann, Franziska|Lindner, Martin S|Renard, Bernhard Y', 'Research Group Bioinformatics (NG4), Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany.', 'Germany|Germany|Germany', 10, 'Algorithms|Animals|Escherichia coli|Gene Expression Profiling|Genes|Genomics|Humans|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt577', '24123675', 'The reliable identification of genes is a major challenge in genome research, as further analysis depends on the correctness of this initial step. With high-throughput RNA-Seq data reflecting currently expressed genes, a particularly meaningful source of information has become commonly available for gene finding. However, practical application in automated gene identification is still not the standard case. A particular challenge in including RNA-Seq data is the difficult handling of ambiguously mapped reads.'),
(77976, 2014, 'bio', 'ORMAN: optimal resolution of ambiguous RNA-Seq multimappings in the presence of novel isoforms', 'Dao, Phuong|Numanagixc4x87, Ibrahim|Lin, Yen-Yi|Hach, Faraz|Karakoc, Emre|Donmez, Nilgun|Collins, Colin|Eichler, Evan E|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, Department of Genome Sciences, University of Washington, Seattle, WA, USA, Vancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada and Division of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 9, 'Algorithms|Alternative Splicing|Exons|Gene Expression Profiling|Humans|RNA Isoforms|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt591', '24130305', 'RNA-Seq technology is promising to uncover many novel alternative splicing events, gene fusions and other variations in RNA transcripts. For an accurate detection and quantification of transcripts, it is important to resolve the mapping ambiguity for those RNA-Seq reads that can be mapped to multiple loci: >17% of the reads from mouse RNA-Seq data and 50% of the reads from some plant RNA-Seq data have multiple mapping loci. In this study, we show how to resolve the mapping ambiguity in the presence of novel transcriptomic events such as exon skipping and novel indels towards accurate downstream analysis. We introduce ORMAN ( O ptimal R esolution of M ultimapping A mbiguity of R N A-Seq Reads), which aims to compute the minimum number of potential transcript products for each gene and to assign each multimapping read to one of these transcripts based on the estimated distribution of the region covering the read. ORMAN achieves this objective through a combinatorial optimization formulation, which is solved through well-known approximation algorithms, integer linear programs and heuristics.'),
(77977, 2014, 'bio', 'SecStAnT: secondary structure analysis tool for data selection, statistics and models building', 'Maccari, Giuseppe|Spampinato, Giulia L B|Tozzini, Valentina', 'Center for Nanotechnology and Innovation @NEST, Istituto Italiano di Tecnologia, NEST, Istituto Nanoscienze - CNR and Scuola Normale Superiore, Piazza San Silvestro 12-56127 Pisa and Dipartimento di Fisica E. Fermi, Università di Pisa Largo B. Pontecorvo 3-56127 Pisa, Italy.|Center for Nanotechnology and Innovation @NEST, Istituto Italiano di Tecnologia, NEST, Istituto Nanoscienze - CNR and Scuola Normale Superiore, Piazza San Silvestro 12-56127 Pisa and Dipartimento di Fisica E. Fermi, Università di Pisa Largo B. Pontecorvo 3-56127 Pisa, Italy.|Center for Nanotechnology and Innovation @NEST, Istituto Italiano di Tecnologia, NEST, Istituto Nanoscienze - CNR and Scuola Normale Superiore, Piazza San Silvestro 12-56127 Pisa and Dipartimento di Fisica E. Fermi, Università di Pisa Largo B. Pontecorvo 3-56127 Pisa, Italy.', 'Italy|Italy|Italy', 0, 'Data Interpretation, Statistical|Models, Molecular|Molecular Dynamics Simulation|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt586', '24130306', 'Atomistic or coarse grained (CG) potentials derived from statistical distributions of internal variables have recently become popular due to the need of simplified interactions for reaching larger scales in simulations or more efficient conformational space sampling. However, the process of parameterization of accurate and predictive statistics-based force fields requires a huge amount of work and is prone to the introduction of bias and errors.'),
(77978, 2014, 'bio', 'Comment on Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching', 'Anderson, Eric C|Ng, Thomas C', 'Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 110 Shaffer Road, Santa Cruz, CA 95060, Department of Applied Math and Statistics and Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.|Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 110 Shaffer Road, Santa Cruz, CA 95060, Department of Applied Math and Statistics and Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.', 'United States|United States', 0, 'Animals|Genotyping Techniques', 'Letter', '10.1093/bioinformatics/btt588', '24130307', 'We show the software SOLOMON is improved by using the likelihood ratio instead of an ad hoc statistic. CODE: xe2x80x82github.com/eriqande/solidmon/releases/tag/v0.1-bioinformatics'),
(77979, 2014, 'bio', 'FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking', '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, 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, P.O. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, 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, P.O. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, 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, P.O. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, 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, P.O. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Databases, Protein|Neural Networks (Computer)|Protein Conformation|Protein Folding|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btt578', '24130308', 'Homology detection enables grouping proteins into families and prediction of their structure and function. The range of application of homology-based predictions can be significantly extended by using sequence profiles and incorporation of local structural features. However, incorporation of the latter terms varies a lot between existing methods, and together with many examples of distant relations not recognized even by the best methods, suggests that further improvements are still possible.'),
(77980, 2014, 'bio', 'De novo finished 28 Mbp Staphylococcus aureus genome assembly from 100 bp short and long range paired-end reads', 'Hernandez, David|Tewhey, Ryan|Veyrieras, Jean-Baptiste|Farinelli, Laurent|Østerås, Magne|François, Patrice|Schrenzel, Jacques', 'Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.|Genomic Research Laboratory, Infectious Diseases Service, Geneva University Hospitals, 1211 Geneva 4, Switzerland, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA, BioMérieux, Data and Knowledge Laboratory, 69280 Marcy lEtoile, France and Fasteris SA, PO Box 28, 1228 Plan-les-Ouates, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 35, 'Algorithms|Base Sequence|Chromosome Mapping|Genome|Genomics|High-Throughput Nucleotide Sequencing|Molecular Sequence Data|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btt590', '24130309', 'Paired-end sequencing allows circumventing the shortness of the reads produced by second generation sequencers and is essential for de novo assembly of genomes. However, obtaining a finished genome from short reads is still an open challenge. We present an algorithm that exploits the pairing information issued from inserts of potentially any length. The method determines paths through an overlaps graph by using a constrained search tree. We also present a method that automatically determines suited overlaps cutoffs according to the contextual coverage, reducing thus the need for manual parameterization. Finally, we introduce an interactive mode that allows querying an assembly at targeted regions.'),
(77981, 2014, 'bio', 'gCMAP: user-friendly connectivity mapping with R', 'Sandmann, Thomas|Kummerfeld, Sarah K|Gentleman, Robert|Bourgon, Richard', 'Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.|Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.', 'United States|United States|United States|United States', 15, 'Animals|Cell Line|Gene Expression Profiling|Humans|Internet|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt592', '24132929', 'Connections between disease phenotypes and drug effects can be made by identifying commonalities in the associated patterns of differential gene expression. Searchable databases that record the impacts of chemical or genetic perturbations on the transcriptome--here referred to as connectivity maps--permit discovery of such commonalities. We describe two R packages, gCMAP and gCMAPWeb, which provide a complete framework to construct and query connectivity maps assembled from user-defined collections of differential gene expression data. Microarray or RNAseq data are processed in a standardized way, and results can be interrogated using various well-established gene set enrichment methods. The packages also feature an easy-to-deploy web application that facilitates reproducible research through automatic generation of graphical and tabular reports.'),
(77982, 2014, 'bio', 'Thymine distribution in genes provides novel insight into the functional significance of the proteome of the malaria parasite Plasmodium falciparum 3D7', 'Palanisamy, Balamurugan|Ekambaram, Rajasekaran|Heese, Klaus', 'School of Biotechnology and Health Sciences, Karunya University, Karunya Nagar, Coimbatore 641114, Tamil Nadu, India and Graduate School of Biomedical Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea.|School of Biotechnology and Health Sciences, Karunya University, Karunya Nagar, Coimbatore 641114, Tamil Nadu, India and Graduate School of Biomedical Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea.|School of Biotechnology and Health Sciences, Karunya University, Karunya Nagar, Coimbatore 641114, Tamil Nadu, India and Graduate School of Biomedical Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea.', 'India|India|India', 2, 'Genes, Protozoan|Genomics|Hydrophobic and Hydrophilic Interactions|Plasmodium falciparum|Proteome|Protozoan Proteins|Thymine', 'Journal Article', '10.1093/bioinformatics/btt587', '24132930', 'Plasmodium falciparum (Pf)-mediated malaria is one of the most devastating diseases in the world, and the search for suitable antimalarial drugs remains an extraordinary challenge for scientists working in this area. Novel unconventional approaches could reveal new potential targets that may be useful for the treatment of malaria. We used a bioinformatics approach to analyze the entire genome of the Pf3D7 strain. Because the carbon (C-) content is a pivotal parameter that determines the hydrophobicity of a protein, which in turn controls protein folding and function, we analyzed the entire Pf3D7 proteome based on the genes thymine (T)-controlled amino acid expression. Our data disclose a total of 14 proteins encoded by chromosome-4 and chromosome-9 that have an outstanding T-encoded and C-controlled hydrophobic character. The identification of these proteins could open new pivotal drug-targeting avenues.'),
(77983, 2014, 'bio', 'MFCompress: a compression tool for FASTA and multi-FASTA data', 'Pinho, Armando J|Pratas, Diogo', 'IEETA, Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal.|IEETA, Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal.', 'Portugal|Portugal', 39, 'Algorithms|Data Compression|Genome|Genomics|Humans|Markov Chains|Software', 'Journal Article', '10.1093/bioinformatics/btt594', '24132931', 'The data deluge phenomenon is becoming a serious problem in most genomic centers. To alleviate it, general purpose tools, such as gzip, are used to compress the data. However, although pervasive and easy to use, these tools fall short when the intention is to reduce as much as possible the data, for example, for medium- and long-term storage. A number of algorithms have been proposed for the compression of genomics data, but unfortunately only a few of them have been made available as usable and reliable compression tools.'),
(77984, 2013, 'bio', 'Are graph databases ready for bioinformatics?', 'Have, Christian Theil|Jensen, Lars Juhl', 'Department of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, 2100 Copenhagen Ø, Denmark and Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2100 Copenhagen N, Denmark.|Department of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, 2100 Copenhagen Ø, Denmark and Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2100 Copenhagen N, Denmark.', 'Denmark|Denmark', 42, 'Computational Biology|Computer Graphics|Databases, Factual', 'Editorial', '10.1093/bioinformatics/btt549', '24135261', '-'),
(77985, 2014, 'bio', 'Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish', 'Stegmaier, Johannes|Shahid, Maryam|Takamiya, Masanari|Yang, Lixin|Rastegar, Sepand|Reischl, Markus|Strähle, Uwe|Mikut, Ralf', 'Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Karlsruhe, Germany, Institute for Toxicology and Genetics (ITG), Karlsruhe Institute of Technology, Karlsruhe, Germany and Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Animals|Animals, Genetically Modified|Fluorescent Dyes|Green Fluorescent Proteins|High-Throughput Screening Assays|Luminescent Proteins|Neurons|Reproducibility of Results|Spinal Cord|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btt600', '24135262', 'To reliably assess the effects of unknown chemicals on the development of fluorescently labeled sensory-, moto- and interneuron populations in the spinal cord of zebrafish, automated data analysis is essential.'),
(77986, 2014, 'bio', 'Discretized Gaussian mixture for genotyping of microsatellite loci containing homopolymer runs', 'Tae, Hongseok|Kim, Dong-Yun|McCormick, John|Settlage, Robert E|Garner, Harold R', 'Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 and Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 and Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 and Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 and Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 and Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States|United States', 11, 'Alleles|Animals|Drosophila|Genetic Loci|Genotype|Genotyping Techniques|Humans|Microsatellite Repeats|Normal Distribution|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt595', '24135263', 'Inferring lengths of inherited microsatellite alleles with single base pair resolution from short sequence reads is challenging due to several sources of noise caused by the repetitive nature of microsatellites and the technologies used to generate raw sequence data.'),
(77987, 2014, 'bio', 'Frequent glycan structure mining of influenza virus data revealed a sulfated glycan motif that increased viral infection', 'Ichimiya, Tomomi|Nishihara, Shoko|Takase-Yoden, Sayaka|Kida, Hiroshi|Aoki-Kinoshita, Kiyoko', 'Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Laboratory of Microbiology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818 and Research Center for Zoonosis Control, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.|Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Laboratory of Microbiology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818 and Research Center for Zoonosis Control, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.|Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Laboratory of Microbiology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818 and Research Center for Zoonosis Control, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.|Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Laboratory of Microbiology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818 and Research Center for Zoonosis Control, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.|Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Laboratory of Microbiology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818 and Research Center for Zoonosis Control, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.', 'Japan|Japan|Japan|Japan|Japan', 9, 'Animals|Data Mining|Dogs|Influenza A Virus, H1N1 Subtype|Madin Darby Canine Kidney Cells|Orthomyxoviridae|Polysaccharides|Software|Sulfotransferases', 'Journal Article', '10.1093/bioinformatics/btt573', '24135264', 'It is well known influenza viruses recognize and bind terminal sialic acid (SA) on glycans that are found on the cell surface. In this work, we used a data mining technique to analyze the glycan array data of influenza viruses to find novel glycan structures other than SA that may be involved in viral infection.'),
(77988, 2014, 'bio', 'A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information', 'Li, Yue|Goldenberg, Anna|Wong, Ka-Chun|Zhang, Zhaolei', 'Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario M5G 1L7 and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario M5G 1L7 and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario M5G 1L7 and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario M5G 1L7 and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.', 'Canada|Canada|Canada|Canada', 19, 'Bayes Theorem|Down-Regulation|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|Humans|MicroRNAs|Oncogenes|Probability|Proteins|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btt599', '24135265', 'Systematic identification of microRNA (miRNA) targets remains a challenge. The miRNA overexpression coupled with genome-wide expression profiling is a promising new approach and calls for a new method that integrates expression and sequence information.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77989, 2014, 'bio', 'PEAR: a fast and accurate Illumina Paired-End reAd mergeR', 'Zhang, Jiajie|Kobert, Kassian|Flouri, Tomáš|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Graduate School for Computing in Medicine and Life Sciences, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Graduate School for Computing in Medicine and Life Sciences, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Graduate School for Computing in Medicine and Life Sciences, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Graduate School for Computing in Medicine and Life Sciences, Institut für Neuro- und Bioinformatik, University of Lübeck, 23538 Lübeck and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany.', 'Germany|Germany|Germany|Germany', 1572, 'Genomics|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btt593', '24142950', 'The Illumina paired-end sequencing technology can generate reads from both ends of target DNA fragments, which can subsequently be merged to increase the overall read length. There already exist tools for merging these paired-end reads when the target fragments are equally long. However, when fragment lengths vary and, in particular, when either the fragment size is shorter than a single-end read, or longer than twice the size of a single-end read, most state-of-the-art mergers fail to generate reliable results. Therefore, a robust tool is needed to merge paired-end reads that exhibit varying overlap lengths because of varying target fragment lengths.'),
(77990, 2014, 'bio', 'Cascleave 20, a new approach for predicting caspase and granzyme cleavage targets', 'Wang, Mingjun|Zhao, Xing-Ming|Tan, Hao|Akutsu, Tatsuya|Whisstock, James C|Song, Jiangning', 'National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 46, 'Amino Acid Sequence|Caspases|Granzymes|Humans|Probability|Proteome|Software Design|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btt603', '24149049', 'Caspases and granzyme B (GrB) are important proteases involved in fundamental cellular processes and play essential roles in programmed cell death, necrosis and inflammation. Although a number of substrates for both types have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remained to be fully characterized. Accordingly, systematic bioinformatics studies of known cleavage sites may provide important insights into their substrate specificity and facilitate the discovery of novel substrates.'),
(77991, 2014, 'bio', 'ATHENA: the analysis tool for heritable and environmental network associations', 'Holzinger, Emily R|Dudek, Scott M|Frase, Alex T|Pendergrass, Sarah A|Ritchie, Marylyn D', 'Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA and Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, PA, USA.|Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA and Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, PA, USA.|Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA and Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, PA, USA.|Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA and Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, PA, USA.|Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA and Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, PA, USA.', 'United States|United States|United States|United States|United States', 23, 'Gene-Environment Interaction|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt572', '24149050', 'Advancements in high-throughput technology have allowed researchers to examine the genetic etiology of complex human traits in a robust fashion. Although genome-wide association studies have identified many novel variants associated with hundreds of traits, a large proportion of the estimated trait heritability remains unexplained. One hypothesis is that the commonly used statistical techniques and study designs are not robust to the complex etiology that may underlie these human traits. This etiology could include non-linear gene × gene or gene × environment interactions. Additionally, other levels of biological regulation may play a large role in trait variability.'),
(77992, 2014, 'bio', 'Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems', 'Geraci, Joseph|Dharsee, Moyez|Nuin, Paulo|Haslehurst, Alexandria|Koti, Madhuri|Feilotter, Harriet E|Evans, Ken', 'Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.|Department of Psychiatry, University Health Network, Toronto, Department of Pathology and Molecular Medicine, Queens University, Kingston, Ontario Cancer Biomarker Network, Toronto and Department of Biomedical and Molecular Sciences, Queens University, Kingston, Ontario, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 4, 'Algorithms|Artificial Intelligence|Classification|Cluster Analysis|Computer Graphics|Female|Gene Expression Profiling|Humans|Lung Neoplasms|Models, Theoretical|Ovarian Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btt602', '24149051', 'We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here.'),
(77993, 2014, 'bio', 'BEReX: Biomedical Entity-Relationship eXplorer', 'Jeon, Minji|Lee, Sunwon|Lee, Kyubum|Tan, Aik-Choon|Kang, Jaewoo', 'Interdisciplinary Graduate Program in Bioinformatics, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Interdisciplinary Graduate Program in Bioinformatics, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Interdisciplinary Graduate Program in Bioinformatics, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Interdisciplinary Graduate Program in Bioinformatics, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Interdisciplinary Graduate Program in Bioinformatics, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.', 'United States|United States|United States|United States|United States', 6, 'Algorithms|Biomedical Technology|Computational Biology|Databases, Factual|Humans|Neural Networks (Computer)|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btt598', '24149052', 'Biomedical Entity-Relationship eXplorer (BEReX) is a new biomedical knowledge integration, search and exploration tool. BEReX integrates eight popular databases (STRING, DrugBank, KEGG, PhamGKB, BioGRID, GO, HPRD and MSigDB) and delineates an integrated network by combining the information available from these databases. Users search the integrated network by entering key words, and BEReX returns a sub-network matching the key words. The resulting graph can be explored interactively. BEReX allows users to find the shortest paths between two remote nodes, find the most relevant drugs, diseases, pathways and so on related to the current network, expand the network by particular types of entities and relations and modify the network by removing or adding selected nodes. BEReX is implemented as a standalone Java application.'),
(77994, 2014, 'bio', 'Inferring the soybean (Glycine max) microRNA functional network based on target gene network', 'Xu, Yungang|Guo, Maozu|Liu, Xiaoyan|Wang, Chunyu|Liu, Yang', 'School of Computer Science and Technology, and School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China.|School of Computer Science and Technology, and School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China.|School of Computer Science and Technology, and School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China.|School of Computer Science and Technology, and School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China.|School of Computer Science and Technology, and School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China.', 'China|China|China|China|China', 0, 'Cluster Analysis|Gene Regulatory Networks|MicroRNAs|Nucleic Acid Conformation|RNA, Plant|Soybeans|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt605', '24149053', 'The rapid accumulation of microRNAs (miRNAs) and experimental evidence for miRNA interactions has ushered in a new area of miRNA research that focuses on network more than individual miRNA interaction, which provides a systematic view of the whole microRNome. So it is a challenge to infer miRNA functional interactions on a system-wide level and further draw a miRNA functional network (miRFN). A few studies have focused on the well-studied human species, however, these methods can neither be extended to other non-model organisms nor take fully into account the information embedded in miRNA-target and target-target interactions. Thus, it is important to develop appropriate methods for inferring the miRNA network of non-model species, such as soybean (Glycine max), without such extensive miRNA-phenotype associated data as miRNA-disease associations in human.'),
(77995, 2014, 'bio', 'SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop', 'Schumacher, André|Pireddu, Luca|Niemenmaa, Matti|Kallio, Aleksi|Korpelainen, Eija|Zanetti, Gianluigi|Heljanko, Keijo', 'Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.|Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'High-Throughput Screening Assays|Software Design', 'Journal Article', '10.1093/bioinformatics/btt601', '24149054', 'Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPigs scalability over many computing nodes and illustrate its use with example scripts.'),
(77996, 2014, 'bio', 'Fast pairwise IBD association testing in genome-wide association studies', 'Han, Buhm|Kang, Eun Yong|Raychaudhuri, Soumya|de Bakker, Paul I W|Eskin, Eleazar', 'Division of Genetics, Brigham and Womens Hospital and Division of Rheumatology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Computer Science Department, University of California, Los Angeles, CA 90095, USA, Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Division of Genetics, Brigham and Womens Hospital and Division of Rheumatology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Computer Science Department, University of California, Los Angeles, CA 90095, USA, Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Division of Genetics, Brigham and Womens Hospital and Division of Rheumatology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Computer Science Department, University of California, Los Angeles, CA 90095, USA, Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Division of Genetics, Brigham and Womens Hospital and Division of Rheumatology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Computer Science Department, University of California, Los Angeles, CA 90095, USA, Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Division of Genetics, Brigham and Womens Hospital and Division of Rheumatology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Computer Science Department, University of California, Los Angeles, CA 90095, USA, Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Algorithms|Case-Control Studies|Chromosome Mapping|Computer Simulation|Diabetes Mellitus, Type 1|Genome, Human|Genome-Wide Association Study|HLA Antigens|Humans|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable', 'Journal Article', '10.1093/bioinformatics/btt609', '24158599', 'Recently, investigators have proposed state-of-the-art Identity-by-descent (IBD) mapping methods to detect IBD segments between purportedly unrelated individuals. The IBD information can then be used for association testing in genetic association studies. One approach for this IBD association testing strategy is to test for excessive IBD between pairs of cases (pairwise method). However, this approach is inefficient because it requires a large number of permutations. Moreover, a limited number of permutations define a lower bound for P-values, which makes fine-mapping of associated regions difficult because, in practice, a much larger genomic region is implicated than the region that is actually associated.'),
(77997, 2014, 'bio', 'Mouse model phenotypes provide information about human drug targets', 'Hoehndorf, Robert|Hiebert, Tanya|Hardy, Nigel W|Schofield, Paul N|Gkoutos, Georgios V|Dumontier, Michel', 'Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.|Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.|Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.|Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.|Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.|Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Animals|Cyclooxygenase 2 Inhibitors|Diclofenac|Drug Repositioning|Humans|Mice|Mice, Knockout|Models, Animal|Phenotype|Proteins', 'Journal Article', '10.1093/bioinformatics/btt613', '24158600', 'Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype.'),
(77998, 2014, 'bio', 'Inferring therapeutic targets from heterogeneous data: HKDC1 is a novel potential therapeutic target for cancer', 'Li, Gong-Hua|Huang, Jing-Fei', 'State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences and Kunming Institute of Zoology, Chinese University of Hongkong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming, Yunnan 650223, China.|State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences and Kunming Institute of Zoology, Chinese University of Hongkong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming, Yunnan 650223, China.', 'China|China', 21, 'Antineoplastic Agents|Clinical Trials as Topic|Computational Biology|Gene Expression Regulation, Neoplastic|Hexokinase|Humans|Lung Neoplasms', 'Journal Article', '10.1093/bioinformatics/btt606', '24162464', 'The discovery of therapeutic targets is important for cancer treatment. Although dozens of targets have been used in cancer therapies, cancer remains a serious disease with a high mortality rate. Owing to the expansion of cancer-related data, we now have the opportunity to infer therapeutic targets using computational biology methods.'),
(77999, 2014, 'bio', 'Protter: interactive protein feature visualization and integration with experimental proteomic data', 'Omasits, Ulrich|Ahrens, Christian H|Müller, Sebastian|Wollscheid, Bernd', 'Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich and Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland.|Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich and Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland.|Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich and Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland.|Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich and Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 438, 'Amino Acid Sequence|Internet|Molecular Sequence Annotation|Molecular Sequence Data|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btt607', '24162465', 'The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics.'),
(78000, 2014, 'bio', 'A pathway-based data integration framework for prediction of disease progression', 'Seoane, José A|Day, Ian N M|Gaunt, Tom R|Campbell, Colin', 'MRC Centre for Causal Analyses in Translational Epidemiology, MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton BS8 2BN, UK and Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK.|MRC Centre for Causal Analyses in Translational Epidemiology, MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton BS8 2BN, UK and Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK.|MRC Centre for Causal Analyses in Translational Epidemiology, MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton BS8 2BN, UK and Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK.|MRC Centre for Causal Analyses in Translational Epidemiology, MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton BS8 2BN, UK and Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Breast Neoplasms|Disease Progression|Gene Expression Regulation|Genomics|Humans|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btt610', '24162466', 'Within medical research there is an increasing trend toward deriving multiple types of data from the same individual. The most effective prognostic prediction methods should use all available data, as this maximizes the amount of information used. In this article, we consider a variety of learning strategies to boost prediction performance based on the use of all available data.'),
(78001, 2014, 'bio', 'Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data', 'Garcia, Tanya P|Müller, Samuel|Carroll, Raymond J|Walzem, Rosemary L', 'Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA.|Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA.|Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA.|Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA.', 'Australia|Australia|Australia|Australia', 22, 'Animals|Gastrointestinal Tract|Humans|Mice|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btt608', '24162467', 'Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple.'),
(78002, 2014, 'bio', 'Anatomical entity mention recognition at literature scale', 'Pyysalo, Sampo|Ananiadou, Sophia', 'National Centre for Text Mining and University of Manchester, Manchester, UK.|National Centre for Text Mining and University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom', 25, 'Algorithms|Anatomy|Animals|Artificial Intelligence|Databases, Factual|Humans|Publications|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/btt580', '24162468', 'Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced.'),
(78003, 2014, 'bio', 'Discriminative motif analysis of high-throughput dataset', 'Yao, Zizhen|Macquarrie, Kyle L|Fong, Abraham P|Tapscott, Stephen J|Ruzzo, Walter L|Gentleman, Robert C', 'Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.|Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.|Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.|Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.|Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.|Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, 98105, USA, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, Washington, 98105, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, Washington, 98105, USA and Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.', 'United States|United States|United States|United States|United States|United States', 27, 'Algorithms|Base Sequence|Chromatin Immunoprecipitation|DNA|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt615', '24162561', 'High-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naïve background distribution but are of questionable biological relevance.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78004, 2014, 'bio', 'Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure', 'Milreu, Paulo Vieira|Klein, Cecilia Coimbra|Cottret, Ludovic|Acuña, Vicente|Birmelé, Etienne|Borassi, Michele|Junot, Christophe|Marchetti-Spaccamela, Alberto|Marino, Andrea|Stougie, Leen|Jourdan, Fabien|Crescenzi, Pierluigi|Lacroix, Vincent|Sagot, Marie-France', 'INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.|INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université dÉvry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire dEtude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 10, 'Cadmium|Enzyme Activation|Glutathione|Metabolomics|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btt597', '24167155', 'The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected.'),
(78005, 2014, 'bio', 'AllergenFP: allergenicity prediction by descriptor fingerprints', 'Dimitrov, Ivan|Naneva, Lyudmila|Doytchinova, Irini|Bangov, Ivan', 'Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria.|Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria.|Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria.|Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria.', 'Bulgaria|Bulgaria|Bulgaria|Bulgaria', 85, 'Algorithms|Allergens|Amino Acid Sequence|Dermatoglyphics|Proteins', 'Journal Article', '10.1093/bioinformatics/btt619', '24167156', 'Allergenicity, like antigenicity and immunogenicity, is a property encoded linearly and non-linearly, and therefore the alignment-based approaches are not able to identify this property unambiguously. A novel alignment-free descriptor-based fingerprint approach is presented here and applied to identify allergens and non-allergens. The approach was implemented into a four step algorithm. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and xcexb2-strand forming propensities. Then, the generated strings of different length are converted into vectors with equal length by auto- and cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of Tanimoto coefficient.'),
(78006, 2014, 'bio', 'Fast protein fragment similarity scoring using a Binet-Cauchy kernel', 'Guyon, Frédéric|Tufféry, Pierre', 'Univ Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques in Silico, UMR 973, F-75205 Paris, France, INSERM, U973, F-75205 Paris, France and Univ Paris Diderot, Ressources Parisiennes de Bioinformatique Structurale, F-75205 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques in Silico, UMR 973, F-75205 Paris, France, INSERM, U973, F-75205 Paris, France and Univ Paris Diderot, Ressources Parisiennes de Bioinformatique Structurale, F-75205 Paris, France.', 'France|France', 13, 'Algorithms|Models, Molecular|Peptide Fragments|Probability|Protein Structure, Tertiary|Proteins|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btt618', '24167157', 'Meaningful scores to assess protein structure similarity are essential to decipher protein structure and sequence evolution. The mining of the increasing number of protein structures requires fast and accurate similarity measures with statistical significance. Whereas numerous approaches have been proposed for protein domains as a whole, the focus is progressively moving to a more local level of structure analysis for which similarity measurement still remains without any satisfactory answer.'),
(78007, 2014, 'bio', 'Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway', 'Yang, Pengyi|Patrick, Ellis|Tan, Shi-Xiong|Fazakerley, Daniel J|Burchfield, James|Gribben, Christopher|Prior, Matthew J|James, David E|Hwa Yang, Yee', 'Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.|Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 15, 'Adipocytes|Biological Transport|Cell Membrane|Insulin|Phosphatidylinositol 3-Kinases|Proteome|Proteomics|Proto-Oncogene Proteins c-akt|Software', 'Journal Article', '10.1093/bioinformatics/btt616', '24167158', 'With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations.'),
(78008, 2014, 'bio', 'snoStrip: a snoRNA annotation pipeline', 'Bartschat, Sebastian|Kehr, Stephanie|Tafer, Hakim|Stadler, Peter F|Hertel, Jana', 'Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.', 'Germany|Germany|Germany|Germany|Germany', 18, 'Base Sequence|Conserved Sequence|High-Throughput Nucleotide Sequencing|RNA, Small Nucleolar|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt604', '24174566', 'Although small nucleolar RNAs form an important class of non-coding RNAs, no comprehensive annotation efforts have been undertaken, presumably because the task is complicated by both the large number of distinct small nucleolar RNA families and their relatively rapid pace of sequence evolution.'),
(78009, 2014, 'bio', 'MSPrep--summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data', 'Hughes, Grant|Cruickshank-Quinn, Charmion|Reisdorph, Richard|Lutz, Sharon|Petrache, Irina|Reisdorph, Nichole|Bowler, Russell|Kechris, Katerina', 'Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.', 'India|India|India|India|India|India|India|India', 29, 'Algorithms|Databases, Factual|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btt589', '24174567', 'Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses.'),
(78010, 2014, 'bio', 'NCS: incorporating positioning data to quantify nucleosome stability in yeast', 'Chiang, Jung-Hsien|Lin, Chan-Hsien', 'Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.', '-|-', 0, 'DNA, Fungal|High-Throughput Nucleotide Sequencing|Nucleosomes|Poly A|Poly T|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btt621', '24177715', 'With the spreading technique of mass sequencing, nucleosome positions and scores for their intensity have become available through several previous studies in yeast, but relatively few studies have specifically aimed to determine the score of nucleosome stability. Based on mass sequencing data, we proposed a nucleosome center score (NCS) for quantifying nucleosome stability by measuring shifts of the nucleosome center, and then mapping NCS scores to nucleosome positions in Brogaard et al.s study.'),
(78011, 2014, 'bio', 'pyGCluster, a novel hierarchical clustering approach', 'Jaeger, Daniel|Barth, Johannes|Niehues, Anna|Fufezan, Christian', 'Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany.|Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany.|Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany.|Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Cluster Analysis|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btt626', '24177716', 'pyGCluster is a clustering algorithm focusing on noise injection for subsequent cluster validation. The reproducibility of a large amount of clusters obtained with agglomerative hierarchical clustering is assessed. Furthermore, a multitude of different distance-linkage combinations are evaluated. Finally, highly reproducible clusters are meta-clustered into communities. Graphical illustration of the results as node and expression maps is implemented.'),
(78012, 2014, 'bio', 'Prediction and early diagnosis of complex diseases by edge-network', 'Yu, Xiangtian|Li, Guojun|Chen, Luonan', 'Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, School of Mathematics, Shandong University, Jinan 250100, China and Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, School of Mathematics, Shandong University, Jinan 250100, China and Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, School of Mathematics, Shandong University, Jinan 250100, China and Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.', 'China|China|China', 0, 'Algorithms|Biomarkers|Disease Progression|Early Diagnosis|Gene Expression Regulation|Humans|Influenza, Human', 'Journal Article', '10.1093/bioinformatics/btt620', '24177717', 'In this article, we develop a novel edge-based network i.e. edge-network, to detect early signals of diseases by identifying the corresponding edge-biomarkers with their dynamical network biomarker score from dynamical network biomarkers. Specifically, we derive an edge-network based on the second-order statistics representation of gene expression profiles, which is able to accurately represent the stochastic dynamics of the original biological system (with Gaussian distribution assumption) by combining with the traditional node-network, which is based only on the first-order statistics representation of the noisy data. In other words, we show that the stochastic network of a biological system can be described by the integration of its node-network and its edge-network in an accurate manner.'),
(78013, 2014, 'bio', 'EXPANDS: expanding ploidy and allele frequency on nested subpopulations', 'Andor, Noemi|Harness, Julie V|Müller, Sabine|Mewes, Hans W|Petritsch, Claudia', 'Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Brain Tumor Research Center, University of California San Francisco, San Francisco, CA 94158, USA, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA, Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158 and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Brain Tumor Research Center, University of California San Francisco, San Francisco, CA 94158, USA, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA, Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158 and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Brain Tumor Research Center, University of California San Francisco, San Francisco, CA 94158, USA, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA, Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158 and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Brain Tumor Research Center, University of California San Francisco, San Francisco, CA 94158, USA, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA, Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158 and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.|Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Brain Tumor Research Center, University of California San Francisco, San Francisco, CA 94158, USA, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA, Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158 and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.', 'Germany|Germany|Germany|Germany|Germany', 68, 'Gene Frequency|Glioblastoma|Humans|Mutation|Neoplasms|Ploidies|Probability|Recurrence', 'Journal Article', '10.1093/bioinformatics/btt622', '24177718', 'Several cancer types consist of multiple genetically and phenotypically distinct subpopulations. The underlying mechanism for this intra-tumoral heterogeneity can be explained by the clonal evolution model, whereby growth advantageous mutations cause the expansion of cancer cell subclones. The recurrent phenotype of many cancers may be a consequence of these coexisting subpopulations responding unequally to therapies. Methods to computationally infer tumor evolution and subpopulation diversity are emerging and they hold the promise to improve the understanding of genetic and molecular determinants of recurrence.'),
(78014, 2014, 'bio', 'The functional therapeutic chemical classification system', 'Croset, Samuel|Overington, John P|Rebholz-Schuhmann, Dietrich', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Alzheimer Disease|Drug Repositioning|Humans|Internet|Pharmaceutical Preparations|Polypharmacology', 'Journal Article', '10.1093/bioinformatics/btt628', '24177719', 'Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds.'),
(78015, 2014, 'bio', 'MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG', 'Posma, Joram M|Robinette, Steven L|Holmes, Elaine|Nicholson, Jeremy K', 'Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 44, 'Genome|Internet|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btt612', '24177720', 'MetaboNetworks is a tool to create custom sub-networks in Matlab using main reaction pairs as defined by the Kyoto Encyclopaedia of Genes and Genomes and can be used to explore transgenomic interactions, for example mammalian and bacterial associations. It calculates the shortest path between a set of metabolites (e.g. biomarkers from a metabonomic study) and plots the connectivity between metabolites as links in a network graph. The resulting graph can be edited and explored interactively. Furthermore, nodes and edges in the graph are linked to the Kyoto Encyclopaedia of Genes and Genomes compound and reaction pair web pages.'),
(78016, 2014, 'bio', 'AnchorMS: a bioinformatics tool to derive structural information from the mass spectra of cross-linked protein complexes', 'Mayne, Shannon L N|Patterton, Hugh-G', 'Advanced Biomolecular Research Cluster, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa.|Advanced Biomolecular Research Cluster, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa.', 'South Africa|South Africa', 2, 'Computational Biology|False Positive Reactions|Humans|Mass Spectrometry|Peptides|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt617', '24177721', 'Mass spectrometry is being increasingly used in the structural elucidation of mega-Dalton protein complexes in an approach termed MS3D, referring to the application of MS to the study of macromolecular structures. This involves the identification of cross-linked residues in the constituent proteins of chemically cross-linked multi-subunit complexes. AnchorMS was developed to simplify MS3D studies by identifying cross-linked peptides in complex peptide mixtures, and to determine the specific residues involved in each cross-link. When identifying cross-linked peptide pairs (CLPP), AnchorMS implements a mathematical model to exclude false positives by using a dynamic score threshold to estimate the number of false-positive peak matches expected in an MS/MS spectrum. This model was derived from CLPPs with randomly generated sequences. AnchorMS does not require specific sample labeling or pre-treatment, and AnchorMS is especially suited for discriminating between CLPPs that differ only in the cross-linked residue pairs.'),
(78017, 2014, 'bio', 'Model-based clustering for RNA-seq data', 'Si, Yaqing|Liu, Peng|Li, Pinghua|Brutnell, Thomas P', 'School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China, Department of Statistics, Iowa State University, Ames, IA 50011, USA, Institute of Tropical Biosciences and Biotechnology (ITBB), Chinese Academy of Tropical Agriculture Sciences (CATAS), Haikou, Hainan 571101, China and Enterprise Institute for Renewable Fuels, Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China, Department of Statistics, Iowa State University, Ames, IA 50011, USA, Institute of Tropical Biosciences and Biotechnology (ITBB), Chinese Academy of Tropical Agriculture Sciences (CATAS), Haikou, Hainan 571101, China and Enterprise Institute for Renewable Fuels, Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China, Department of Statistics, Iowa State University, Ames, IA 50011, USA, Institute of Tropical Biosciences and Biotechnology (ITBB), Chinese Academy of Tropical Agriculture Sciences (CATAS), Haikou, Hainan 571101, China and Enterprise Institute for Renewable Fuels, Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China, Department of Statistics, Iowa State University, Ames, IA 50011, USA, Institute of Tropical Biosciences and Biotechnology (ITBB), Chinese Academy of Tropical Agriculture Sciences (CATAS), Haikou, Hainan 571101, China and Enterprise Institute for Renewable Fuels, Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Models, Statistical|Plant Leaves|Zea mays', 'Journal Article', '10.1093/bioinformatics/btt632', '24191069', 'RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis.'),
(78018, 2014, 'bio', 'StochDecomp--Matlab package for noise decomposition in stochastic biochemical systems', 'Jetka, Tomasz|Charzyxc5x84ska, Agata|Gambin, Anna|Stumpf, Michael P H|Komorowski, Michaxc5x82', 'Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.|Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.|Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.|Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.|Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.', 'Poland|Poland|Poland|Poland|Poland', 3, 'Bayes Theorem|Biochemical Phenomena|Janus Kinases|STAT Transcription Factors|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btt631', '24191070', 'Stochasticity is an indispensable aspect of biochemical processes at the cellular level. Studies on how the noise enters and propagates in biochemical systems provided us with non-trivial insights into the origins of stochasticity, in total, however, they constitute a patchwork of different theoretical analyses.'),
(78019, 2014, 'bio', 'Phylowood: interactive web-based animations of biogeographic and phylogeographic histories', 'Landis, Michael J|Bedford, Trevor', 'Department of Integrative Biology, UC Berkeley, Berkeley, CA 94720, USA, Institute of Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Integrative Biology, UC Berkeley, Berkeley, CA 94720, USA, Institute of Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.', 'United States|United States', 0, 'Animals|Internet|Phylogeny|Phylogeography|Software', 'Journal Article', '10.1093/bioinformatics/btt635', '24191071', 'Phylowood is a web service that uses JavaScript to generate in-browser animations of biogeographic and phylogeographic histories from annotated phylogenetic input. The animations are interactive, allowing the user to adjust spatial and temporal resolution, and highlight phylogenetic lineages of interest.'),
(78020, 2014, 'bio', 'Integrating multiple resources to identify specific transcriptional cooperativity with a Bayesian approach', 'Hu, Pengzhan|Shen, Zhongchao|Tu, Haibo|Zhang, Li|Shi, Tieliu', 'Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.|Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.|Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.|Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.|Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.', 'China|China|China|China|China', 5, 'Bayes Theorem|Cell Transformation, Neoplastic|Gene Expression|Gene Regulatory Networks|Genome, Human|Genomics|Hep G2 Cells|Humans|Liver Neoplasms|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btt596', '24192543', 'Limited cohort of transcription factors is capable to structure various gene-expression patterns. Transcriptional cooperativity (TC) is deemed to be the main mechanism of complexity and precision in regulatory programs. Although many data types generated from numerous experimental technologies are utilized in an attempt to understand combinational transcriptional regulation, complementary computational approach that can integrate diverse data resources and assimilate them into biological model is still under development.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78021, 2014, 'bio', 'WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing', 'Holt, Carson|Losic, Bojan|Pai, Deepa|Zhao, Zhen|Trinh, Quang|Syam, Sujata|Arshadi, Niloofar|Jang, Gun Ho|Ali, Johar|Beck, Tim|McPherson, John|Muthuswamy, Lakshmi B', 'Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.|Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Alleles|Aneuploidy|Animals|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Humans|Mice|Neoplasms|Sequence Analysis, DNA|Software|Xenograft Model Antitumor Assays', 'Journal Article', '10.1093/bioinformatics/btt611', '24192544', 'Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.'),
(78022, 2014, 'bio', 'Testing multiple biological mediators simultaneously', 'Boca, Simina M|Sinha, Rashmi|Cross, Amanda J|Moore, Steven C|Sampson, Joshua N', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.', 'United States|United States|United States|United States|United States', 19, 'Adenoma|Algorithms|Animals|Biomarkers, Tumor|Case-Control Studies|Colorectal Neoplasms|Computer Simulation|Diet|Docosahexaenoic Acids|Fishes|Humans|Meat|Risk Factors', 'Journal Article', '10.1093/bioinformatics/btt633', '24202540', 'Modern biomedical and epidemiological studies often measure hundreds or thousands of biomarkers, such as gene expression or metabolite levels. Although there is an extensive statistical literature on adjusting for multiple comparisons when testing whether these biomarkers are directly associated with a disease, testing whether they are biological mediators between a known risk factor and a disease requires a more complex null hypothesis, thus offering additional methodological challenges.'),
(78023, 2014, 'bio', 'Computing cavities, channels, pores and pockets in proteins from non-spherical ligands models', 'Benkaidali, Lydia|André, François|Maouche, Boubekeur|Siregar, Pridi|Benyettou, Mohamed|Maurel, François|Petitjean, Michel', 'ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.|ITODYS, UMR 7086, CNRS, Université Denis Diderot, Paris 7, Programme Doctoral International, Université Pierre & Marie Curie, Paris 6, CEA/DSV/iBiTec-S/SB2SM, CNRS, UMR 8221, Saclay, France, LPCTCI, Université des Sciences et de la Technologie Houari Boumédiène, Algiers, Algeria, IBC, Integrative BioComputing SARL, Place du Granier, 35135 Rennes-Chantepie, France, LAMOSI, Université des Sciences et de la Technologie dOran Mohamed Boudiaf, Oran, Algeria and MTi, UMR-S 973, INSERM, University Denis Diderot, Paris 7, France.', 'Algeria|Algeria|Algeria|Algeria|Algeria|Algeria|Algeria', 23, 'Algorithms|Binding Sites|Cytochrome P-450 CYP1A1|Cytochrome P-450 CYP3A|Drug Design|Humans|Ligands|Models, Molecular|Porosity|Protein Interaction Domains and Motifs|Software', 'Journal Article', '10.1093/bioinformatics/btt644', '24202541', 'Identifying protein cavities, channels and pockets accessible to ligands is a major step to predict potential protein-ligands complexes. It is also essential for preparation of protein-ligand docking experiments in the context of enzymatic activity mechanism and structure-based drug design.'),
(78024, 2014, 'bio', 'PREDDIMER: a web server for prediction of transmembrane helical dimers', 'Polyansky, Anton A|Chugunov, Anton O|Volynsky, Pavel E|Krylov, Nikolay A|Nolde, Dmitry E|Efremov, Roman G', 'M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.|M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.|M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.|M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.|M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.|M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia, Max F. Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, Vienna AT-1030, Austria and Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russia.', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Algorithms|Internet|Membrane Proteins|Models, Molecular|Protein Multimerization|Protein Structure, Secondary|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btt645', '24202542', 'Here we present PREDDIMER, a web tool for prediction of dimer structure of transmembrane (TM) helices. PREDDIMER allows (i) reconstruction of a number of dimer structures for given sequence(s) of TM protein fragments, (ii) ranking and filtering of predicted structures according to respective values of a scoring function, (iii) visualization of predicted 3D dimer structures and (iv) visualization of surface hydrophobicity of TM helices and their contacting (interface) regions represented as 2D maps.'),
(78025, 2014, 'bio', 'Development of a domain-specific genetic language to design Chlamydomonas reinhardtii expression vectors', 'Wilson, Mandy L|Okumoto, Sakiko|Adam, Laura|Peccoud, Jean', 'Virginia Bioinformatics Institute, Department of Plant Pathology, Physiology, and Weed Science and ICTAS Center for Systems Biology of Engineered Tissues, MC 0193, Virginia Tech, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Department of Plant Pathology, Physiology, and Weed Science and ICTAS Center for Systems Biology of Engineered Tissues, MC 0193, Virginia Tech, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Department of Plant Pathology, Physiology, and Weed Science and ICTAS Center for Systems Biology of Engineered Tissues, MC 0193, Virginia Tech, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Department of Plant Pathology, Physiology, and Weed Science and ICTAS Center for Systems Biology of Engineered Tissues, MC 0193, Virginia Tech, Blacksburg, VA 24061, USA.', 'United States|United States|United States|United States', 4, '5 Untranslated Regions|Algorithms|Chlamydomonas reinhardtii|Chloroplasts|Gene Expression Regulation, Plant|Genes, Plant|Genetic Vectors|Genome, Plant|Operon|Recombination, Genetic|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btt646', '24215020', 'Expression vectors used in different biotechnology applications are designed with domain-specific rules. For instance, promoters, origins of replication or homologous recombination sites are host-specific. Similarly, chromosomal integration or viral delivery of an expression cassette imposes specific structural constraints. As de novo gene synthesis and synthetic biology methods permeate many biotechnology specialties, the design of application-specific expression vectors becomes the new norm. In this context, it is desirable to formalize vector design strategies applicable in different domains.'),
(78026, 2014, 'bio', 'A MATLAB-based tool for accurate detection of perfect overlapping and nested inverted repeats in DNA sequences', 'Sreeskandarajan, Sutharzan|Flowers, Michelle M|Karro, John E|Liang, Chun', 'Department of Biology, Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA and State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China.|Department of Biology, Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA and State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China.|Department of Biology, Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA and State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China.|Department of Biology, Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA and State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China.', 'China|China|China|China', 9, 'Algorithms|Genome|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt651', '24215021', 'Palindromic sequences, or inverted repeats (IRs), in DNA sequences involve important biological processes such as DNA-protein binding, DNA replication and DNA transposition. Development of bioinformatics tools that are capable of accurately detecting perfect IRs can enable genome-wide studies of IR patterns in both prokaryotes and eukaryotes. Different from conventional string-comparison approaches, we propose a novel algorithm that uses a cumulative score system based on a prime number representation of nucleotide bases. We then implemented this algorithm as a MATLAB-based program for perfect IR detection. In comparison with other existing tools, our program demonstrates a high accuracy in detecting nested and overlapping IRs.'),
(78027, 2014, 'bio', 'Rapid detection of expanded short tandem repeats in personal genomics using hybrid sequencing', 'Doi, Koichiro|Monjo, Taku|Hoang, Pham H|Yoshimura, Jun|Yurino, Hideaki|Mitsui, Jun|Ishiura, Hiroyuki|Takahashi, Yuji|Ichikawa, Yaeko|Goto, Jun|Tsuji, Shoji|Morishita, Shinichi', 'Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Department of Information and Communication Engineering, Faculty of Engineering and Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Base Sequence|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Microsatellite Repeats|Molecular Sequence Data|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btt647', '24215022', 'Long expansions of short tandem repeats (STRs), i.e. DNA repeats of 2-6 nt, are associated with some genetic diseases. Cost-efficient high-throughput sequencing can quickly produce billions of short reads that would be useful for uncovering disease-associated STRs. However, enumerating STRs in short reads remains largely unexplored because of the difficulty in elucidating STRs much longer than 100 bp, the typical length of short reads.'),
(78028, 2014, 'bio', 'RNAseqViewer: visualization tool for RNA-Seq data', 'Rogé, Xavier|Zhang, Xuegong', 'MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of Automation and School of Life Sciences, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of Automation and School of Life Sciences, Tsinghua University, Beijing 100084, China.', 'China|China', 6, 'Gene Expression Profiling|Genome, Human|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btt649', '24215023', 'With the advances of RNA sequencing technologies, scientists need new tools to analyze transcriptome data. We introduce RNAseqViewer, a new visualization tool dedicated to RNA-Seq data. The program offers innovative ways to represent transcriptome data for single or multiple samples. It is a handy tool for scientists who use RNA-Seq data to compare multiple transcriptomes, for example, to compare gene expression and alternative splicing of cancer samples or of different development stages.'),
(78029, 2014, 'bio', 'SBML and CellML translation in antimony and JSim', 'Smith, Lucian P|Butterworth, Erik|Bassingthwaighte, James B|Sauro, Herbert M', 'Department of Bioengineering, University of Washington, Seattle, WA, USA.|Department of Bioengineering, University of Washington, Seattle, WA, USA.|Department of Bioengineering, University of Washington, Seattle, WA, USA.|Department of Bioengineering, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States', 8, 'Cluster Analysis|Computer Simulation|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btt641', '24215024', 'The creation and exchange of biologically relevant models is of great interest to many researchers. When multiple standards are in use, models are more readily used and re-used if there exist robust translators between the various accepted formats.'),
(78030, 2014, 'bio', 'RelateAdmix: a software tool for estimating relatedness between admixed individuals', 'Moltke, Ida|Albrechtsen, Anders', 'Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA and Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.|Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA and Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.', 'Denmark|Denmark', 28, 'Likelihood Functions|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btt652', '24215025', 'Pairwise relatedness plays an important role in a range of genetic research fields. However, currently only few estimators exist for individuals that are admixed, i.e. have ancestry from more than one population, and these estimators fail in some situations.'),
(78031, 2014, 'bio', 'PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra', 'Kertész-Farkas, Attila|Reiz, Beáta|Vera, Roberto|Myers, Michael P|Pongor, Sándor', 'Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, 99 Padriciano, Trieste, Italy, 34149, Institute of Biophysics, Biological Research Centre, Temesvari krt. 62, H-6727 Szeged, Hungary, Protein Networks Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, Padriciano 99, 34149 Trieste, Italy and Faculty of Information Technology, Pázmány Péter Catholic University, Práter u. 50/a, H-1083 Budapest, Hungary.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, 99 Padriciano, Trieste, Italy, 34149, Institute of Biophysics, Biological Research Centre, Temesvari krt. 62, H-6727 Szeged, Hungary, Protein Networks Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, Padriciano 99, 34149 Trieste, Italy and Faculty of Information Technology, Pázmány Péter Catholic University, Práter u. 50/a, H-1083 Budapest, Hungary.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, 99 Padriciano, Trieste, Italy, 34149, Institute of Biophysics, Biological Research Centre, Temesvari krt. 62, H-6727 Szeged, Hungary, Protein Networks Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, Padriciano 99, 34149 Trieste, Italy and Faculty of Information Technology, Pázmány Péter Catholic University, Práter u. 50/a, H-1083 Budapest, Hungary.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, 99 Padriciano, Trieste, Italy, 34149, Institute of Biophysics, Biological Research Centre, Temesvari krt. 62, H-6727 Szeged, Hungary, Protein Networks Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, Padriciano 99, 34149 Trieste, Italy and Faculty of Information Technology, Pázmány Péter Catholic University, Práter u. 50/a, H-1083 Budapest, Hungary.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, 99 Padriciano, Trieste, Italy, 34149, Institute of Biophysics, Biological Research Centre, Temesvari krt. 62, H-6727 Szeged, Hungary, Protein Networks Group, International Centre for Genetic Engineering and Biotechnology, AREA Research Park, Padriciano 99, 34149 Trieste, Italy and Faculty of Information Technology, Pázmány Péter Catholic University, Práter u. 50/a, H-1083 Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary', 4, 'Algorithms|Computational Biology|Humans|Peptide Fragments|Protein Processing, Post-Translational|Proteins|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btt642', '24215026', 'Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times.'),
(78032, 2014, 'bio', 'Bayesian network prior: network analysis of biological data using external knowledge', 'Isci, Senol|Dogan, Haluk|Ozturk, Cengizhan|Otu, Hasan H', 'Bogazici University, Institute of Biomedical Engineering, Kandilli Campus, 34684, Cengelkoy - Istanbul, TUBITAK-BILGEM, Informatics and Information Security Research Center, 41470, Gebze-Kocaeli and Istanbul Bilgi University, Department of Genetics and Bioengineering, 34060, Eyup - Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, Kandilli Campus, 34684, Cengelkoy - Istanbul, TUBITAK-BILGEM, Informatics and Information Security Research Center, 41470, Gebze-Kocaeli and Istanbul Bilgi University, Department of Genetics and Bioengineering, 34060, Eyup - Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, Kandilli Campus, 34684, Cengelkoy - Istanbul, TUBITAK-BILGEM, Informatics and Information Security Research Center, 41470, Gebze-Kocaeli and Istanbul Bilgi University, Department of Genetics and Bioengineering, 34060, Eyup - Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, Kandilli Campus, 34684, Cengelkoy - Istanbul, TUBITAK-BILGEM, Informatics and Information Security Research Center, 41470, Gebze-Kocaeli and Istanbul Bilgi University, Department of Genetics and Bioengineering, 34060, Eyup - Istanbul, Turkey.', 'Turkey|Turkey|Turkey|Turkey', 26, 'Bayes Theorem|Carcinoma, Renal Cell|Gene Expression|Gene Regulatory Networks|Genomics|Humans|Kidney Neoplasms|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btt643', '24215027', 'Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event gene interaction and is used to calculate the probability of a candidate graph (G) in the structure learning process.'),
(78033, 2014, 'bio', 'AVIA: an interactive web-server for annotation, visualization and impact analysis of genomic variations', 'Vuong, Hue|Stephens, Robert M|Volfovsky, Natalia', 'Advanced Biomedical Computing Center (ABCC), Information Systems Program, SAIC-Frederick Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center (ABCC), Information Systems Program, SAIC-Frederick Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.|Advanced Biomedical Computing Center (ABCC), Information Systems Program, SAIC-Frederick Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.', 'United States|United States|United States', 3, 'Gene Deletion|Genome|Genomics|Internet|Mutagenesis, Insertional|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btt655', '24215028', 'The plethora of information that emerges from large-scale genome characterization studies has triggered the development of computational frameworks and tools for efficient analysis, interpretation and visualization of genomic data. Functional annotation of genomic variations and the ability to visualize the data in the context of whole genome and/or multiple genomes has remained a challenging task. We have developed an interactive web-based tool, AVIA (Annotation, Visualization and Impact Analysis), to explore and interpret large sets of genomic variations (single nucleotide variations and insertion/deletions) and to help guide and summarize genomic experiments. The annotation, summary plots and tables are packaged and can be downloaded by the user from the email link provided.'),
(78034, 2014, 'bio', 'HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences', 'Matias Rodrigues, João F|von Mering, Christian', 'Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.|Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland', 26, 'Algorithms|Cluster Analysis|RNA, Bacterial|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt657', '24215029', 'Nucleotide sequence data are being produced at an ever increasing rate. Clustering such sequences by similarity is often an essential first step in their analysis-intended to reduce redundancy, define gene families or suggest taxonomic units. Exact clustering algorithms, such as hierarchical clustering, scale relatively poorly in terms of run time and memory usage, yet they are desirable because heuristic shortcuts taken during clustering might have unintended consequences in later analysis steps.'),
(78035, 2014, 'bio', 'Parallel content-based sub-image retrieval using hierarchical searching', 'Yang, Lin|Qi, Xin|Xing, Fuyong|Kurc, Tahsin|Saltz, Joel|Foran, David J', 'Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.|Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.|Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.|Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.|Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.|Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.', 'Jersey|Jersey|Jersey|Jersey|Jersey|Jersey', 11, 'Algorithms|Cluster Analysis|Color|Image Processing, Computer-Assisted', 'Journal Article', '10.1093/bioinformatics/btt623', '24215030', 'The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches.'),
(78036, 2014, 'bio', 'PSAR-align: improving multiple sequence alignment using probabilistic sampling', 'Kim, Jaebum|Ma, Jian', 'Department of Animal Biotechnology, UBITA Center for Biotechnology Research (CBRU), Konkuk University, Seoul 143-701, Korea, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Animal Biotechnology, UBITA Center for Biotechnology Research (CBRU), Konkuk University, Seoul 143-701, Korea, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States', 12, 'Algorithms|High-Throughput Nucleotide Sequencing|Probability|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btt636', '24222208', 'We developed PSAR-Align, a multiple sequence realignment tool that can refine a given multiple sequence alignment based on suboptimal alignments generated by probabilistic sampling. Our evaluation demonstrated that PSAR-Align is able to improve the results from various multiple sequence alignment tools.'),
(78037, 2014, 'bio', 'tRanslatome: an R/Bioconductor package to portray translational control', 'Tebaldi, Toma|Dassi, Erik|Kostoska, Galena|Viero, Gabriella|Quattrone, Alessandro', 'Laboratory of Translational Genomics - Centre for Integrative Biology, University of Trento, Via delle Regole 101, 38123 Mattarello (TN) and Institute of Biophysics CNR - Via alla Cascata 56/C, 38123 Povo (TN), Italy.|Laboratory of Translational Genomics - Centre for Integrative Biology, University of Trento, Via delle Regole 101, 38123 Mattarello (TN) and Institute of Biophysics CNR - Via alla Cascata 56/C, 38123 Povo (TN), Italy.|Laboratory of Translational Genomics - Centre for Integrative Biology, University of Trento, Via delle Regole 101, 38123 Mattarello (TN) and Institute of Biophysics CNR - Via alla Cascata 56/C, 38123 Povo (TN), Italy.|Laboratory of Translational Genomics - Centre for Integrative Biology, University of Trento, Via delle Regole 101, 38123 Mattarello (TN) and Institute of Biophysics CNR - Via alla Cascata 56/C, 38123 Povo (TN), Italy.|Laboratory of Translational Genomics - Centre for Integrative Biology, University of Trento, Via delle Regole 101, 38123 Mattarello (TN) and Institute of Biophysics CNR - Via alla Cascata 56/C, 38123 Povo (TN), Italy.', 'Italy|Italy|Italy|Italy|Italy', 12, 'Cell Differentiation|Computational Biology|Databases, Factual|Genomics|Hepatocytes|Humans|Protein Biosynthesis|Proteome|RNA, Messenger|Regulatory Sequences, Nucleic Acid|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btt634', '24222209', 'High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different -omics. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs, the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies.'),
(78038, 2014, 'bio', 'DoMosaics: software for domain arrangement visualization and domain-centric analysis of proteins', 'Moore, Andrew D|Held, Andreas|Terrapon, Nicolas|Weiner, January|Bornberg-Bauer, Erich', 'Institute for Evolution and Biodiversity, Hüfferstrasse 1, Westphalian Wilhelms-University Münster, 48147 Münster, Germany, and Max Planck Institute for Infection Biology, Chariteplatz 1, 10117 Berlin, Germany.|Institute for Evolution and Biodiversity, Hüfferstrasse 1, Westphalian Wilhelms-University Münster, 48147 Münster, Germany, and Max Planck Institute for Infection Biology, Chariteplatz 1, 10117 Berlin, Germany.|Institute for Evolution and Biodiversity, Hüfferstrasse 1, Westphalian Wilhelms-University Münster, 48147 Münster, Germany, and Max Planck Institute for Infection Biology, Chariteplatz 1, 10117 Berlin, Germany.|Institute for Evolution and Biodiversity, Hüfferstrasse 1, Westphalian Wilhelms-University Münster, 48147 Münster, Germany, and Max Planck Institute for Infection Biology, Chariteplatz 1, 10117 Berlin, Germany.|Institute for Evolution and Biodiversity, Hüfferstrasse 1, Westphalian Wilhelms-University Münster, 48147 Münster, Germany, and Max Planck Institute for Infection Biology, Chariteplatz 1, 10117 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 29, 'Amino Acyl-tRNA Synthetases|Animals|Computer Graphics|Drosophila melanogaster|Molecular Sequence Annotation|Phylogeny|Protein Interaction Domains and Motifs|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btt640', '24222210', 'DoMosaics is an application that unifies protein domain annotation, domain arrangement analysis and visualization in a single tool. It simplifies the analysis of protein families by consolidating disjunct procedures based on often inconvenient command-line applications and complex analysis tools. It provides a simple user interface with access to domain annotation services such as InterProScan or a local HMMER installation, and can be used to compare, analyze and visualize the evolution of domain architectures.'),
(78039, 2014, 'bio', 'MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments', 'Lienhard, Matthias|Grimm, Christina|Morkel, Markus|Herwig, Ralf|Chavez, Lukas', 'Departments of Vertebrate Genomics and Developmental Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Department of Rheumatology and Clinical Immunology, Institute of Pathology, Laboratory of Molecular Tumour Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany and Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|Departments of Vertebrate Genomics and Developmental Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Department of Rheumatology and Clinical Immunology, Institute of Pathology, Laboratory of Molecular Tumour Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany and Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|Departments of Vertebrate Genomics and Developmental Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Department of Rheumatology and Clinical Immunology, Institute of Pathology, Laboratory of Molecular Tumour Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany and Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|Departments of Vertebrate Genomics and Developmental Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Department of Rheumatology and Clinical Immunology, Institute of Pathology, Laboratory of Molecular Tumour Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany and Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|Departments of Vertebrate Genomics and Developmental Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany, Department of Rheumatology and Clinical Immunology, Institute of Pathology, Laboratory of Molecular Tumour Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany and Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.', 'Germany|Germany|Germany|Germany|Germany', 140, 'Adenoma|Animals|Chromatin Immunoprecipitation|CpG Islands|DNA Methylation|DNA-Binding Proteins|Databases, Factual|Epigenomics|Genome-Wide Association Study|Genomics|Intestinal Neoplasms|Mice|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btt650', '24227674', 'DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA.');
