INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76889, 2012, 'bio', 'Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data', 'Kearse, Matthew|Moir, Richard|Wilson, Amy|Stones-Havas, Steven|Cheung, Matthew|Sturrock, Shane|Buxton, Simon|Cooper, Alex|Markowitz, Sidney|Duran, Chris|Thierer, Tobias|Ashton, Bruce|Meintjes, Peter|Drummond, Alexei', 'Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.|Biomatters Ltd., 2/76 Anzac Avenue, Auckland, 1010, New Zealand.', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New ', 8404, 'Algorithms|Base Sequence|Computational Biology|Programming Languages|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts199', '22543367', 'The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.'),
(76890, 2012, 'bio', 'easyFRAP: an interactive, easy-to-use tool for qualitative and quantitative analysis of FRAP data', 'Rapsomaniki, Maria Anna|Kotsantis, Panagiotis|Symeonidou, Ioanna-Eleni|Giakoumakis, Nickolaos-Nikiforos|Taraviras, Stavros|Lygerou, Zoi', 'Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr|Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr|Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr|Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr|Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr|Laboratory of Biology, School of Medicine, University of Patras, 26505 Rio, Patras, Greece. rapsoman@upatras.gr', 'Greece|Greece|Greece|Greece|Greece|Greece', 87, 'Fluorescence Recovery After Photobleaching|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts241', '22543368', 'We present easyFRAP, a versatile tool that assists quantitative and qualitative analysis of fluorescence recovery after photobleaching (FRAP) data. The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.'),
(76891, 2012, 'bio', 'Optimal simultaneous superpositioning of multiple structures with missing data', 'Theobald, Douglas L|Steindel, Phillip A', 'Department of Biochemistry, Brandeis University, MS009, Waltham, MA 02454, USA. dtheobald@brandeis.edu|Department of Biochemistry, Brandeis University, MS009, Waltham, MA 02454, USA. dtheobald@brandeis.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Least-Squares Analysis|Likelihood Functions|Models, Molecular|Protein Structure, Tertiary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bts243', '22543369', 'Superpositioning is an essential technique in structural biology that facilitates the comparison and analysis of conformational differences among topologically similar structures. Performing a superposition requires a one-to-one correspondence, or alignment, of the point sets in the different structures. However, in practice, some points are usually missing from several structures, for example, when the alignment contains gaps. Current superposition methods deal with missing data simply by superpositioning a subset of points that are shared among all the structures. This practice is inefficient, as it ignores important data, and it fails to satisfy the common least-squares criterion. In the extreme, disregarding missing positions prohibits the calculation of a superposition altogether.'),
(76892, 2012, 'bio', 'Truncated branch and bound achieves efficient constraint-based genetic design', 'Egen, Dennis|Lun, Desmond S', 'Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ 08102, USA.|Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ 08102, USA.', 'United States|United States', 18, 'Algorithms|Computational Biology|Computer-Aided Design|Escherichia coli|Gene Knockout Techniques|Metabolic Engineering|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/bts255', '22543499', 'Computer-aided genetic design is a promising approach to a core problem of metabolic engineering-that of identifying genetic manipulation strategies that result in engineered strains with favorable product accumulation. This approach has proved to be effective for organisms including Escherichia coli and Saccharomyces cerevisiae, allowing for rapid, rational design of engineered strains. Finding optimal genetic manipulation strategies, however, is a complex computational problem in which running time grows exponentially with the number of manipulations (i.e. knockouts, knock-ins or regulation changes) in the strategy. Thus, computer-aided gene identification has to date been limited in the complexity or optimality of the strategies it finds or in the size and level of detail of the metabolic networks under consideration.'),
(76893, 2012, 'bio', 'Statistical analysis of glycosylation profiles to compare tissue type and inflammatory disease state', 'Hayes, Catherine A|Nemes, Szilard|Karlsson, Niclas G', 'Department of Medical Biochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. catherine.hayes@medkem.gu.se|Department of Medical Biochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. catherine.hayes@medkem.gu.se|Department of Medical Biochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. catherine.hayes@medkem.gu.se', 'Sweden|Sweden|Sweden', 8, 'Chromatography, Liquid|Colitis, Ulcerative|Colon|Cystic Fibrosis|Data Interpretation, Statistical|Glycomics|Glycosylation|Humans|Lung|Mass Spectrometry|Models, Statistical|Monosaccharides|Oligosaccharides|Protein Processing, Post-Translational', 'Journal Article', '10.1093/bioinformatics/bts242', '22543500', 'Glycosylation is one of the most important post-translational modifications of proteins and explains some aspects of the diversification of higher organisms not explained by template-driven synthesis. For glycomics to mature as much as genomics and proteomics, the necessary tools need to be developed and tested. Liquid chromatography-mass spectrometry is one of the gold standards for oligosaccharide analysis and leads to large amounts of data, not easily interpreted manually. We present a study on the testing and validation of statistical analysis tools to aid the structural elucidation of these analyses as well as using the results to answer biologically relevant questions.'),
(76894, 2012, 'bio', 'FindGeo: a tool for determining metal coordination geometry', 'Andreini, Claudia|Cavallaro, Gabriele|Lorenzini, Serena', 'Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it', 'Italy|Italy|Italy', 0, 'Computational Biology|Metals|Nucleic Acid Conformation|Nucleic Acids|Protein Structure, Secondary|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts246', '22556364', 'Metals are essential for the structure and function of many proteins and nucleic acids. The geometrical arrangement of the atoms that coordinate a metal in a biological macromolecule is an important determinant of the specificity and role of that metal. At present, however, this information can be retrieved only from the literature, which sometimes contains an improper or incorrect description of the geometry, and often lacks it altogether. Thus, we developed FindGeo to quickly and easily determine the coordination geometry of selected, or all, metals in a given structure. FindGeo works by superimposing the metal-coordinating atoms in the input structure to a library of templates with alternative ideal geometries, which are ranked by RMSD to identify the best geometry assignment.'),
(76895, 2012, 'bio', 'Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform', 'Cox, Anthony J|Bauer, Markus J|Jakobi, Tobias|Rosone, Giovanna', 'Computational Biology Group, Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex, UK. acox@illumina.com|Computational Biology Group, Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex, UK. acox@illumina.com|Computational Biology Group, Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex, UK. acox@illumina.com|Computational Biology Group, Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex, UK. acox@illumina.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 85, 'Algorithms|Computer Simulation|Data Compression|Databases, Nucleic Acid|Escherichia coli|Genome, Human|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts173', '22556365', 'The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets.'),
(76896, 2012, 'bio', 'Analysis of case-control association studies with known risk variants', 'Zaitlen, Noah|Pasaniuc, Bogdan|Patterson, Nick|Pollack, Samuela|Voight, Benjamin|Groop, Leif|Altshuler, David|Henderson, Brian E|Kolonel, Laurence N|Le Marchand, Loic|Waters, Kevin|Haiman, Christopher A|Stranger, Barbara E|Dermitzakis, Emmanouil T|Kraft, Peter|Price, Alkes L', 'Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu|Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Case-Control Studies|Genetic Association Studies|Genome-Wide Association Study|Humans|Logistic Models|Models, Statistical|Polymorphism, Single Nucleotide|Prevalence|Risk|Software', 'Journal Article', '10.1093/bioinformatics/bts259', '22556366', 'The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature.'),
(76897, 2012, 'bio', 'graph2tab, a library to convert experimental workflow graphs into tabular formats', 'Brandizi, Marco|Kurbatova, Natalja|Sarkans, Ugis|Rocca-Serra, Philippe', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. brandizi@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. brandizi@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. brandizi@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. brandizi@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Computational Biology|Computer Graphics|Databases, Factual|Oligonucleotide Array Sequence Analysis|Programming Languages|Workflow', 'Journal Article', '10.1093/bioinformatics/bts258', '22556367', 'Spreadsheet-like tabular formats are ever more popular in the biomedical field as a mean for experimental reporting. The problem of converting the graph of an experimental workflow into a table-based representation occurs in many such formats and is not easy to solve.'),
(76898, 2012, 'bio', 'SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes', 'Pruesse, Elmar|Peplies, Jörg|Glöckner, Frank Oliver', 'Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstr.1, 28359 Bremen, Germany. epruesse@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstr.1, 28359 Bremen, Germany. epruesse@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstr.1, 28359 Bremen, Germany. epruesse@mpi-bremen.de', 'Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Computational Biology|Databases, Nucleic Acid|Genes, rRNA|RNA, Ribosomal|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bts252', '22556368', 'In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements.'),
(76899, 2012, 'bio', 'Ka-me: a Voronoi image analyzer', 'Khiripet, Noppadon|Khantuwan, Wongarnet|Jungck, John R', 'Knowledge Elicitation and Archiving Laboratory, National Electronics and Computer Technology Center (NECTEC), 112 Phahon Yothin Rd., Klong 1, Klong Luang, Pathumthani 12120, Thailand. noppadon.khiripet@nectec.or.th|Knowledge Elicitation and Archiving Laboratory, National Electronics and Computer Technology Center (NECTEC), 112 Phahon Yothin Rd., Klong 1, Klong Luang, Pathumthani 12120, Thailand. noppadon.khiripet@nectec.or.th|Knowledge Elicitation and Archiving Laboratory, National Electronics and Computer Technology Center (NECTEC), 112 Phahon Yothin Rd., Klong 1, Klong Luang, Pathumthani 12120, Thailand. noppadon.khiripet@nectec.or.th', 'Thailand|Thailand|Thailand', 8, 'Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/bts253', '22556369', 'Ka-me is a Voronoi image analyzer that allows users to analyze any image with a convex polygonal tessellation or any spatial point distribution by fitting Voronoi polygons and their dual, Delaunay triangulations, to the pattern. The analytical tools include a variety of graph theoretic and geometric tools that summarize the distribution of the numbers of edges per face, areas, perimeters, angles of Delaunay triangle edges (anglograms), Gabriel graphs, nearest neighbor graphs, minimal spanning trees, Ulam trees, Pitteway tests, circumcircles and convexhulls, as well as spatial statistics (Clark-Evans Nearest Neighborhood and Variance to Mean Ratio) and export functions for standard relationships (Lewiss Law, Deschs Law and Aboav-Weaire Law).'),
(76900, 2012, 'bio', 'SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations', 'Liu, Dajiang J|Leal, Suzanne M', 'Department of Biostatistics, Center of Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA. dajiang@umich.edu|Department of Biostatistics, Center of Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA. dajiang@umich.edu', 'United States|United States', 4, 'Adenoma|Case-Control Studies|Colorectal Neoplasms|Genetic Association Studies|Genetic Variation|Genotype|Humans|Phenotype|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts263', '22556370', 'Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.'),
(76901, 2012, 'bio', 'Utilizing RNA-Seq data for de novo coexpression network inference', 'Iancu, Ovidiu D|Kawane, Sunita|Bottomly, Daniel|Searles, Robert|Hitzemann, Robert|McWeeney, Shannon', 'Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu|Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu|Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu|Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu|Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu|Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239-3098, USA. iancuo@ohsu.edu', 'United States|United States|United States|United States|United States|United States', 86, 'Animals|Gene Expression Profiling|Gene Regulatory Networks|Male|Mice|Mice, Inbred C57BL|Mice, Inbred DBA|Oligonucleotide Array Sequence Analysis|RNA|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bioinformatics/bts245', '22556371', 'RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks.'),
(76902, 2012, 'bio', 'BRAT-BW: efficient and accurate mapping of bisulfite-treated reads', 'Harris, Elena Y|Ponts, Nadia|Le Roch, Karine G|Lonardi, Stefano', 'Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu', 'United States|United States|United States|United States', 0, 'Cytosine|DNA Methylation|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/bts264', '22563065', 'We introduce BRAT-BW, a fast, accurate and memory-efficient tool that maps bisulfite-treated short reads (BS-seq) to a reference genome using the FM-index (Burrows-Wheeler transform). BRAT-BW is significantly more memory efficient and faster on longer reads than current state-of-the-art tools for BS-seq data, without compromising on accuracy. BRAT-BW is a part of a software suite for genome-wide single base-resolution methylation data analysis that supports single and paired-end reads and includes a tool for estimation of methylation level at each cytosine.'),
(76903, 2012, 'bio', 'Identifying differentially expressed transcripts from RNA-seq data with biological variation', 'Glaus, Peter|Honkela, Antti|Rattray, Magnus', 'School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. glaus@cs.man.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. glaus@cs.man.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. glaus@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 113, 'Bayes Theorem|Gene Expression Profiling|Genetic Variation|High-Throughput Nucleotide Sequencing|Models, Statistical|Sequence Alignment|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts260', '22563066', 'High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.'),
(76904, 2012, 'bio', 'Two effective methods for correcting experimental high-throughput screening data', 'Dragiev, Plamen|Nadon, Robert|Makarenkov, Vladimir', 'Dxc3xa9partement dInformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, C.P.8888, s. Centre-Ville, Montrxc3xa9al, QC, Canada.|Dxc3xa9partement dInformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, C.P.8888, s. Centre-Ville, Montrxc3xa9al, QC, Canada.|Dxc3xa9partement dInformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, C.P.8888, s. Centre-Ville, Montrxc3xa9al, QC, Canada.', 'Canada|Canada|Canada', 0, 'Computer Simulation|Drug Discovery|High-Throughput Screening Assays', 'Evaluation Studies', '10.1093/bioinformatics/bts262', '22563067', 'Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003, Carralot et al., 2012, Kevorkov and Makarenkov, 2005, Makarenkov et al., 2007, Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the xcfx87(2) goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.'),
(76905, 2012, 'bio', 'Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data', 'Zacher, Benedikt|Abnaof, Khalid|Gade, Stephan|Younesi, Erfan|Tresch, Achim|Fröhlich, Holger', 'Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.|Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.|Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.|Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.|Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.|Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Bayes Theorem|Computational Biology|Escherichia coli|Female|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|Ovarian Neoplasms|Pancreatic Neoplasms|RNA, Messenger|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bts257', '22563068', 'There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse.'),
(76906, 2012, 'bio', 'A holistic in silico approach to predict functional sites in protein structures', 'Segura, Joan|Jones, Pamela F|Fernandez-Fuentes, Narcis', 'Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds LS9 7TF, UK.|Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds LS9 7TF, UK.|Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds LS9 7TF, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Binding Sites|Computational Biology|Computer Simulation|DNA|Internet|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts269', '22563069', 'Proteins execute and coordinate cellular functions by interacting with other biomolecules. Among these interactions, protein-protein (including peptide-mediated), protein-DNA and protein-RNA interactions cover a wide range of critical processes and cellular functions. The functional characterization of proteins requires the description and mapping of functional biomolecular interactions and the identification and characterization of functional sites is an important step towards this end.'),
(76907, 2012, 'bio', 'GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization', 'Liao, Yu-Chieh|Tsai, Ming-Hsin|Chen, Feng-Chi|Hsiung, Chao A', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan, R.O.C.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan, R.O.C.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan, R.O.C.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan, R.O.C.', '-|-|-|-', 22, 'Computer Graphics|Computer Simulation|Genome|Genomics|Metabolic Networks and Pathways|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts267', '22563070', 'Genome-scale metabolic network models have become an indispensable part of the increasingly important field of systems biology. Metabolic systems biology studies usually include three major components-network model construction, objective- and experiment-guided model editing and visualization, and simulation studies based mainly on flux balance analyses. Bioinformatics tools are required to facilitate these complicated analyses. Although some of the required functions have been served separately by existing tools, a free software resource that simultaneously serves the needs of the three major components is not yet available.'),
(76908, 2012, 'bio', 'BreakFusion: targeted assembly-based identification of gene fusions in whole transcriptome paired-end sequencing data', 'Chen, Ken|Wallis, John W|Kandoth, Cyriac|Kalicki-Veizer, Joelle M|Mungall, Karen L|Mungall, Andrew J|Jones, Steven J|Marra, Marco A|Ley, Timothy J|Mardis, Elaine R|Wilson, Richard K|Weinstein, John N|Ding, Li', 'Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 41, 'Cell Line, Tumor|Computational Biology|Humans|Sequence Alignment|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts272', '22563071', 'Despite recent progress, computational tools that identify gene fusions from next-generation whole transcriptome sequencing data are often limited in accuracy and scalability. Here, we present a software package, BreakFusion that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency.'),
(76909, 2012, 'bio', 'A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging', 'Logsdon, Benjamin A|Carty, Cara L|Reiner, Alexander P|Dai, James Y|Kooperberg, Charles', 'Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 98109, Seattle, WA 98195, USA. blogsdon@fhcrc.org|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 98109, Seattle, WA 98195, USA. blogsdon@fhcrc.org|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 98109, Seattle, WA 98195, USA. blogsdon@fhcrc.org|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 98109, Seattle, WA 98195, USA. blogsdon@fhcrc.org|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 98109, Seattle, WA 98195, USA. blogsdon@fhcrc.org', 'United States|United States|United States|United States|United States', 12, 'African Americans|Algorithms|Bayes Theorem|Body Height|Genetic Loci|Genome-Wide Association Study|Humans|Models, Statistical|Polymorphism, Single Nucleotide|Regression Analysis', 'Evaluation Studies', '10.1093/bioinformatics/bts261', '22563072', 'For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm.'),
(76910, 2012, 'bio', 'OnlineCall: fast online parameter estimation and base calling for illuminas next-generation sequencing', 'Das, Shreepriya|Vikalo, Haris', 'Electrical and Computer Engineering Department, The University of Texas, Austin, TX 78712, USA.|Electrical and Computer Engineering Department, The University of Texas, Austin, TX 78712, USA.', 'United States|United States', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts256', '22569177', 'Next-generation DNA sequencing platforms are becoming increasingly cost-effective and capable of providing enormous number of reads in a relatively short time. However, their accuracy and read lengths are still lagging behind those of conventional Sanger sequencing method. Performance of next-generation sequencing platforms is fundamentally limited by various imperfections in the sequencing-by-synthesis and signal acquisition processes. This drives the search for accurate, scalable and computationally tractable base calling algorithms capable of accounting for such imperfections.'),
(76911, 2012, 'bio', 'Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly', 'Li, Heng', 'Medical Population Genetics Program, Broad Institute, 7 Cambridge Center, MA 02142, USA. hengli@broadinstitute.org', 'United States', 177, 'Algorithms|Computational Biology|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts280', '22569178', 'Eugene Myers in his string graph paper suggested that in a string graph or equivalently a unitig graph, any path spells a valid assembly. As a string/unitig graph also encodes every valid assembly of reads, such a graph, provided that it can be constructed correctly, is in fact a lossless representation of reads. In principle, every analysis based on whole-genome shotgun sequencing (WGS) data, such as SNP and insertion/deletion (INDEL) calling, can also be achieved with unitigs.'),
(76912, 2012, 'bio', 'The Hematopoietic Expression Viewer: expanding mobile apps as a scientific tool', 'James, Regis A|Rao, Mitchell M|Chen, Edward S|Goodell, Margaret A|Shaw, Chad A', 'Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 7, 'Computational Biology|Gene Expression|Hematopoietic Stem Cells|Humans|Microarray Analysis|Software|Transcriptome|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts279', '22576171', 'Many important data in current biological science comprise hundreds, thousands or more individual results. These massive data require computational tools to navigate results and effectively interact with the content. Mobile device apps are an increasingly important tool in the everyday lives of scientists and non-scientists alike. These software present individuals with compact and efficient tools to interact with complex data at meetings or other locations remote from their main computing environment. We believe that apps will be important tools for biologists, geneticists and physicians to review content while participating in biomedical research or practicing medicine. We have developed a prototype app for displaying gene expression data using the iOS platform. To present the software engineering requirements, we review the model-view-controller schema for Apples iOS. We apply this schema to a simple app for querying locally developed microarray gene expression data. The challenge of this application is to balance between storing content locally within the app versus obtaining it dynamically via a network connection.'),
(76913, 2012, 'bio', 'BEDOPS: high-performance genomic feature operations', 'Neph, Shane|Kuehn, M Scott|Reynolds, Alex P|Haugen, Eric|Thurman, Robert E|Johnson, Audra K|Rynes, Eric|Maurano, Matthew T|Vierstra, Jeff|Thomas, Sean|Sandstrom, Richard|Humbert, Richard|Stamatoyannopoulos, John A', 'Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu|Department of Genome Sciences and Department of Medicine, University of Washington, Seattle, Washington, DC 98195, USA. sjn@u.washington.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 285, 'Data Compression|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/bts277', '22576172', 'The large and growing number of genome-wide datasets highlights the need for high-performance feature analysis and data comparison methods, in addition to efficient data storage and retrieval techniques. We introduce BEDOPS, a software suite for common genomic analysis tasks which offers improved flexibility, scalability and execution time characteristics over previously published packages. The suite includes a utility to compress large inputs into a lossless format that can provide greater space savings and faster data extractions than alternatives.'),
(76914, 2012, 'bio', 'CUSHAW: a CUDA compatible short read aligner to large genomes based on the Burrows-Wheeler transform', 'Liu, Yongchao|Schmidt, Bertil|Maskell, Douglas L', 'School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuy@uni-mainz.de|School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuy@uni-mainz.de|School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuy@uni-mainz.de', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Computational Biology|Genome, Human|Genomics|Humans|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/bts276', '22576173', 'New high-throughput sequencing technologies have promoted the production of short reads with dramatically low unit cost. The explosive growth of short read datasets poses a challenge to the mapping of short reads to reference genomes, such as the human genome, in terms of alignment quality and execution speed.'),
(76915, 2012, 'bio', 'MicrobeDB: a locally maintainable database of microbial genomic sequences', 'Langille, Morgan G I|Laird, Matthew R|Hsiao, William W L|Chiu, Terry A|Eisen, Jonathan A|Brinkman, Fiona S L', 'Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com|Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com|Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com|Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com|Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com|Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. morgan.g.i.langille@gmail.com', 'Canada|Canada|Canada|Canada|Canada|Canada', 12, 'Computational Biology|Databases, Genetic|Genome, Archaeal|Genome, Bacterial|Genomics|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts273', '22576174', 'Analysis of microbial genomes often requires the general organization and comparison of tens to thousands of genomes both from public repositories and unpublished sources. MicrobeDB provides a foundation for such projects by the automation of downloading published, completed bacterial and archaeal genomes from key sources, parsing annotations of all genomes (both public and private) into a local database, and allowing interaction with the database through an easy to use programming interface. MicrobeDB creates a simple to use, easy to maintain, centralized local resource for various large-scale comparative genomic analyses and a back-end for future microbial application design.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76916, 2012, 'bio', 'CalMaTe: a method and software to improve allele-specific copy number of SNP arrays for downstream segmentation', 'Ortiz-Estevez, Maria|Aramburu, Ander|Bengtsson, Henrik|Neuvial, Pierre|Rubio, Angel', 'CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.|CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.|CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.|CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.|CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.', 'Spain|Spain|Spain|Spain|Spain', 12, 'Alleles|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts248', '22576175', 'CalMaTe calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.'),
(76917, 2012, 'bio', 'Mosaic: making biological sense of complex networks', 'Zhang, Chao|Hanspers, Kristina|Kuchinsky, Allan|Salomonis, Nathan|Xu, Dong|Pico, Alexander R', 'Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Color|Computational Biology|Molecular Sequence Annotation|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts278', '22576176', 'We present a Cytoscape plugin called Mosaic to support interactive network annotation, partitioning, layout and coloring based on gene ontology or other relevant annotations.'),
(76918, 2012, 'bio', 'CPSS: a computational platform for the analysis of small RNA deep sequencing data', 'Zhang, Yuanwei|Xu, Bo|Yang, Yifan|Ban, Rongjun|Zhang, Huan|Jiang, Xiaohua|Cooke, Howard J|Xue, Yu|Shi, Qinghua', 'Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.', 'China|China|China|China|China|China|China|China|China', 39, 'Animals|Chromosome Mapping|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Internet|Mice|MicroRNAs|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts282', '22576177', 'Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets.'),
(76919, 2012, 'bio', 'Correcting for the bias due to expression specificity improves the estimation of constrained evolution of expression between mouse and human', 'Piasecka, Barbara|Robinson-Rechavi, Marc|Bergmann, Sven', 'Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 13, 'Animals|Computational Biology|Evolution, Molecular|Gene Expression|Gene Expression Profiling|Humans|Mice|Transcriptome', 'Comparative Study', '10.1093/bioinformatics/bts266', '22576178', 'Comparative analyses of gene expression data from different species have become an important component of the study of molecular evolution. Thus methods are needed to estimate evolutionary distances between expression profiles, as well as a neutral reference to estimate selective pressure. Divergence between expression profiles of homologous genes is often calculated with Pearsons or Euclidean distance. Neutral divergence is usually inferred from randomized data. Despite being widely used, neither of these two steps has been well studied. Here, we analyze these methods formally and on real data, highlight their limitations and propose improvements.'),
(76920, 2012, 'bio', 'Genome-scale analysis of interaction dynamics reveals organization of biological networks', 'Das, Jishnu|Mohammed, Jaaved|Yu, Haiyuan', 'Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.|Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.|Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.', 'United States|United States|United States', 44, 'Algorithms|Computational Biology|Genomics|Humans|Saccharomyces cerevisiae|Signal Transduction|Systems Biology|Two-Hybrid System Techniques', 'Journal Article', '10.1093/bioinformatics/bts283', '22576179', 'Analyzing large-scale interaction networks has generated numerous insights in systems biology. However, such studies have primarily been focused on highly co-expressed, stable interactions. Most transient interactions that carry out equally important functions, especially in signal transduction pathways, are yet to be elucidated and are often wrongly discarded as false positives. Here, we revisit a previously described Smith-Waterman-like dynamic programming algorithm and use it to distinguish stable and transient interactions on a genomic scale in human and yeast. We find that in biological networks, transient interactions are key links topologically connecting tightly regulated functional modules formed by stable interactions and are essential to maintaining the integrity of cellular networks. We also perform a systematic analysis of interaction dynamics across different technologies and find that high-throughput yeast two-hybrid is the only available technology for detecting transient interactions on a large scale.'),
(76921, 2012, 'bio', 'Fast and sensitive mapping of bisulfite-treated sequencing data', 'Otto, Christian|Stadler, Peter F|Hoffmann, Steve', 'Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Department of Computer Science, University Leipzig, 04107 Leipzig, Germany.|Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Department of Computer Science, University Leipzig, 04107 Leipzig, Germany.|Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Department of Computer Science, University Leipzig, 04107 Leipzig, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Cytosine|DNA|DNA Methylation|Genome|Humans|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/bts254', '22581174', 'Cytosine DNA methylation is one of the major epigenetic modifications and influences gene expression, developmental processes, X-chromosome inactivation, and genomic imprinting. Aberrant methylation is furthermore known to be associated with several diseases including cancer. The gold standard to determine DNA methylation on genome-wide scales is bisulfite sequencing: DNA fragments are treated with sodium bisulfite resulting in the conversion of unmethylated cytosines into uracils, whereas methylated cytosines remain unchanged. The resulting sequencing reads thus exhibit asymmetric bisulfite-related mismatches and suffer from an effective reduction of the alphabet size in the unmethylated regions, rendering the mapping of bisulfite sequencing reads computationally much more demanding. As a consequence, currently available read mapping software often fails to achieve high sensitivity and in many cases requires unrealistic computational resources to cope with large real-life datasets.'),
(76922, 2012, 'bio', 'Robustness and accuracy of functional modules in integrated network analysis', 'Beisser, Daniela|Brunkhorst, Stefan|Dandekar, Thomas|Klau, Gunnar W|Dittrich, Marcus T|Müller, Tobias', 'Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 17, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Lymphoma, Large B-Cell, Diffuse|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma', 'Comparative Study', '10.1093/bioinformatics/bts265', '22581175', 'High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions.'),
(76923, 2012, 'bio', 'Software support for SBGN maps: SBGN-ML and LibSBGN', 'van Iersel, Martijn P|Villéger, Alice C|Czauderna, Tobias|Boyd, Sarah E|Bergmann, Frank T|Luna, Augustin|Demir, Emek|Sorokin, Anatoly|Dogrusoz, Ugur|Matsuoka, Yukiko|Funahashi, Akira|Aladjem, Mirit I|Mi, Huaiyu|Moodie, Stuart L|Kitano, Hiroaki|Le Novère, Nicolas|Schreiber, Falk', 'EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net|EMBL European Bioinformatics Institute, Hinxton, UK. sbgn-libsbgn@lists.sourceforge.net', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 48, 'Computational Biology|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts270', '22581176', 'LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.'),
(76924, 2012, 'bio', 'Exhaustive database searching for amino acid mutations in proteomes', 'Hyatt, Doug|Pan, Chongle', 'BioSciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|BioSciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Computational Biology|Databases, Protein|Microbial Consortia|Mining|Mutation|Proteins|Proteome|Proteomics|Rhodopseudomonas|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bts274', '22581177', 'Amino acid mutations in proteins can be found by searching tandem mass spectra acquired in shotgun proteomics experiments against protein sequences predicted from genomes. Traditionally, unconstrained searches for amino acid mutations have been accomplished by using a sequence tagging approach that combines de novo sequencing with database searching. However, this approach is limited by the performance of de novo sequencing.'),
(76925, 2012, 'bio', 'iFad: an integrative factor analysis model for drug-pathway association inference', 'Ma, Haisu|Zhao, Hongyu', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Cell Line, Tumor|Computer Simulation|Drug Discovery|Factor Analysis, Statistical|Humans|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/bts285', '22581178', 'Pathway-based drug discovery considers the therapeutic effects of compounds in the global physiological environment. This approach has been gaining popularity in recent years because the target pathways and mechanism of action for many compounds are still unknown, and there are also some unexpected off-target effects. Therefore, the inference of drug-pathway associations is a crucial step to fully realize the potential of system-based pharmacological research. Transcriptome data offer valuable information on drug-pathway targets because the pathway activities may be reflected through gene expression levels. Hence, it is of great interest to jointly analyze the drug sensitivity and gene expression data from the same set of samples to investigate the gene-pathway-drug-pathway associations.'),
(76926, 2012, 'bio', 'Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs', 'Saunders, Christopher T|Wong, Wendy S W|Swamy, Sajani|Becq, Jennifer|Murray, Lisa J|Cheetham, R Keira', 'Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com|Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com|Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com|Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com|Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com|Illumina, Inc., 5200 Illumina Way, San Diego, CA 92122, USA. csaunders@illumina.com', 'United States|United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Computational Biology|Exome|Gene Frequency|Genetic Variation|Genome|Humans|INDEL Mutation|Models, Genetic|Neoplasms|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/bts271', '22581179', 'Whole genome and exome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. The consequent increased demand for somatic variant analysis of paired samples requires methods specialized to model this problem so as to sensitively call variants at any practical level of tumor impurity.'),
(76927, 2012, 'bio', 'Anonymous nuclear loci in non-model organisms: making the most of high-throughput genome surveys', 'Bertozzi, Terry|Sanders, Kate L|Sistrom, Mark J|Gardner, Michael G', 'Evolutionary Biology Unit, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia. terry.bertozzi@samuseum.sa.gov.au|Evolutionary Biology Unit, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia. terry.bertozzi@samuseum.sa.gov.au|Evolutionary Biology Unit, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia. terry.bertozzi@samuseum.sa.gov.au|Evolutionary Biology Unit, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia. terry.bertozzi@samuseum.sa.gov.au', 'Australia|Australia|Australia|Australia', 20, 'Animals|Cell Nucleus|Computational Biology|Gene Library|Genetic Loci|Genomics|Reptiles|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts284', '22581180', 'When working with non-model organisms, few if any species-specific markers are available for phylogenetic, phylogeographic and population studies. Therefore, researchers often try to adapt markers developed in distantly related taxa, resulting in poor amplification and ascertainment bias in their target taxa. Markers can be developed de novo and anonymous nuclear loci (ANL) are proving to be a boon for researchers seeking large numbers of fast-evolving, independent loci. However, the development of ANL can be laboratory intensive and expensive. A workflow is described to identify suitable low-copy anonymous loci from high-throughput shotgun sequences, dramatically reducing the cost and time required to develop these markers and produce robust multilocus datasets.'),
(76928, 2012, 'bio', 'SRMA: an R package for resequencing array data analysis', 'Zhang, Nianxiang|Xu, Yan|OHely, Martin|Speed, Terence P|Scharfe, Curt|Wang, Wenyi', 'Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts286', '22581181', 'Sequencing by hybridization to oligonucleotides has evolved into an inexpensive, reliable and fast technology for targeted sequencing. Hundreds of human genes can now be sequenced within a day using a single hybridization to a resequencing microarray. However, several issues inherent to these arrays (e.g. cross-hybridization, variable probe/target affinity) cause sequencing errors and have prevented more widespread applications. We developed an R package for resequencing microarray data analysis that integrates a novel statistical algorithm, sequence robust multi-array analysis (SRMA), for rare variant detection with high sensitivity (false negative rate, FNR 5%) and accuracy (false positive rate, FPR 1xc3x9710xe2x81xbbxe2x81xb5). The SRMA package consists of five modules for quality control, data normalization, single array analysis, multi-array analysis and output analysis. The entire workflow is efficient and identifies rare DNA single nucleotide variations and structural changes such as gene deletions with high accuracy and sensitivity.'),
(76929, 2012, 'bio', 'HCS-Analyzer: open source software for high-content screening data correction and analysis', 'Ogier, Arnaud|Dorval, Thierry', 'Cellular Differentiation and Toxicity Prediction, Institut Pasteur Korea 696 Sampyeong-dong, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-400 Korea.|Cellular Differentiation and Toxicity Prediction, Institut Pasteur Korea 696 Sampyeong-dong, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-400 Korea.', '-|-', 17, 'Algorithms|Cluster Analysis|Computational Biology|Drug Industry|High-Throughput Screening Assays|RNA, Small Interfering|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts288', '22586178', 'High-throughput screening is a powerful technology principally used by pharmaceutical industries allowing the identification of molecules of interest within large libraries. Originally target based, cellular assays provide a way to test compounds (or other biological material such as small interfering RNA) in a more physiologically realistic in vitro environment. High-content screening (HCS) platforms are now available at lower cost, giving the opportunity for universities or research institutes to access those technologies for research purposes. However, the amount of information extracted from each experiment is multiplexed and hence difficult to handle. In such context, there is an important need for an easy-to-use, but still powerful software able to manage multidimensional screening data by performing adapted quality control and classification. HCS-analyzer includes: a user-friendly interface specifically dedicated to HCS readouts, an automated approach to identify systematic errors potentially occurring during screening and a set of tools to classify, cluster and identify phenotypes of interest among large and multivariate data.'),
(76930, 2012, 'bio', 'In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids', 'Kangas, Lars J|Metz, Thomas O|Isaac, Giorgis|Schrom, Brian T|Ginovska-Pangovska, Bojana|Wang, Luning|Tan, Li|Lewis, Robert R|Miller, John H', 'Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov|Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 38, 'Algorithms|Artificial Intelligence|Computer Simulation|Lipids|Metabolomics|Sensitivity and Specificity|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bts194', '22592377', 'Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry.'),
(76931, 2012, 'bio', 'Improvement of 3D protein models using multiple templates guided by single-template model quality assessment', 'Buenavista, Maria T|Roche, Daniel B|McGuffin, Liam J', 'School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK.|School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK.|School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK.', 'United Kingdom|United Kingdom|United Kingdom', 34, 'Cluster Analysis|Computational Biology|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/bts292', '22592378', 'Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust2, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method.'),
(76932, 2012, 'bio', 'OSA: a fast and accurate alignment tool for RNA-Seq', 'Hu, Jun|Ge, Huanying|Newman, Matt|Liu, Kejun', 'Division of Bioinformatics, Omicsoft Inc., 164 Quade Drive, Cary, NC 27513, USA. john.hu@omicsoft.com|Division of Bioinformatics, Omicsoft Inc., 164 Quade Drive, Cary, NC 27513, USA. john.hu@omicsoft.com|Division of Bioinformatics, Omicsoft Inc., 164 Quade Drive, Cary, NC 27513, USA. john.hu@omicsoft.com|Division of Bioinformatics, Omicsoft Inc., 164 Quade Drive, Cary, NC 27513, USA. john.hu@omicsoft.com', 'United States|United States|United States|United States', 56, 'Computational Biology|Genome|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts294', '22592379', 'Accurately mapping RNA-Seq reads to the reference genome is a critical step for performing downstream analysis such as transcript assembly, isoform detection and quantification. Many tools have been developed, however, given the huge size of the next generation sequencing datasets and the complexity of the transcriptome, RNA-Seq read mapping remains a challenge with the ever-increasing amount of data. We develop Omicsoft sequence aligner (OSA), a fast and accurate alignment tool for RNA-Seq data. Benchmarked with existing methods, OSA improves mapping speed 4-10-fold with better sensitivity and less false positives.'),
(76933, 2012, 'bio', 'MotifSuite: workflow for probabilistic motif detection and assessment', 'Claeys, Marleen|Storms, Valerie|Sun, Hong|Michoel, Tom|Marchal, Kathleen', 'Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Heverlee, Belgium.|Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Heverlee, Belgium.|Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Heverlee, Belgium.|Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Heverlee, Belgium.|Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Heverlee, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Internet|Protein Structure, Tertiary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bts293', '22592380', 'Probabilistic motif detection requires a multi-step approach going from the actual de novo regulatory motif finding up to a tedious assessment of the predicted motifs. MotifSuite, a user-friendly web interface streamlines this analysis flow. Its core consists of two post-processing procedures that allow prioritizing the motif detection output. The tools offered by MotifSuite are built around the well-established motif detection tool MotifSampler and can also be used in combination with any other probabilistic motif detection tool. Elaborate guidelines on each of its applications have been provided.'),
(76934, 2012, 'bio', 'IRView: a database and viewer for protein interacting regions', 'Fujimori, Shigeo|Hirai, Naoya|Masuoka, Kazuyo|Oshikubo, Tomohiro|Yamashita, Tatsuhiro|Washio, Takanori|Saito, Ayumu|Nagasaki, Masao|Miyano, Satoru|Miyamoto-Sato, Etsuko', 'Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.|Division of Interactome Medical Sciences, Institute of Medical Science, The University of Tokyo, Tokyo 108-8039, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 2, 'Alternative Splicing|Animals|Databases, Protein|Humans|Internet|Mice|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts289', '22592381', 'Protein-protein interactions (PPIs) are mediated through specific regions on proteins. Some proteins have two or more protein interacting regions (IRs) and some IRs are competitively used for interactions with different proteins. IRView currently contains data for 3417 IRs in human and mouse proteins. The data were obtained from different sources and combined with annotated region data from InterPro. Information on non-synonymous single nucleotide polymorphism sites and variable regions owing to alternative mRNA splicing is also included. The IRView web interface displays all IR data, including user-uploaded data, on reference sequences so that the positional relationship between IRs can be easily understood. IRView should be useful for analyzing underlying relationships between the proteins behind the PPI networks.'),
(76935, 2012, 'bio', 'Identifying mechanistic similarities in drug responses', 'Zhao, Chen|Hua, Jianping|Bittner, Michael L|Ivanov, Ivan|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cell Line, Tumor|Drug Design|Gene Expression Regulation|Humans|Image Processing, Computer-Assisted|Promoter Regions, Genetic|Proteins|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bts290', '22592382', 'In early drug development, it would be beneficial to be able to identify those dynamic patterns of gene response that indicate that drugs targeting a particular gene will be likely or not to elicit the desired response. One approach would be to quantitate the degree of similarity between the responses that cells show when exposed to drugs, so that consistencies in the regulation of cellular response processes that produce success or failure can be more readily identified.'),
(76936, 2012, 'bio', 'Fast computation for genome-wide association studies using boosted one-step statistics', 'Voorman, Arend|Rice, Ken|Lumley, Thomas', 'Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. voorma@u.washington.edu|Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. voorma@u.washington.edu|Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. voorma@u.washington.edu', 'United States|United States|United States', 11, 'Algorithms|Computational Biology|Genome-Wide Association Study|Genotype|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/bts291', '22592383', 'Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical.'),
(76937, 2012, 'bio', 'R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression', 'Dawson, John A|Ye, Shuyun|Kendziorski, Christina', 'Statistics and Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA. DrJADawson@hotmail.com|Statistics and Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA. DrJADawson@hotmail.com|Statistics and Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA. DrJADawson@hotmail.com', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Gene Expression|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts268', '22595207', 'R/EBcoexpress implements the approach of Dawson and Kendziorski using R, a freely available, open source statistical programming language. The approach identifies differential co-expression (DC) by examining the correlations among gene pairs using an empirical Bayesian approach, producing a false discovery rate controlled list of DC pairs. This interrogation of DC gene pairs complements but is distinct from differential expression analyses, under the general goal of understanding differential regulation across biological conditions.'),
(76938, 2012, 'bio', 'Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic', 'Gu, Jinghua|Xuan, Jianhua|Riggins, Rebecca B|Chen, Li|Wang, Yue|Clarke, Robert', 'Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|Cell Cycle|Cell Line, Tumor|Computational Biology|Computer Simulation|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Signal Transduction|Signal-To-Noise Ratio|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bts296', '22595208', 'Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive noise in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context.'),
(76939, 2012, 'bio', 'flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding', 'Ge, Yongchao|Sealfon, Stuart C', 'Department of Neurology and Center of Translational System Biology, Mount Sinai School of Medicine, New York, NY 10029, USA. yongchao.ge@mssm.edu|Department of Neurology and Center of Translational System Biology, Mount Sinai School of Medicine, New York, NY 10029, USA. yongchao.ge@mssm.edu', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Flow Cytometry|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/bts300', '22595209', 'For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these two problems would be very useful.'),
(76940, 2012, 'bio', 'CodonLogo: a sequence logo-based viewer for codon patterns', 'Sharma, Virag|Murphy, David P|Provan, Gregory|Baranov, Pavel V', 'Department of Biochemistry, College Cork, Cork, Ireland.|Department of Biochemistry, College Cork, Cork, Ireland.|Department of Biochemistry, College Cork, Cork, Ireland.|Department of Biochemistry, College Cork, Cork, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Base Sequence|Codon|Computational Biology|Pattern Recognition, Automated|Position-Specific Scoring Matrices|Programming Languages|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bts295', '22595210', 'Conserved patterns across a multiple sequence alignment can be visualized by generating sequence logos. Sequence logos show each column in the alignment as stacks of symbol(s) where the height of a stack is proportional to its informational content, whereas the height of each symbol within the stack is proportional to its frequency in the column. Sequence logos use symbols of either nucleotide or amino acid alphabets. However, certain regulatory signals in messenger RNA (mRNA) act as combinations of codons. Yet no tool is available for visualization of conserved codon patterns.'),
(76941, 2012, 'bio', 'Efficient RNA pairwise structure comparison by SETTER method', 'Hoksza, David|Svozil, Daniel', 'SIRET Research Group, Department of Software Engineering, FMP, Charles University in Prague, 11800 Czech Republic. hoksza@ksi.mff.cuni.cz|SIRET Research Group, Department of Software Engineering, FMP, Charles University in Prague, 11800 Czech Republic. hoksza@ksi.mff.cuni.cz', '-|-', 0, 'Algorithms|Computational Biology|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts301', '22611129', 'Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their 3D structures. Although a structural alignment of short RNAs is achievable in a reasonable amount of time, large structures represent much bigger challenge. However, the growth of the number of large RNAs deposited in the PDB database calls for the development of fast and accurate methods for analyzing their structures, as well as for rapid similarity searches in databases.'),
(76942, 2012, 'bio', 'regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions', 'Teng, Mingxiang|Ichikawa, Shoji|Padgett, Leah R|Wang, Yadong|Mort, Matthew|Cooper, David N|Koller, Daniel L|Foroud, Tatiana|Edenberg, Howard J|Econs, Michael J|Liu, Yunlong', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.', 'China|China|China|China|China|China|China|China|China|China|China', 7, 'Area Under Curve|Binding Sites|Computational Biology|Databases, Genetic|Genetic Linkage|Genome, Human|Genome-Wide Association Study|HEK293 Cells|Humans|Phenotype|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|ROC Curve|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bts275', '22611130', 'One of the fundamental questions in genetics study is to identify functional DNA variants that are responsible to a disease or phenotype of interest. Results from large-scale genetics studies, such as genome-wide association studies (GWAS), and the availability of high-throughput sequencing technologies provide opportunities in identifying causal variants. Despite the technical advances, informatics methodologies need to be developed to prioritize thousands of variants for potential causative effects.'),
(76943, 2012, 'bio', 'Gossamer--a resource-efficient de novo assembler', 'Conway, Thomas|Wazny, Jeremy|Bromage, Andrew|Zobel, Justin|Beresford-Smith, Bryan', 'NICTA Victoria Research Laboratory, Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia. tom.conway@nicta.com.au', 'Australia|Australia|Australia|Australia|Australia', 12, 'Algorithms|Computational Biology|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts297', '22611131', 'The de novo assembly of short read high-throughput sequencing data poses significant computational challenges. The volume of data is huge, the reads are tiny compared to the underlying sequence, and there are significant numbers of sequencing errors. There are numerous software packages that allow users to assemble short reads, but most are either limited to relatively small genomes (e.g. bacteria) or require large computing infrastructure or employ greedy algorithms and thus often do not yield high-quality results.'),
(76944, 2012, 'bio', 'PyKnot: a PyMOL tool for the discovery and analysis of knots in proteins', 'Lua, Rhonald C', 'Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. rhonald.lua@gmail.com', 'United States', 15, 'Computational Biology|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts299', '22611132', 'Understanding the differences between knotted and unknotted protein structures may offer insights into how proteins fold. To characterize the type of knot in a protein, we have developed PyKnot, a plugin that works seamlessly within the PyMOL molecular viewer and gives quick results including the knots invariants, crossing numbers and simplified knot projections and backbones. PyKnot may be useful to researchers interested in classifying knots in macromolecules and provides tools for students of biology and chemistry with which to learn topology and macromolecular visualization.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76945, 2012, 'bio', 'Flexible-meccano: a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables', 'Ozenne, Valéry|Bauer, Frédéric|Salmon, Loïc|Huang, Jie-Rong|Jensen, Malene Ringkjøbing|Segard, Stéphane|Bernadó, Pau|Charavay, Céline|Blackledge, Martin', 'Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.|Protein Dynamics and Flexibility, Institut de Biologie Structurale Jean-Pierre Ebel, 41 Rue Jules Horowitz, Grenoble, France.', 'France|France|France|France|France|France|France|France|France', 184, 'Algorithms|Humans|Magnetic Resonance Spectroscopy|Protein Conformation|Protein Folding|Proteins|Scattering, Small Angle', 'Journal Article', '10.1093/bioinformatics/bts172', '22613562', 'Intrinsically disordered proteins (IDPs) represent a significant fraction of the human proteome. The classical structure function paradigm that has successfully underpinned our understanding of molecular biology breaks down when considering proteins that have no stable tertiary structure in their functional form. One convenient approach is to describe the protein in terms of an equilibrium of rapidly inter-converting conformers. Currently, tools to generate such ensemble descriptions are extremely rare, and poorly adapted to the prediction of experimental data.'),
(76946, 2012, 'bio', 'BAIUCAS: a novel BLAST-based algorithm for the identification of upstream open reading frames with conserved amino acid sequences and its application to the Arabidopsis thaliana genome', 'Takahashi, Hiro|Takahashi, Anna|Naito, Satoshi|Onouchi, Hitoshi', 'Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan.|Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan.|Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan.|Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan.', 'Japan|Japan|Japan|Japan', 0, '5 Untranslated Regions|Algorithms|Amino Acid Sequence|Amino Acids|Arabidopsis|Base Sequence|Conserved Sequence|Genome, Plant|Genomics|Open Reading Frames|RNA, Messenger|Regulatory Sequences, Nucleic Acid|Sequence Homology|Species Specificity', 'Journal Article', '10.1093/bioinformatics/bts303', '22618534', 'Upstream open reading frames (uORFs) are often found in the 5-untranslated regions of eukaryotic messenger RNAs. Some uORFs have been shown to encode functional peptides involved in the translational regulation of the downstream main ORFs. Comparative genomic approaches have been used in genome-wide searches for uORFs encoding bioactive peptides, and by comparing uORF sequences between a few selected species or among a small group of species, uORFs with conserved amino acid sequences (UCASs) have been identified in plants, mammals and insects. Regulatory regions within uORF-encoded peptides that are involved in translational control are typically 10-20 amino acids long. Detection of homology between such short regions largely depends on the selection of species for comparison. To maximize the chances of identifying UCASs with short conserved regions, we devised a novel algorithm for homology search among a large number of species and the automatic selection of uORFs conserved in a wide range of species.'),
(76947, 2012, 'bio', 'High-throughput analysis of epistasis in genome-wide association studies with BiForce', 'Gyenesei, Attila|Moody, Jonathan|Semple, Colin A M|Haley, Chris S|Wei, Wen-Hua', 'Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland.|Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland.|Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland.|Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland.|Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland.', 'Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Computational Biology|Computer Simulation|Epistasis, Genetic|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts304', '22618535', 'Gene-gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS.'),
(76948, 2012, 'bio', 'SALIGN: a web server for alignment of multiple protein sequences and structures', 'Braberg, Hannes|Webb, Benjamin M|Tjioe, Elina|Pieper, Ursula|Sali, Andrej|Madhusudhan, M S', 'Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.', 'United States|United States|United States|United States|United States|United States', 53, 'Amino Acid Sequence|Computational Biology|Internet|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts302', '22618536', 'Accurate alignment of protein sequences and/or structures is crucial for many biological analyses, including functional annotation of proteins, classifying protein sequences into families, and comparative protein structure modeling. Described here is a web interface to SALIGN, the versatile protein multiple sequence/structure alignment module of MODELLER. The web server automatically determines the best alignment procedure based on the inputs, while allowing the user to override default parameter values. Multiple alignments are guided by a dendrogram computed from a matrix of all pairwise alignment scores. When aligning sequences to structures, SALIGN uses structural environment information to place gaps optimally. If two multiple sequence alignments of related proteins are input to the server, a profile-profile alignment is performed. All features of the server have been previously optimized for accuracy, especially in the contexts of comparative modeling and identification of interacting protein partners.'),
(76949, 2012, 'bio', 'RAxML-Light: a tool for computing terabyte phylogenies', 'Stamatakis, A|Aberer, A J|Goll, C|Smith, S A|Berger, S A|Izquierdo-Carrasco, F', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-68159 Heidelberg, Germany. alexandros.stamatakis@h-its.org', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Computer Simulation|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bts309', '22628519', 'Due to advances in molecular sequencing and the increasingly rapid collection of molecular data, the field of phyloinformatics is transforming into a computational science. Therefore, new tools are required that can be deployed in supercomputing environments and that scale to hundreds or thousands of cores.'),
(76950, 2012, 'bio', 'Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data', 'Tekwe, Carmen D|Carroll, Raymond J|Dabney, Alan R', 'Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA. ctekwe@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA. ctekwe@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA. ctekwe@stat.tamu.edu', 'United States|United States|United States', 0, 'Chromatography, Liquid|Computer Simulation|Diabetes Mellitus|Humans|Likelihood Functions|Mass Spectrometry|Models, Statistical|Proteins|Proteomics|Software|Statistics, Nonparametric|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bts306', '22628520', 'Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.'),
(76951, 2012, 'bio', 'The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets', 'Stocks, Matthew B|Moxon, Simon|Mapleson, Daniel|Woolfenden, Hugh C|Mohorianu, Irina|Folkes, Leighton|Schwach, Frank|Dalmay, Tamas|Moulton, Vincent', 'School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Genomics|MicroRNAs|RNA|RNA Interference|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts311', '22628521', 'RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods. However, many bioinformatics tools do not scale accordingly, are cumbersome, or require extensive support from bioinformatics experts. Therefore, researchers need user-friendly, robust tools, capable of not only processing large sRNA datasets in a reasonable time frame but also presenting the results in an intuitive fashion and visualizing sRNA genomic features. Herein, we present the UEA sRNA workbench, a suite of tools that is a successor to the web-based UEA sRNA Toolkit, but in downloadable format and with several enhanced and additional features.'),
(76952, 2012, 'bio', 'Multi-scale computational modeling of developmental biology', 'Setty, Yaki', 'Weizmann Institute of Science, Computer Science and Applied Mathematics, Rehovot, Israel. yaki.setty@gmail.com', 'Israel', 0, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|Computer Simulation|Developmental Biology|Models, Biological|Programming Languages', 'Journal Article', '10.1093/bioinformatics/bts307', '22628522', 'Normal development of multicellular organisms is regulated by a highly complex process in which a set of precursor cells proliferate, differentiate and move, forming over time a functioning tissue. To handle their complexity, developmental systems can be studied over distinct scales. The dynamics of each scale is determined by the collective activity of entities at the scale below it.'),
(76953, 2012, 'bio', 'DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment', 'Volkamer, Andrea|Kuhn, Daniel|Rippmann, Friedrich|Rarey, Matthias', 'Center for Bioinformatics, University of Hamburg, Bundesstr, Germany.|Center for Bioinformatics, University of Hamburg, Bundesstr, Germany.|Center for Bioinformatics, University of Hamburg, Bundesstr, Germany.|Center for Bioinformatics, University of Hamburg, Bundesstr, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Binding Sites|Computational Biology|Drug Discovery|Internet|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts310', '22628523', 'Many drug discovery projects fail because the underlying target is finally found to be undruggable. Progress in structure elucidation of proteins now opens up a route to automatic structure-based target assessment. DoGSiteScorer is a newly developed automatic tool combining pocket prediction, characterization and druggability estimation and is now available through a web server.'),
(76954, 2012, 'bio', 'Marker2sequence, mine your QTL regions for candidate genes', 'Chibon, Pierre-Yves|Schoof, Heiko|Visser, Richard G F|Finkers, Richard', 'Wageningen UR Plant Breeding, Wageningen University and Research Centre, PO Box 386, 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University and Research Centre, PO Box 386, 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University and Research Centre, PO Box 386, 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University and Research Centre, PO Box 386, 6700 AJ, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Chromosome Mapping|Computational Biology|Lycopersicon esculentum|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/bts298', '22628524', 'Marker2sequence (M2S) aims at mining quantitative trait loci (QTLs) for candidate genes. For each gene, within the QTL region, M2S uses data integration technology to integrate putative gene function with associated gene ontology terms, proteins, pathways and literature. As a typical QTL region easily contains several hundreds of genes, this gene list can then be further filtered using a keyword-based query on the aggregated annotations. M2S will help breeders to identify potential candidate genes for their traits of interest.'),
(76955, 2012, 'bio', 'BATMAN--an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model', 'Hao, Jie|Astle, William|De Iorio, Maria|Ebbels, Timothy M D', 'Section of Biomolecular Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK.|Section of Biomolecular Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK.|Section of Biomolecular Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK.|Section of Biomolecular Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 88, 'Algorithms|Bayes Theorem|Computational Biology|Magnetic Resonance Spectroscopy|Markov Chains|Metabolomics|Monte Carlo Method|Software', 'Journal Article', '10.1093/bioinformatics/bts308', '22635605', 'Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artefacts and limit immediate biological interpretation of models.'),
(76956, 2012, 'bio', 'Gowinda: unbiased analysis of gene set enrichment for genome-wide association studies', 'Kofler, Robert|Schlötterer, Christian', 'Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Wien, Austria.|Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Wien, Austria.', 'Austria|Austria', 0, 'Animals|Computational Biology|Drosophila melanogaster|Genome-Wide Association Study|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Probability|Software', 'Journal Article', '10.1093/bioinformatics/bts315', '22635606', 'An analysis of gene set [e.g. Gene Ontology (GO)] enrichment assumes that all genes are sampled independently from each other with the same probability. These assumptions are violated in genome-wide association (GWA) studies since (i) longer genes typically have more single-nucleotide polymorphisms resulting in a higher probability of being sampled and (ii) overlapping genes are sampled in clusters. Herein, we introduce Gowinda, a software specifically designed to test for enrichment of gene sets in GWA studies. We show that GO tests on GWA data could result in a substantial number of false-positive GO terms. Permutation tests implemented in Gowinda eliminate these biases, but maintain sufficient power to detect enrichment of GO terms. Since sufficient resolution for large datasets requires millions of permutations, we use multi-threading to keep computation times reasonable.'),
(76957, 2012, 'bio', 'Blockwise HMM computation for large-scale population genomic inference', 'Paul, Joshua S|Song, Yun S', 'Computer Science Division and Department of Statistics, University of California, Berkeley, CA 94720, USA.|Computer Science Division and Department of Statistics, University of California, Berkeley, CA 94720, USA.', 'United States|United States', 9, 'Algorithms|Computational Biology|Genetics, Population|Genomics|Haplotypes|Humans|Markov Chains|Probability|Software', 'Journal Article', '10.1093/bioinformatics/bts314', '22641715', 'A promising class of methods for large-scale population genomic inference use the conditional sampling distribution (CSD), which approximates the probability of sampling an individual with a particular DNA sequence, given that a collection of sequences from the population has already been observed. The CSD has a wide range of applications, including imputing missing sequence data, estimating recombination rates, inferring human colonization history and identifying tracts of distinct ancestry in admixed populations. Most well-used CSDs are based on hidden Markov models (HMMs). Although computationally efficient in principle, methods resulting from the common implementation of the relevant HMM techniques remain intractable for large genomic datasets.'),
(76958, 2012, 'bio', 'An integrated open framework for thermodynamics of reactions that combines accuracy and coverage', 'Noor, Elad|Bar-Even, Arren|Flamholz, Avi|Lubling, Yaniv|Davidi, Dan|Milo, Ron', 'Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel', 59, 'Computational Biology|Energy Metabolism|Escherichia coli|Hydrogen-Ion Concentration|Kinetics|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/bts317', '22645166', 'The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions.'),
(76959, 2012, 'bio', 'A common ancestry for BAP1 and Uch37 regulators', 'Sanchez-Pulido, Luis|Kong, Lesheng|Ponting, Chris P', 'MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK. luis.sanchezpulido@dpag.ox.ac.uk|MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK. luis.sanchezpulido@dpag.ox.ac.uk|MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK. luis.sanchezpulido@dpag.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 36, 'Animals|Carboxypeptidases|Computational Biology|DNA-Binding Proteins|Humans|Membrane Glycoproteins|Repressor Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Tumor Suppressor Proteins|Ubiquitin Thiolesterase', 'Journal Article', '10.1093/bioinformatics/bts319', '22645167', 'To reveal how the polycomb repressive-deubiquitinase (PR-DUB) complex controls substrate selection specificity, we undertook a detailed computational sequence analysis of its components: additional sex combs like 1 (ASXL1) and BRCA1-associated protein 1 (BAP1) proteins. This led to the discovery of two previously unrecognized domains in ASXL1: a forkhead (winged-helix) DNA-binding domain and a deubiquitinase adaptor domain shared with two regulators of ubiquitin carboxyl-terminal hydrolase 37 (Uch37), namely adhesion regulating molecule 1 (ADRM1) and nuclear factor related to kappaB (NFRKB). Our analysis demonstrates a common ancestry for BAP1 and Uch37 regulators in PR-DUB, INO80 chromatin remodelling and proteosome complexes.'),
(76960, 2012, 'bio', 'AS-EAST: a functional annotation tool for putative proteins encoded by alternatively spliced transcripts', 'Shionyu, Masafumi|Takahashi, Ken-ichi|Go, Mitiko', 'Department of Computer Bioscience, Nagahama Institute of Bio-Science and Technology, 1266, Tamura-cho, Nagahama, Shiga 526-0829, Japan. m_shionyu@nagahama-i-bio.ac.jp|Department of Computer Bioscience, Nagahama Institute of Bio-Science and Technology, 1266, Tamura-cho, Nagahama, Shiga 526-0829, Japan. m_shionyu@nagahama-i-bio.ac.jp|Department of Computer Bioscience, Nagahama Institute of Bio-Science and Technology, 1266, Tamura-cho, Nagahama, Shiga 526-0829, Japan. m_shionyu@nagahama-i-bio.ac.jp', 'Japan|Japan|Japan', 2, 'Alternative Splicing|Amino Acid Sequence|Computational Biology|Protein Isoforms|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts320', '22645168', 'Alternative Splicing Effects ASsessment Tools (AS-EAST) is an online tool for the functional annotation of putative proteins encoded by transcripts generated by alternative splicing (AS). When provided with a transcript sequence, AS-EAST identifies regions altered by AS events in the putative protein sequence encoded by the transcript. Users can evaluate the predicted function of the putative protein by inspecting whether functional domains are included in the altered regions. Moreover, users can infer the loss of inter-molecular interactions in the protein network according to whether the AS events affect interaction residues observed in the 3D structure of the reference isoform. The information obtained from AS-EAST will help to design experimental analyses for the functional significance of novel splice isoforms.'),
(76961, 2012, 'bio', 'Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale', 'Park, Chihyun|Ahn, Jaegyoon|Yoon, Youngmi|Park, Sanghyun', 'Department of Computer Science, Yonsei University, South Korea, Seoul 120-749, South Korea.|Department of Computer Science, Yonsei University, South Korea, Seoul 120-749, South Korea.|Department of Computer Science, Yonsei University, South Korea, Seoul 120-749, South Korea.|Department of Computer Science, Yonsei University, South Korea, Seoul 120-749, South Korea.', '-|-|-|-', 2, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computational Biology|DNA Copy Number Variations|Gene Regulatory Networks|Genome, Human|Genotype|Humans|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1093/bioinformatics/bts318', '22652832', 'Identifying functional relation of copy number variation regions (CNVRs) and gene is an essential process in understanding the impact of genotypic variations on phenotype. There have been many related works, but only a few attempts were made to normal populations.'),
(76962, 2012, 'bio', 'BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms', 'Sosnowski, Olivier|Charcosset, Alain|Joets, Johann', 'INRA, UMR 0320/UMR 8120 Génétique Végétale, F-91190 Gif-sur-Yvette, France.|INRA, UMR 0320/UMR 8120 Génétique Végétale, F-91190 Gif-sur-Yvette, France.|INRA, UMR 0320/UMR 8120 Génétique Végétale, F-91190 Gif-sur-Yvette, France.', 'France|France|France', 83, 'Algorithms|Chromosome Mapping|Computational Biology|Meta-Analysis as Topic|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/bts313', '22661647', 'Compilation of genetic maps combined to quantitative trait loci (QTL) meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. Despite several limitations, the software is still widely used. We developed a new version proposing additional up to date methods and improving graphical representation and exploration of large datasets.'),
(76963, 2012, 'bio', 'POOL server: machine learning application for functional site prediction in proteins', 'Somarowthu, Srinivas|Ondrechen, Mary Jo', 'Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.', 'United States|United States', 22, 'Artificial Intelligence|Computational Biology|Internet|Protein Structure, Tertiary|Proteins|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/bts321', '22661648', 'We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction.'),
(76964, 2012, 'bio', 'MobiDB: a comprehensive database of intrinsic protein disorder annotations', 'Di Domenico, Tomás|Walsh, Ian|Martin, Alberto J M|Tosatto, Silvio C E', 'Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy', 107, 'Crystallography, X-Ray|Databases, Protein|Magnetic Resonance Spectroscopy|Molecular Sequence Annotation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts327', '22661649', 'Disordered protein regions are key to the function of numerous processes within an organism and to the determination of a proteins biological role. The most common source for protein disorder annotations, DisProt, covers only a fraction of the available sequences. Alternatively, the Protein Data Bank (PDB) has been mined for missing residues in X-ray crystallographic structures. Herein, we provide a centralized source for data on different flavours of disorder in protein structures, MobiDB, building on and expanding the content provided by already existing sources. In addition to the DisProt and PDB X-ray structures, we have added experimental information from NMR structures and five different flavours of two disorder predictors (ESpritz and IUpred). These are combined into a weighted consensus disorder used to classify disordered regions into flexible and constrained disorder. Users are encouraged to submit manual annotations through a submission form. MobiDB features experimental annotations for 17 285 proteins, covering the entire PDB and predictions for the SwissProt database, with 565 200 annotated sequences. Depending on the disorder flavour, 6-20% of the residues are predicted as disordered.'),
(76965, 2012, 'bio', 'DiNuP: a systematic approach to identify regions of differential nucleosome positioning', 'Fu, Kai|Tang, Qianzi|Feng, Jianxing|Liu, X Shirley|Zhang, Yong', 'Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.', 'China|China|China|China|China', 14, 'Chromatin Assembly and Disassembly|Computational Biology|DNA, Fungal|High-Throughput Nucleotide Sequencing|Nucleosomes|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts329', '22668788', 'With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available.'),
(76966, 2012, 'bio', 'SYNCSA--R tool for analysis of metacommunities based on functional traits and phylogeny of the community components', 'Debastiani, Vanderlei J|Pillar, Valério D', 'Programa de Pós-Graduação em Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91501-970, Brazil. vanderleidebastiani@yahoo.com.br|Programa de Pós-Graduação em Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91501-970, Brazil. vanderleidebastiani@yahoo.com.br', 'Brazil|Brazil', 59, 'Biota|Computational Biology|Ecology|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bts325', '22668789', 'SYNCSA is an R package for the analysis of metacommunities based on functional traits and phylogeny of the community components. It offers tools to calculate several matrix correlations that express trait-convergence assembly patterns, trait-divergence assembly patterns and phylogenetic signal in functional traits at the species pool level and at the metacommunity level.'),
(76967, 2012, 'bio', 'Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach', 'Nascimento, Moysés|Sáfadi, Thelma|Fonseca e Silva, Fabyano|Nascimento, Ana Carolina C', 'Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-000, Brasil. moysesnascim@ufv.br|Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-000, Brasil. moysesnascim@ufv.br|Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-000, Brasil. moysesnascim@ufv.br|Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-000, Brasil. moysesnascim@ufv.br', '-|-|-|-', 8, 'Algorithms|Bayes Theorem|Cell Cycle|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/bts322', '22668790', 'In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering.'),
(76968, 2012, 'bio', 'Borrowing strength: a likelihood ratio test for related sparse signals', 'Wit, Ernst C|Bakewell, David J G', 'Johann Bernoulli Institute, University of Groningen, 9747 AG Groningen, The Netherlands. e.c.wit@rug.nl|Johann Bernoulli Institute, University of Groningen, 9747 AG Groningen, The Netherlands. e.c.wit@rug.nl', 'Netherlands|Netherlands', 2, 'Cell Line, Tumor|Computational Biology|Computer Simulation|Data Mining|Humans|Leukemia|Likelihood Functions|Models, Statistical|Monte Carlo Method|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/bts316', '22668791', 'Cancer biology is a field where the complexity of the phenomena battles against the availability of data. Often only a few observations per signal source, i.e. genes, are available. Such scenarios are becoming increasingly more relevant as modern sensing technologies generally have no trouble in measuring lots of channels, but where the number of subjects, such as patients or samples, is limited. In statistics, this problem falls under the heading large p, small n. Moreover, in such situations the use of asymptotic analytical results should generally be mistrusted.'),
(76969, 2012, 'bio', 'Genomic dark matter: the reliability of short read mapping illustrated by the genome mappability score', 'Lee, Hayan|Schatz, Michael C', 'Department of Computer Science, Stony Brook University, Stony Brook, NY, USA. hlee@cshl.edu|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA. hlee@cshl.edu', 'United States|United States', 69, 'Algorithms|Animals|Chromosome Mapping|Computational Biology|Computer Simulation|Drosophila melanogaster|Genomics|Humans|Mice|Reproducibility of Results|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts330', '22668792', 'Genome resequencing and short read mapping are two of the primary tools of genomics and are used for many important applications. The current state-of-the-art in mapping uses the quality values and mapping quality scores to evaluate the reliability of the mapping. These attributes, however, are assigned to individual reads and do not directly measure the problematic repeats across the genome. Here, we present the Genome Mappability Score (GMS) as a novel measure of the complexity of resequencing a genome. The GMS is a weighted probability that any read could be unambiguously mapped to a given position and thus measures the overall composition of the genome itself.'),
(76970, 2012, 'bio', 'NetCmpt: a network-based tool for calculating the metabolic competition between bacterial species', 'Kreimer, Anat|Doron-Faigenboim, Adi|Borenstein, Elhanan|Freilich, Shiri', 'Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. anat.kreimer@gmail.com|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. anat.kreimer@gmail.com|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. anat.kreimer@gmail.com|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. anat.kreimer@gmail.com', 'United States|United States|United States|United States', 21, 'Algorithms|Bacteria|Computational Biology|Internet|Metabolic Networks and Pathways|Models, Biological|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/bts323', '22668793', 'NetCmpt is a tool for calculating the competitive potential between pairs of bacterial species. The score describes the effective metabolic overlap (EMO) between two species, derived from analyzing the topology of the corresponding metabolic models. NetCmpt is based on the EMO algorithm, developed and validated in previous studies. It takes as input lists of species-specific enzymatic reactions (EC numbers) and generates a matrix of the potential competition scores between all pairwise combinations.'),
(76971, 2012, 'bio', 'Empirical Bayes conditional independence graphs for regulatory network recovery', 'Mahdi, Rami|Madduri, Abishek S|Wang, Guoqing|Strulovici-Barel, Yael|Salit, Jacqueline|Hackett, Neil R|Crystal, Ronald G|Mezey, Jason G', 'Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com|Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Humans|Respiratory Mucosa|Software', 'Journal Article', '10.1093/bioinformatics/bts312', '22685074', 'Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.'),
(76972, 2012, 'bio', 'Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations', 'Gray, Vanessa E|Kukurba, Kimberly R|Kumar, Sudhir', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.', 'United States|United States|United States', 40, 'Amino Acids|Computational Biology|Databases, Protein|Molecular Sequence Annotation|Mutagenesis, Site-Directed|Mutation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts336', '22685075', 'Site-directed mutagenesis is frequently used by scientists to investigate the functional impact of amino acid mutations in the laboratory. Over 10,000 such laboratory-induced mutations have been reported in the UniProt database along with the outcomes of functional assays. Here, we explore the performance of state-of-the-art computational tools (Condel, PolyPhen-2 and SIFT) in correctly annotating the function-altering potential of 10,913 laboratory-induced mutations from 2372 proteins. We find that computational tools are very successful in diagnosing laboratory-induced mutations that elicit significant functional change in the laboratory (up to 92% accuracy). But, these tools consistently fail in correctly annotating laboratory-induced mutations that show no functional impact in the laboratory assays. Therefore, the overall accuracy of computational tools for laboratory-induced mutations is much lower than that observed for the naturally occurring human variants. We tested and rejected the possibilities that the preponderance of changes to alanine and the presence of multiple base-pair mutations in the laboratory were the reasons for the observed discordance between the performance of computational tools for natural and laboratory mutations. Instead, we discover that the laboratory-induced mutations occur predominately at the highly conserved positions in proteins, where the computational tools have the lowest accuracy of correct prediction for variants that do not impact function (neutral). Therefore, the comparisons of experimental-profiling results with those from computational predictions need to be sensitive to the evolutionary conservation of the positions harboring the amino acid change.'),
(76973, 2012, 'bio', 'ETAscape: analyzing protein networks to predict enzymatic function and substrates in Cytoscape', 'Bachman, Benjamin J|Venner, Eric|Lua, Rhonald C|Erdin, Serkan|Lichtarge, Olivier', 'Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 6, 'Computational Biology|Enzymes|Genomics|Proteins|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/bts331', '22689386', 'Most proteins lack experimentally validated functions. To address this problem, we implemented the Evolutionary Trace Annotation (ETA) method in the Cytoscape network visualization environment. The result is the ETAscape plugin, which builds a structural genomics network based on local structural and evolutionary similarities among proteins and then globally diffuses known annotations across the resulting network. The plugin displays these novel functional annotations, their confidence, the molecular basis for individual matches and the set of matches that lead to a prediction.'),
(76974, 2012, 'bio', 'SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing', 'Sun, Zhifu|Baheti, Saurabh|Middha, Sumit|Kanwar, Rahul|Zhang, Yuji|Li, Xing|Beutler, Andreas S|Klee, Eric|Asmann, Yan W|Thompson, E Aubrey|Kocher, Jean-Pierre A', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. sun.zhifu@mayo.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|CpG Islands|DNA Methylation|Genomics|Molecular Sequence Annotation|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/bts337', '22689387', 'Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that integrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76975, 2012, 'bio', 'synbreed: a framework for the analysis of genomic prediction data using R', 'Wimmer, Valentin|Albrecht, Theresa|Auinger, Hans-Jürgen|Schön, Chris-Carolin', 'Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany.|Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany.|Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany.|Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Simulation|Electronic Data Processing|Genomics|Genotyping Techniques|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/bts335', '22689388', 'We present a novel R package named synbreed to derive genome-based predictions from high-throughput genotyping and large-scale phenotyping data. The package contains a comprehensive collection of functions required to fit and cross-validate genomic prediction models. All functions are embedded within the framework of a single, unified data object. Thereby a versatile genomic prediction analysis pipeline covering data processing, visualization and analysis is established within one software package. The implementation is flexible with respect to a wide range of data formats and models. The package fills an existing gap in the availability of user-friendly software for next-generation genetics research and education.'),
(76976, 2012, 'bio', 'BatMis: a fast algorithm for k-mismatch mapping', 'Tennakoon, Chandana|Purbojati, Rikky W|Sung, Wing-Kin', 'NUS Graduate School for Integrative Sciences and Engineering, CeLS #05-01, 28 Medical Drive, Singapore 117456, Singapore.|NUS Graduate School for Integrative Sciences and Engineering, CeLS #05-01, 28 Medical Drive, Singapore 117456, Singapore.|NUS Graduate School for Integrative Sciences and Engineering, CeLS #05-01, 28 Medical Drive, Singapore 117456, Singapore.', 'Singapore|Singapore|Singapore', 20, 'Algorithms|Computational Biology|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts339', '22689389', 'Second-generation sequencing (SGS) generates millions of reads that need to be aligned to a reference genome allowing errors. Although current aligners can efficiently map reads allowing a small number of mismatches, they are not well suited for handling a large number of mismatches. The efficiency of aligners can be improved using various heuristics, but the sensitivity and accuracy of the alignments are sacrificed. In this article, we introduce Basic Alignment tool for Mismatches (BatMis)--an efficient method to align short reads to a reference allowing k mismatches. BatMis is a Burrows-Wheeler transformation based aligner that uses a seed and extend approach, and it is an exact method.'),
(76977, 2012, 'bio', 'ISMB 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, CA, USA Editorial', 'Berger, Bonnie', 'NUS Graduate School for Integrative Sciences and Engineering, CeLS #05-01, 28 Medical Drive, Singapore 117456, Singapore.', 'Singapore', 0, 'Computational Biology|Congresses as Topic|Diagnostic Imaging|Gene Expression Profiling|Genomics|Molecular Biology|Protein Interaction Maps|Sequence Analysis', 'Editorial', '10.1093/bioinformatics/bts281', '22689748', '-'),
(76978, 2012, 'bio', 'Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores', 'Hochbaum, Dorit S|Hsu, Chun-Nan|Yang, Yan T', 'Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA.|Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA.|Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA.', 'United States|United States|United States', 6, 'Animals|CHO Cells|Cricetinae|Drug Discovery|Pharmaceutical Preparations|Support Vector Machine', 'Comparative Study', '10.1093/bioinformatics/bts232', '22689749', 'The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut-normalized cut prime (FABS-NC()), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.'),
(76979, 2012, 'bio', 'Statistical model-based testing to evaluate the recurrence of genomic aberrations', 'Niida, Atushi|Imoto, Seiya|Shimamura, Teppei|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aniida@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aniida@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aniida@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aniida@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan', 0, 'Cell Line, Tumor|Chromosome Aberrations|Genes, Neoplasm|Genomics|Humans|Models, Statistical|Neoplasms|Recurrence', 'Journal Article', '10.1093/bioinformatics/bts203', '22689750', 'In cancer genomes, chromosomal regions harboring cancer genes are often subjected to genomic aberrations like copy number alteration and loss of heterozygosity. Given this, finding recurrent genomic aberrations is considered an apt approach for screening cancer genes. Although several permutation-based tests have been proposed for this purpose, none of them are designed to find recurrent aberrations from the genomic dataset without paired normal sample controls. Their application to unpaired genomic data may lead to false discoveries, because they retrieve pseudo-aberrations that exist in normal genomes as polymorphisms.'),
(76980, 2012, 'bio', 'Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease', 'Patel, Chirag J|Chen, Rong|Butte, Atul J', 'Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.', 'United States|United States|United States', 25, 'Computational Biology|Databases, Genetic|Diabetes Mellitus, Type 2|Environment|Gene-Environment Interaction|Genome-Wide Association Study|Humans|Toxicogenetics', 'Journal Article', '10.1093/bioinformatics/bts229', '22689751', 'Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possible interaction in context of disease. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies. Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D.'),
(76981, 2012, 'bio', 'Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning', 'Wang, Hua|Nie, Feiping|Huang, Heng|Risacher, Shannon L|Saykin, Andrew J|Shen, Li|Alzheimers Disease Neuroimaging Initiative', 'Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Alzheimer Disease|Artificial Intelligence|Biomarkers|Brain|Cognition Disorders|Genotype|Humans|Neuroimaging|Phenotype|Quantitative Trait Loci|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bts228', '22689752', 'Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.'),
(76982, 2012, 'bio', 'Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs', 'Lee, Seunghak|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States', 35, 'Algorithms|Chromosome Mapping|Computational Biology|Epistasis, Genetic|Genome|Genome-Wide Association Study|Genotype|Humans|Linear Models|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/bts227', '22689753', 'As many complex disease and expression phenotypes are the outcome of intricate perturbation of molecular networks underlying gene regulation resulted from interdependent genome variations, association mapping of causal QTLs or expression quantitative trait loci must consider both additive and epistatic effects of multiple candidate genotypes. This problem poses a significant challenge to contemporary genome-wide-association (GWA) mapping technologies because of its computational complexity. Fortunately, a plethora of recent developments in biological network community, especially the availability of genetic interaction networks, make it possible to construct informative priors of complex interactions between genotypes, which can substantially reduce the complexity and increase the statistical power of GWA inference.'),
(76983, 2012, 'bio', 'Incorporating prior information into association studies', 'Darnell, Gregory|Duong, Dat|Han, Buhm|Eskin, Eleazar', 'Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|Gene Frequency|Genetic Variation|Genome-Wide Association Study|HapMap Project|Humans|Likelihood Functions|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/bts235', '22689754', 'Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power.'),
(76984, 2012, 'bio', 'DELISHUS: an efficient and exact algorithm for genome-wide detection of deletion polymorphism in autism', 'Aguiar, Derek|Halldórsson, Bjarni V|Morrow, Eric M|Istrail, Sorin', 'Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.', 'United States|United States|United States|United States', 3, 'Algorithms|Autistic Disorder|Computational Biology|DNA Copy Number Variations|Genome-Wide Association Study|Genotype|Humans|Inheritance Patterns|Phenotype|Polymorphism, Single Nucleotide|Sequence Deletion', 'Journal Article', '10.1093/bioinformatics/bts234', '22689755', 'The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A considerable portion of autism appears to be correlated with copy number variation, which is not directly probed by single nucleotide polymorphism (SNP) array or sequencing technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem partly due to the inability of algorithms to detect them.'),
(76985, 2012, 'bio', 'Joint stage recognition and anatomical annotation of Drosophila gene expression patterns', 'Cai, Xiao|Wang, Hua|Huang, Heng|Ding, Chris', 'Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.', 'United States|United States|United States|United States', 15, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Genome, Insect|Image Processing, Computer-Assisted|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/bts220', '22689756', 'Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a Drosophila melanogaster embryo delivers the detailed spatio-temporal patterns of the gene expression. Many related biological problems such as the detection of co-expressed genes, co-regulated genes and transcription factor binding motifs rely heavily on the analysis of these image patterns. To provide the text-based pattern searching for facilitating related biological studies, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with developmental stage term and anatomical ontology terms manually by domain experts. Due to the rapid increase in the number of such images and the inevitable bias annotations by human curators, it is necessary to develop an automatic method to recognize the developmental stage and annotate anatomical terms.'),
(76986, 2012, 'bio', 'Detection of allele-specific methylation through a generalized heterogeneous epigenome model', 'Peng, Qian|Ecker, Joseph R', 'Department of Computer Science and Engineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. qpeng@cs.ucsd.edu|Department of Computer Science and Engineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. qpeng@cs.ucsd.edu', 'United States|United States', 0, 'Alleles|Arabidopsis|DNA Methylation|Epigenesis, Genetic|Epigenomics|High-Throughput Nucleotide Sequencing|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bts231', '22689757', 'High-throughput sequencing has made it possible to sequence DNA methylation of a whole genome at the single-base resolution. A sample, however, may contain a number of distinct methylation patterns. For instance, cells of different types and in different developmental stages may have different methylation patterns. Alleles may be differentially methylated, which may partially explain that the large portions of epigenomes from single cell types are partially methylated, and may have major effects on transcriptional output. Approaches relying on DNA sequence polymorphism to identify individual patterns from a mixture of heterogeneous epigenomes are insufficient as methylcytosines occur at a much higher density than SNPs.'),
(76987, 2012, 'bio', 'Xenome--a tool for classifying reads from xenograft samples', 'Conway, Thomas|Wazny, Jeremy|Bromage, Andrew|Tymms, Martin|Sooraj, Dhanya|Williams, Elizabeth D|Beresford-Smith, Bryan', 'NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au|NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 96, 'Algorithms|Animals|DNA, Complementary|Genome|Humans|Mice|Sequence Analysis, RNA|Software|Transplantation, Heterologous', 'Journal Article', '10.1093/bioinformatics/bts236', '22689758', 'Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed.'),
(76988, 2012, 'bio', 'Dissect: detection and characterization of novel structural alterations in transcribed sequences', 'Yorukoglu, Deniz|Hach, Faraz|Swanson, Lucas|Collins, Colin C|Birol, Inanc|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu|School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu|School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu|School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu|School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu|School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC, Canada. denizy@mit.edu', 'Canada|Canada|Canada|Canada|Canada|Canada', 12, 'Algorithms|Cell Line, Tumor|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Male|Models, Theoretical|Prostatic Neoplasms|RNA|Sequence Alignment|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts214', '22689759', 'Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention. In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model, a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs.'),
(76989, 2012, 'bio', 'SEQuel: improving the accuracy of genome assemblies', 'Ronen, Roy|Boucher, Christina|Chitsaz, Hamidreza|Pevzner, Pavel', 'Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States', 43, 'Algorithms|Computational Biology|Contig Mapping|Escherichia coli|Genome, Bacterial|INDEL Mutation|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts219', '22689760', 'Assemblies of next-generation sequencing (NGS) data, although accurate, still contain a substantial number of errors that need to be corrected after the assembly process. We develop SEQuel, a tool that corrects errors (i.e. insertions, deletions and substitution errors) in the assembled contigs. Fundamental to the algorithm behind SEQuel is the positional de Bruijn graph, a graph structure that models k-mers within reads while incorporating the approximate positions of reads into the model.'),
(76990, 2012, 'bio', 'Weighted pooling--practical and cost-effective techniques for pooled high-throughput sequencing', 'Golan, David|Erlich, Yaniv|Rosset, Saharon', 'School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel', 12, 'Alleles|Computational Biology|DNA|Genetic Carrier Screening|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts208', '22689761', 'Despite the rapid decline in sequencing costs, sequencing large cohorts of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of pools, thus dramatically reducing the cost of such large-scale sequencing projects. These approaches use combinatorial pooling designs where each individual is either present or absent from a pool. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers.'),
(76991, 2012, 'bio', 'Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure', 'Reinharz, Vladimir|Major, François|Waldispühl, Jérôme', 'School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, Canada.|School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, Canada.|School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, Canada.', 'Canada|Canada|Canada', 31, 'Algorithms|Base Pairing|Databases, Nucleic Acid|Models, Theoretical|Nucleic Acid Conformation|Nucleotide Motifs|RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts226', '22689763', 'The prediction of RNA 3D structures from its sequence only is a milestone to RNA function analysis and prediction. In recent years, many methods addressed this challenge, ranging from cycle decomposition and fragment assembly to molecular dynamics simulations. However, their predictions remain fragile and limited to small RNAs. To expand the range and accuracy of these techniques, we need to develop algorithms that will enable to use all the structural information available. In particular, the energetic contribution of secondary structure interactions is now well documented, but the quantification of non-canonical interactions-those shaping the tertiary structure-is poorly understood. Nonetheless, even if a complete RNA tertiary structure energy model is currently unavailable, we now have catalogues of local 3D structural motifs including non-canonical base pairings. A practical objective is thus to develop techniques enabling us to use this knowledge for robust RNA tertiary structure predictors.'),
(76992, 2012, 'bio', 'Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers', 'Hoinka, Jan|Zotenko, Elena|Friedman, Adam|Sauna, Zuben E|Przytycka, Teresa M', 'National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States|United States', 47, 'Algorithms|Aptamers, Nucleotide|Computational Biology|Nucleic Acid Conformation|Nucleic Acids|Nucleotide Motifs|SELEX Aptamer Technique', 'Journal Article', '10.1093/bioinformatics/bts210', '22689764', 'Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process.'),
(76993, 2012, 'bio', 'GraphClust: alignment-free structural clustering of local RNA secondary structures', 'Heyne, Steffen|Costa, Fabrizio|Rose, Dominic|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg,Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg,Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg,Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg,Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.', 'Germany|Germany|Germany|Germany', 46, 'Algorithms|Animals|Base Sequence|Cluster Analysis|Computational Biology|Humans|Models, Theoretical|Nucleic Acid Conformation|Nucleotide Motifs|RNA, Long Noncoding|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bts224', '22689765', 'Clustering according to sequence-structure similarity has now become a generally accepted scheme for ncRNA annotation. Its application to complete genomic sequences as well as whole transcriptomes is therefore desirable but hindered by extremely high computational costs.'),
(76994, 2012, 'bio', 'Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks', 'Penfold, Christopher A|Buchanan-Wollaston, Vicky|Denby, Katherine J|Wild, David L', 'Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 36, 'Algorithms|Arabidopsis|Bayes Theorem|Gene Expression Regulation|Gene Regulatory Networks|Models, Theoretical|Statistics, Nonparametric|Transcription Factors|Two-Hybrid System Techniques', 'Journal Article', '10.1093/bioinformatics/bts222', '22689766', 'The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions, that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets.'),
(76995, 2012, 'bio', 'NORMAL: accurate nucleosome positioning using a modified Gaussian mixture model', 'Polishko, Anton|Ponts, Nadia|Le Roch, Karine G|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. polishka@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. polishka@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. polishka@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. polishka@cs.ucr.edu', 'United States|United States|United States|United States', 27, 'Algorithms|DNA|Models, Statistical|Normal Distribution|Nucleosomes|Plasmodium falciparum|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts206', '22689767', 'Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome, then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or fuzzy in the sequenced cell sample.'),
(76996, 2012, 'bio', 'Improved synapse detection for mGRASP-assisted brain connectivity mapping', 'Feng, Linqing|Zhao, Ting|Kim, Jinhyun', 'Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, Korea.|Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, Korea.|Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, Korea.', '-|-|-', 12, 'Animals|Bayes Theorem|Brain Mapping|Computational Biology|Green Fluorescent Proteins|Image Processing, Computer-Assisted|Mice|Microscopy|Nerve Net|Neurons|Software|Synapses', 'Journal Article', '10.1093/bioinformatics/bts221', '22689768', 'A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy.'),
(76997, 2012, 'bio', 'Lineage-based identification of cellular states and expression programs', 'Hashimoto, Tatsunori|Jaakkola, Tommi|Sherwood, Richard|Mazzoni, Esteban O|Wichterle, Hynek|Gifford, David', 'Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States|United States|United States', 2, 'Algorithms|Animals|Cell Differentiation|Computational Biology|Linear Models|Mice|Validation Studies as Topic', 'Comparative Study', '10.1093/bioinformatics/bts204', '22689769', 'We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L(1) that based methods controls the parameters in three distinct ways: the number of genes change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization, techniques, such as singular value decomposition and non-negative matrix factorization show that our method provides higher predictive power in held, out tests while inducing sparse and biologically relevant gene sets.'),
(76998, 2012, 'bio', 'Matching experiments across species using expression values and textual information', 'Wise, Aaron|Oltvai, Zoltán N|Bar-Joseph, Ziv', 'Lane Center for Computational Biology, Carnegie Mellon University Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 2, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Gene Expression Profiling|Genomics|Humans|Mice|Oligonucleotide Array Sequence Analysis|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bts205', '22689770', 'With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease-related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species.'),
(76999, 2012, 'bio', 'Fast alignment of fragmentation trees', 'Hufsky, Franziska|Dührkop, Kai|Rasche, Florian|Chimani, Markus|Böcker, Sebastian', 'Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Factual|Mass Spectrometry|Metabolomics', 'Journal Article', '10.1093/bioinformatics/bts207', '22689771', 'Mass spectrometry allows sensitive, automated and high-throughput analysis of small molecules such as metabolites. One major bottleneck in metabolomics is the identification of unknown small molecules not in any database. Recently, fragmentation tree alignments have been introduced for the automated comparison of the fragmentation patterns of small molecules. Fragmentation pattern similarities are strongly correlated with the chemical similarity of the molecules, and allow us to cluster compounds based solely on their fragmentation patterns.'),
(77000, 2012, 'bio', 'DACTAL: divide-and-conquer trees (almost) without alignments', 'Nelesen, Serita|Liu, Kevin|Wang, Li-San|Linder, C Randal|Warnow, Tandy', 'Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA.|Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA.|Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA.|Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA.|Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Likelihood Functions|Phylogeny|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/bts218', '22689772', 'While phylogenetic analyses of datasets containing 1000-5000 sequences are challenging for existing methods, the estimation of substantially larger phylogenies poses a problem of much greater complexity and scale.'),
(77001, 2012, 'bio', 'Efficient algorithms for the reconciliation problem with gene duplication, horizontal transfer and loss', 'Bansal, Mukul S|Alm, Eric J|Kellis, Manolis', 'Computer Science and Artificial Intelligence Laboratory, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mukul@csail.mit.edu|Computer Science and Artificial Intelligence Laboratory, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mukul@csail.mit.edu|Computer Science and Artificial Intelligence Laboratory, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mukul@csail.mit.edu', 'United States|United States|United States', 114, 'Algorithms|Evolution, Molecular|Gene Deletion|Gene Duplication|Gene Transfer, Horizontal|Genomics|Multigene Family|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bts225', '22689773', 'Gene family evolution is driven by evolutionary events such as speciation, gene duplication, horizontal gene transfer and gene loss, and inferring these events in the evolutionary history of a given gene family is a fundamental problem in comparative and evolutionary genomics with numerous important applications. Solving this problem requires the use of a reconciliation framework, where the input consists of a gene family phylogeny and the corresponding species phylogeny, and the goal is to reconcile the two by postulating speciation, gene duplication, horizontal gene transfer and gene loss events. This reconciliation problem is referred to as duplication-transfer-loss (DTL) reconciliation and has been extensively studied in the literature. Yet, even the fastest existing algorithms for DTL reconciliation are too slow for reconciling large gene families and for use in more sophisticated applications such as gene tree or species tree reconstruction.'),
(77002, 2012, 'bio', 'Extending ontologies by finding siblings using set expansion techniques', 'Fabian, Götz|Wächter, Thomas|Schroeder, Michael', 'Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062 Dresden, Germany. goetz.fabian@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062 Dresden, Germany. goetz.fabian@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062 Dresden, Germany. goetz.fabian@biotec.tu-dresden.de', 'Germany|Germany|Germany', 0, 'Algorithms|Data Mining|Internet|Medical Subject Headings|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/bts215', '22689774', 'Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text-based ontologies level by level.'),
(77003, 2012, 'bio', 'Protein subcellular location pattern classification in cellular images using latent discriminative models', 'Li, Jieyue|Xiong, Liang|Schneider, Jeff|Murphy, Robert F', 'Center for Bioimage informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.|Center for Bioimage informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.|Center for Bioimage informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.|Center for Bioimage informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Humans|Image Processing, Computer-Assisted|Logistic Models|Microscopy, Fluorescence|Organelles|Proteins|Software|Spatial Analysis|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bts230', '22689776', 'Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins, each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood.'),
(77004, 2012, 'bio', 'How networks change with time', 'Park, Yongjin|Bader, Joel S', 'Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States', 24, 'Algorithms|Animals|Bayes Theorem|Cluster Analysis|Computational Biology|Drosophila|Gene Expression Profiling|Likelihood Functions|Mitochondria|Nuclear Pore|Protein Interaction Mapping|Protein Interaction Maps|Ribosomes|Saccharomyces cerevisiae|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/bts211', '22689777', 'Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals.'),
(77005, 2012, 'bio', 'A single source k-shortest paths algorithm to infer regulatory pathways in a gene network', 'Shih, Yu-Keng|Parthasarathy, Srinivasan', 'Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Gene Regulatory Networks|Models, Theoretical|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts212', '22689778', 'Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77006, 2012, 'bio', 'A conditional neural fields model for protein threading', 'Ma, Jianzhu|Peng, Jian|Wang, Sheng|Xu, Jinbo', 'Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.', 'United States|United States|United States|United States', 40, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Computational Biology|Likelihood Functions|Protein Structure, Secondary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bts213', '22689779', 'Alignment errors are still the main bottleneck for current template-based protein modeling (TM) methods, including protein threading and homology modeling, especially when the sequence identity between two proteins under consideration is low (<30%).'),
(77007, 2012, 'bio', 'Novel domain combinations in proteins encoded by chimeric transcripts', 'Frenkel-Morgenstern, Milana|Valencia, Alfonso', 'Structural Biology and BioComputing Program, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Structural Biology and BioComputing Program, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.', 'Spain|Spain', 0, 'Humans|Mutant Chimeric Proteins|Oncogene Proteins, Fusion|Protein Interaction Domains and Motifs|Protein Interaction Maps|RNA|Sequence Analysis, RNA|Trans-Splicing', 'Journal Article', '10.1093/bioinformatics/bts216', '22689780', 'Chimeric RNA transcripts are generated by different mechanisms including pre-mRNA trans-splicing, chromosomal translocations and/or gene fusions. It was shown recently that at least some of chimeric transcripts can be translated into functional chimeric proteins.'),
(77008, 2012, 'bio', 'GenomeRing: alignment visualization based on SuperGenome coordinates', 'Herbig, A|Jäger, G|Battke, F|Nieselt, K', 'Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Campylobacter jejuni|Genomic Islands|Genomics|Helicobacter pylori|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bts217', '22689781', 'The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.'),
(77009, 2012, 'bio', 'MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins', 'Disfani, Fatemeh Miri|Hsu, Wei-Lun|Mizianty, Marcin J|Oldfield, Christopher J|Xue, Bin|Dunker, A Keith|Uversky, Vladimir N|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Amino Acids|Binding Sites|Computational Biology|Hydrophobic and Hydrophilic Interactions|Molecular Sequence Annotation|Protein Structure, Secondary|Proteins|Sequence Alignment|Support Vector Machine', 'Comparative Study', '10.1093/bioinformatics/bts209', '22689782', 'Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains.'),
(77010, 2012, 'bio', 'Recognition models to predict DNA-binding specificities of homeodomain proteins', 'Christensen, Ryan G|Enuameh, Metewo Selase|Noyes, Marcus B|Brodsky, Michael H|Wolfe, Scot A|Stormo, Gary D', 'Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.', 'United States|United States|United States|United States|United States|United States', 28, 'Algorithms|Amino Acid Sequence|Animals|Artificial Intelligence|Binding Sites|Computational Biology|DNA|Drosophila|Homeodomain Proteins|Humans|Mice|Models, Statistical|Sequence Alignment|Support Vector Machine|Transcription Factors|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/bts202', '22689783', 'Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes.'),
(77011, 2012, 'bio', 'Ranking models of transmembrane xcexb2-barrel proteins using Z-coordinate predictions', 'Hayat, Sikander|Elofsson, Arne', 'Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Science for Life Laboratory, Swedish E-science Research Center, Stockholm University, SE-10691 Stockholm, Sweden.|Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Science for Life Laboratory, Swedish E-science Research Center, Stockholm University, SE-10691 Stockholm, Sweden.', 'Sweden|Sweden', 7, 'Computational Biology|Membrane Proteins|Models, Molecular|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/bts233', '22689784', 'Transmembrane xcexb2-barrels exist in the outer membrane of gram-negative bacteria as well as in chloroplast and mitochondria. They are often involved in transport processes and are promising antimicrobial drug targets. Structures of only a few xcexb2-barrel protein families are known. Therefore, a method that could automatically generate such models would be valuable. The symmetrical arrangement of the barrels suggests that an approach based on idealized geometries may be successful.'),
(77012, 2012, 'bio', 'Minimum message length inference of secondary structure from protein coordinate data', 'Konagurthu, Arun S|Lesk, Arthur M|Allison, Lloyd', 'Clayton School of Information Technology, Monash University, Clayton VIC 3800, Australia. arun.konagurthu@monash.edu|Clayton School of Information Technology, Monash University, Clayton VIC 3800, Australia. arun.konagurthu@monash.edu|Clayton School of Information Technology, Monash University, Clayton VIC 3800, Australia. arun.konagurthu@monash.edu', 'Australia|Australia|Australia', 0, 'Algorithms|Bayes Theorem|Computational Biology|Models, Theoretical|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1093/bioinformatics/bts223', '22689785', 'Secondary structure underpins the folding pattern and architecture of most proteins. Accurate assignment of the secondary structure elements is therefore an important problem. Although many approximate solutions of the secondary structure assignment problem exist, the statement of the problem has resisted a consistent and mathematically rigorous definition. A variety of comparative studies have highlighted major disagreements in the way the available methods define and assign secondary structure to coordinate data.'),
(77013, 2012, 'bio', 'mcaGUI: microbial community analysis R-Graphical User Interface (GUI)', 'Copeland, Wade K|Krishnan, Vandhana|Beck, Daniel|Settles, Matt|Foster, James A|Cho, Kyu-Chul|Day, Mitch|Hickey, Roxana|Schütte, Ursel M E|Zhou, Xia|Williams, Christopher J|Forney, Larry J|Abdo, Zaid', 'Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com|Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA. wade@kingcopeland.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Biodiversity|Cluster Analysis|Computer Graphics|Metagenome|Multivariate Analysis|Principal Component Analysis|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts338', '22692220', 'Microbial communities have an important role in natural ecosystems and have an impact on animal and human health. Intuitive graphic and analytical tools that can facilitate the study of these communities are in short supply. This article introduces Microbial Community Analysis GUI, a graphical user interface (GUI) for the R-programming language (R Development Core Team, 2010). With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. This GUI provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future.'),
(77014, 2012, 'bio', 'SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides', 'Zhang, Lida|Huang, Yuyi|Zou, Zehong|He, Ying|Chen, Ximo|Tao, Ailin', 'Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.|Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.|Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.|Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.|Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.|Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.', 'China|China|China|China|China|China', 17, 'Algorithms|Allergens|Amino Acid Sequence|Peptides|Sensitivity and Specificity|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bts326', '22692221', 'SORTALLER is an online allergen classifier based on allergen family featured peptide (AFFP) dataset and normalized BLAST E-values, which establish the featured vectors for support vector machine (SVM). AFFPs are allergen-specific peptides panned from irredundant allergens and harbor perfect information with noise fragments eliminated because of their similarity to non-allergens. SORTALLER performed significantly better than other existing software and reached a perfect balance with high specificity (98.4%) and sensitivity (98.6%) for discriminating allergenic proteins from several independent datasets of protein sequences of diverse sources, also highlighting with the Matthews correlation coefficient (MCC) as high as 0.970, fast running speed and rapidly predicting a batch of amino acid sequences with a single click.'),
(77015, 2012, 'bio', 'Associating microbiome composition with environmental covariates using generalized UniFrac distances', 'Chen, Jun|Bittinger, Kyle|Charlson, Emily S|Hoffmann, Christian|Lewis, James|Wu, Gary D|Collman, Ronald G|Bushman, Frederic D|Li, Hongzhe', 'Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 329, 'Bacteria|Cluster Analysis|Computational Biology|Computer Simulation|Cross-Sectional Studies|Diet|Environment|Humans|Metagenome|Models, Statistical|Monte Carlo Method|Smoking', 'Comparative Study', '10.1093/bioinformatics/bts342', '22711789', 'The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages.'),
(77016, 2012, 'bio', 'MolClass: a web portal to interrogate diverse small molecule screen datasets with different computational models', 'Wildenhain, Jan|Fitzgerald, Nicholas|Tyers, Mike', 'Wellcome Trust Centre for Cell Biology and School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3JR, UK. jan.wildenhain@ed.ac.uk|Wellcome Trust Centre for Cell Biology and School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3JR, UK. jan.wildenhain@ed.ac.uk|Wellcome Trust Centre for Cell Biology and School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3JR, UK. jan.wildenhain@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Artificial Intelligence|Biochemical Phenomena|Computational Biology|Computer Simulation|Databases, Factual|Internet|Software', 'Journal Article', '10.1093/bioinformatics/bts349', '22711790', 'The MolClass toolkit and data portal generate computational models from user-defined small molecule datasets based on structural features identified in hit and non-hit molecules in different screens. Each new model is applied to all datasets in the database to classify compound specificity. MolClass thus defines a likelihood value for each compound entry and creates an activity fingerprint across diverse sets of screens. MolClass uses a variety of machine-learning methods to find molecular patterns and can therefore also assign a priori predictions of bioactivities for previously untested molecules. The power of the MolClass resource will grow as a function of the number of screens deposited in the database.'),
(77017, 2012, 'bio', 'Bluues server: electrostatic properties of wild-type and mutated protein structures', 'Walsh, Ian|Minervini, Giovanni|Corazza, Alessandra|Esposito, Gennaro|Tosatto, Silvio C E|Fogolari, Federico', 'Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 42, 'Internet|Kinetics|Mutant Proteins|Point Mutation|Proteins|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/bts343', '22711791', 'Electrostatic calculations are an important tool for deciphering many functional mechanisms in proteins. Generalized Born (GB) models offer a fast and convenient computational approximation over other implicit solvent-based electrostatic models. Here we present a novel GB-based web server, using the program Bluues, to calculate numerous electrostatic features including pKa-values and surface potentials. The output is organized allowing both experts and beginners to rapidly sift the data. A novel feature of the Bluues server is that it explicitly allows to find electrostatic differences between wild-type and mutant structures.'),
(77018, 2012, 'bio', 'Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model', 'Abate, Francesco|Acquaviva, Andrea|Paciello, Giulia|Foti, Carmelo|Ficarra, Elisa|Ferrarini, Alberto|Delledonne, Massimo|Iacobucci, Ilaria|Soverini, Simona|Martinelli, Giovanni|Macii, Enrico', 'Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it|Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 25, 'Algorithms|Computational Biology|Gene Fusion|Humans|Leukemia, Myelogenous, Chronic, BCR-ABL Positive|RNA|RNA Splicing|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts334', '22711792', 'Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model.'),
(77019, 2012, 'bio', 'Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics', 'Hoehndorf, Robert|Dumontier, Michel|Gkoutos, Georgios V', 'Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. rh497@cam.ac.uk|Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. rh497@cam.ac.uk|Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. rh497@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 33, 'Databases, Factual|Humans|Information Storage and Retrieval|Knowledge Bases|Pharmacogenetics|Software|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bts350', '22711793', 'Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large-scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several pharmacogenomics databases. An integrated analysis of the information in these databases can reveal disease pathways and facilitate novel biomedical analyses.'),
(77020, 2012, 'bio', 'Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification', 'Zhang, Z|Guillaume, F|Sartelet, A|Charlier, C|Georges, M|Farnir, F|Druet, T', 'Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.|Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 25, 'Animals|Cattle|Chromosome Mapping|Computational Biology|Computer Simulation|Haplotypes|Linear Models|Male|Markov Chains|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts348', '22711794', 'In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model.'),
(77021, 2012, 'bio', 'BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events', 'Gerner, Martin|Sarafraz, Farzaneh|Bergman, Casey M|Nenadic, Goran', 'Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. martin.gerner@gmail.com|Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. martin.gerner@gmail.com|Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. martin.gerner@gmail.com|Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. martin.gerner@gmail.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Biochemical Phenomena|Computational Biology|Data Mining|MEDLINE|PubMed|Software', 'Journal Article', '10.1093/bioinformatics/bts332', '22711795', 'Although the amount of data in biology is rapidly increasing, critical information for understanding biological events like phosphorylation or gene expression remains locked in the biomedical literature. Most current text mining (TM) approaches to extract information about biological events are focused on either limited-scale studies and/or abstracts, with data extracted lacking context and rarely available to support further research.'),
(77022, 2012, 'bio', 'ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality', 'Szalay-Beko, Máté|Palotai, Robin|Szappanos, Balázs|Kovács, István A|Papp, Balázs|Csermely, Péter', 'Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest 1444, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 68, 'Algorithms|Color|Computational Biology|Metabolic Networks and Pathways|Protein Interaction Maps|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts352', '22718784', 'The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks.'),
(77023, 2012, 'bio', 'Topology of functional networks predicts physical binding of proteins', 'Saraç, Omer Sinan|Pancaldi, Vera|Bähler, Jürg|Beyer, Andreas', 'Biotechnology Center, Technische Universitt Dresden, D-01062 Dresden, Germany.|Biotechnology Center, Technische Universitt Dresden, D-01062 Dresden, Germany.|Biotechnology Center, Technische Universitt Dresden, D-01062 Dresden, Germany.|Biotechnology Center, Technische Universitt Dresden, D-01062 Dresden, Germany.', 'Germany|Germany|Germany|Germany', 4, 'Area Under Curve|Artificial Intelligence|Computational Biology|Humans|Protein Binding|Protein Interaction Mapping|Proteins|ROC Curve|Saccharomyces cerevisiae|Schizosaccharomyces|Software', 'Journal Article', '10.1093/bioinformatics/bts351', '22718785', 'It has been recognized that the topology of molecular networks provides information about the certainty and nature of individual interactions. Thus, network motifs have been used for predicting missing links in biological networks and for removing false positives. However, various different measures can be inferred from the structure of a given network and their predictive power varies depending on the task at hand.'),
(77024, 2012, 'bio', 'ChromoHub: a data hub for navigators of chromatin-mediated signalling', 'Liu, Lihua|Zhen, Xi Ting|Denton, Emily|Marsden, Brian D|Schapira, Matthieu', 'Structural Genomics Consortium, University of Toronto, Toronto, ON M5G1L7, Canada.|Structural Genomics Consortium, University of Toronto, Toronto, ON M5G1L7, Canada.|Structural Genomics Consortium, University of Toronto, Toronto, ON M5G1L7, Canada.|Structural Genomics Consortium, University of Toronto, Toronto, ON M5G1L7, Canada.|Structural Genomics Consortium, University of Toronto, Toronto, ON M5G1L7, Canada.', 'Canada|Canada|Canada|Canada|Canada', 58, 'Chromatin|Databases, Genetic|Epigenesis, Genetic|Gene Expression Regulation|Histone Code|Histone Deacetylases|Histones|Phylogeny|Protein Methyltransferases|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts340', '22718786', 'The rapidly increasing research activity focused on chromatin-mediated regulation of epigenetic mechanisms is generating waves of data on writers, readers and erasers of the histone code, such as protein methyltransferases, bromodomains or histone deacetylases. To make these data easily accessible to communities of research scientists coming from diverse horizons, we have created ChromoHub, an online resource where users can map on phylogenetic trees disease associations, protein structures, chemical inhibitors, histone substrates, chromosomal aberrations and other types of data extracted from public repositories and the published literature. The interface can be used to define the structural or chemical coverage of a protein family, highlight domain architectures, interrogate disease relevance or zoom in on specific genes for more detailed information. This open-access resource should serve as a hub for cell biologists, medicinal chemists, structural biologists and other navigators that explore the biology of chromatin signalling.'),
(77025, 2012, 'bio', 'miRcode: a map of putative microRNA target sites in the long non-coding transcriptome', 'Jeggari, Ashwini|Marks, Debora S|Larsson, Erik', 'Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden.|Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden.|Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden.', 'Sweden|Sweden|Sweden', 303, 'Computational Biology|MicroRNAs|RNA, Long Noncoding|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts344', '22718787', 'Although small non-coding RNAs, such as microRNAs, have well-established functions in the cell, long non-coding RNAs (lncRNAs) have only recently started to emerge as abundant regulators of cell physiology, and their functions may be diverse. A small number of studies describe interactions between small and lncRNAs, with lncRNAs acting either as inhibitory decoys or as regulatory targets of microRNAs, but such interactions are still poorly explored. To facilitate the study of microRNA-lncRNA interactions, we implemented miRcode: a comprehensive searchable map of putative microRNA target sites across the complete GENCODE annotated transcriptome, including 10 419 lncRNA genes in the current version.'),
(77026, 2012, 'bio', 'Accelerated protein structure comparison using TM-score-GPU', 'Hung, Ling-Hong|Samudrala, Ram', 'Department of Microbiology, University of Washington, Seattle, WA 98195-7735, USA.|Department of Microbiology, University of Washington, Seattle, WA 98195-7735, USA.', 'United States|United States', 9, 'Algorithms|Computational Biology|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts345', '22718788', 'Accurate comparisons of different protein structures play important roles in structural biology, structure prediction and functional annotation. The root-mean-square-deviation (RMSD) after optimal superposition is the predominant measure of similarity due to the ease and speed of computation. However, global RMSD is dependent on the length of the protein and can be dominated by divergent loops that can obscure local regions of similarity. A more sophisticated measure of structure similarity, Template Modeling (TM)-score, avoids these problems, and it is one of the measures used by the community-wide experiments of critical assessment of protein structure prediction to compare predicted models with experimental structures. TM-score calculations are, however, much slower than RMSD calculations. We have therefore implemented a very fast version of TM-score for Graphical Processing Units (TM-score-GPU), using a new and novel hybrid Kabsch/quaternion method for calculating the optimal superposition and RMSD that is designed for parallel applications. This acceleration in speed allows TM-score to be used efficiently in computationally intensive applications such as for clustering of protein models and genome-wide comparisons of structure.'),
(77027, 2012, 'bio', 'Identification of feedback loops in neural networks based on multi-step Granger causality', 'Dong, Chao-Yi|Shin, Dongkwan|Joo, Sunghoon|Nam, Yoonkey|Cho, Kwang-Hyun', 'Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, Peoples Republic of China.|Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, Peoples Republic of China.|Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, Peoples Republic of China.|Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, Peoples Republic of China.|Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, Peoples Republic of China.', 'China|China|China|China|China', 12, 'Algorithms|Animals|Causality|Computational Biology|Computer Simulation|Feedback|Neural Networks (Computer)|Rats', 'Journal Article', '10.1093/bioinformatics/bts354', '22730429', 'Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors.'),
(77028, 2012, 'bio', 'Bendix: intuitive helix geometry analysis and abstraction', 'Dahl, Anna Caroline E|Chavent, Matthieu|Sansom, Mark S P', 'Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK.|Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK.|Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK.', 'United Kingdom|United Kingdom|United Kingdom', 70, 'Computational Biology|Membrane Proteins|Molecular Dynamics Simulation|Protein Structure, Secondary|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts357', '22730430', 'The flexibility of xcexb1-helices is important for membrane protein function and calls for better visualization and analysis. Software is presented that quantifies and projects the helix axis evolution over time, with the choice of uniform or analytic heatmap graphics according to the local geometry. Bendix supports static, molecular dynamics, atomistic and coarse-grained input.'),
(77029, 2012, 'bio', 'Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization', 'Gönen, Mehmet', 'Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Espoo, Finland. mehmet.gonen@aalto.fi', 'Finland', 196, 'Algorithms|Bayes Theorem|Drug Discovery|Enzymes|Genomics|Humans|Ion Channels|Pharmaceutical Preparations|Proteins|Receptors, Cytoplasmic and Nuclear|Receptors, G-Protein-Coupled|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bts360', '22730431', 'Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug-target interactions and formulating successful computational methods for predicting interactions remains challenging.'),
(77030, 2012, 'bio', 'Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models', 'do Rego, Thais G|Roider, Helge G|de Carvalho, Francisco A T|Costa, Ivan G', 'Center of Informatics, Federal University of Pernambuco, Recife 50740-560, Brazil.|Center of Informatics, Federal University of Pernambuco, Recife 50740-560, Brazil.|Center of Informatics, Federal University of Pernambuco, Recife 50740-560, Brazil.|Center of Informatics, Federal University of Pernambuco, Recife 50740-560, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 7, 'Animals|Bayes Theorem|Binding Sites|Blood Cells|Cell Differentiation|Embryonic Stem Cells|Epigenesis, Genetic|Histones|Linear Models|Mice|Promoter Regions, Genetic|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bts362', '22730432', 'Blood cell development is thought to be controlled by a circuit of transcription factors (TFs) and chromatin modifications that determine the cell fate through activating cell type-specific expression programs. To shed light on the interplay between histone marks and TFs during blood cell development, we model gene expression from regulatory signals by means of combinations of sparse linear regression models.'),
(77031, 2012, 'bio', 'BIDDSAT: visualizing the content of biodiversity data publishers in the Global Biodiversity Information Facility network', 'Otegui, Javier|Ariño, Arturo H', 'Department of Zoology and Ecology, University of Navarra, 31080 Pamplona, Spain. jotellechea@alumni.unav.es|Department of Zoology and Ecology, University of Navarra, 31080 Pamplona, Spain. jotellechea@alumni.unav.es', 'Spain|Spain', 6, 'Biodiversity|Data Mining|Databases, Factual|Information Services|Internet|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/bts359', '22730433', 'In any data quality workflow, data publishers must become aware of issues in their data so these can be corrected. User feedback mechanisms provide one avenue, while global assessments of datasets provide another. To date, there is no publicly available tool to allow both biodiversity data institutions sharing their data through the Global Biodiversity Information Facility network and its potential users to assess datasets as a whole. Contributing to bridge this gap both for publishers and users, we introduce BIoDiversity DataSets Assessment Tool, an online tool that enables selected diagnostic visualizations on the content of data publishers and/or their individual collections.'),
(77032, 2012, 'bio', 'gSearch: a fast and flexible general search tool for whole-genome sequencing', 'Song, Taemin|Hwang, Kyu-Baek|Hsing, Michael|Lee, Kyungjoon|Bohn, Justin|Kong, Sek Won', 'School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, South Korea.', '-|-|-|-|-|-', 0, 'Algorithms|Genomics|Molecular Sequence Annotation|Search Engine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts358', '22730434', 'Various processes such as annotation and filtering of variants or comparison of variants in different genomes are required in whole-genome or exome analysis pipelines. However, processing different databases and searching among millions of genomic loci is not trivial.'),
(77033, 2012, 'bio', 'DySC: software for greedy clustering of 16S rRNA reads', 'Zheng, Zejun|Kramer, Stefan|Schmidt, Bertil', 'Institut für Informatik, Johannes Gutenberg University Mainz, Mainz 55099, Germany.|Institut für Informatik, Johannes Gutenberg University Mainz, Mainz 55099, Germany.|Institut für Informatik, Johannes Gutenberg University Mainz, Mainz 55099, Germany.', 'Germany|Germany|Germany', 13, 'Cluster Analysis|Metagenome|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bioinformatics/bts355', '22730435', 'Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime.'),
(77034, 2012, 'bio', 'RESQUE: network reduction using semi-Markov random walk scores for efficient querying of biological networks', 'Sahraeian, Sayed Mohammad Ebrahim|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.', 'United States|United States', 18, 'Algorithms|Animals|Computational Biology|Drosophila melanogaster|Gene Regulatory Networks|Humans|Markov Chains|Metabolic Networks and Pathways|Protein Interaction Mapping|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/bts341', '22730436', 'Recent technological advances in measuring molecular interactions have resulted in an increasing number of large-scale biological networks. Translation of these enormous network data into meaningful biological insights requires efficient computational techniques that can unearth the biological information that is encoded in the networks. One such example is network querying, which aims to identify similar subnetwork regions in a large target network that are similar to a given query network. Network querying tools can be used to identify novel biological pathways that are homologous to known pathways, thereby enabling knowledge transfer across different organisms.'),
(77035, 2012, 'bio', 'Comments on MMFPh: a maximal motif finder for phosphoproteomics datasets', 'He, Zengyou|Gong, Haipeng', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Humans|Proteins|Proteomics|Software', 'Letter', '10.1093/bioinformatics/bts346', '22734018', '-'),
(77036, 2012, 'bio', 'Integrating literature-constrained and data-driven inference of signalling networks', 'Eduati, Federica|De Las Rivas, Javier|Di Camillo, Barbara|Toffolo, Gianna|Saez-Rodriguez, Julio', 'Department of Information Engineering, University of Padova, Padova, 31050, Italy.|Department of Information Engineering, University of Padova, Padova, 31050, Italy.|Department of Information Engineering, University of Padova, Padova, 31050, Italy.|Department of Information Engineering, University of Padova, Padova, 31050, Italy.|Department of Information Engineering, University of Padova, Padova, 31050, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Cell Line, Tumor|Humans|Phosphoproteins|Protein Interaction Maps|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bts363', '22734019', 'Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks.'),
(77037, 2012, 'bio', 'A bioimage informatics approach to automatically extract complex fungal networks', 'Obara, Boguslaw|Grau, Vicente|Fricker, Mark D', 'Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk|Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk|Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 22, 'Algorithms|Fungi|Image Processing, Computer-Assisted|Mycelium|Phanerochaete', 'Journal Article', '10.1093/bioinformatics/bts364', '22743223', 'Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77038, 2012, 'bio', 'GO-Elite: a flexible solution for pathway and ontology over-representation', 'Zambon, Alexander C|Gaj, Stan|Ho, Isaac|Hanspers, Kristina|Vranizan, Karen|Evelo, Chris T|Conklin, Bruce R|Pico, Alexander R|Salomonis, Nathan', 'Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.|Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Information Storage and Retrieval|Internet|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bts366', '22743224', 'We introduce GO-Elite, a flexible and powerful pathway analysis tool for a wide array of species, identifiers (IDs), pathways, ontologies and gene sets. In addition to the Gene Ontology (GO), GO-Elite allows the user to perform over-representation analysis on any structured ontology annotations, pathway database or biological IDs (e.g. gene, protein or metabolite). GO-Elite exploits the structured nature of biological ontologies to report a minimal set of non-overlapping terms. The results can be visualized on WikiPathways or as networks. Built-in support is provided for over 60 species and 50 ID systems, covering gene, disease and phenotype ontologies, multiple pathway databases, biomarkers, and transcription factor and microRNA targets. GO-Elite is available as a web interface, GenMAPP-CS plugin and as a cross-platform application.'),
(77039, 2012, 'bio', 'Targeted retrieval of gene expression measurements using regulatory models', 'Georgii, Elisabeth|Salojärvi, Jarkko|Brosché, Mikael|Kangasjärvi, Jaakko|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Aalto, Espoo, Finland. elisabeth.georgii@aalto.fi|Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Aalto, Espoo, Finland. elisabeth.georgii@aalto.fi|Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Aalto, Espoo, Finland. elisabeth.georgii@aalto.fi|Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Aalto, Espoo, Finland. elisabeth.georgii@aalto.fi|Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Aalto, Espoo, Finland. elisabeth.georgii@aalto.fi', 'Finland|Finland|Finland|Finland|Finland', 17, 'Arabidopsis|Data Mining|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Leukemia|Linear Models|Models, Genetic|Osmotic Pressure', 'Journal Article', '10.1093/bioinformatics/bts361', '22743225', 'Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge, (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes.'),
(77040, 2012, 'bio', 'RSeQC: quality control of RNA-seq experiments', 'Wang, Liguo|Wang, Shengqin|Li, Wei', 'Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. WL1@bcm.edu|Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. WL1@bcm.edu|Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. WL1@bcm.edu', 'United States|United States|United States', 0, 'Computational Biology|Quality Control|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts356', '22743226', 'RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed.'),
(77041, 2012, 'bio', 'PurityEst: estimating purity of human tumor samples using next-generation sequencing data', 'Su, Xiaoping|Zhang, Li|Zhang, Jianping|Meric-Bernstam, Funda|Weinstein, John N', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. xsu1@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. xsu1@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. xsu1@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. xsu1@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. xsu1@mdanderson.org', 'United States|United States|United States|United States|United States', 44, 'Algorithms|Alleles|Base Sequence|Heterozygote|Humans|Models, Genetic|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts365', '22743227', 'We developed a novel algorithm, PurityEst, to infer the tumor purity level from the allelic differential representation of heterozygous loci with somatic mutations in a human tumor sample with a matched normal tissue using next-generation sequencing data. We applied our tool to a whole cancer genome sequencing datasets and demonstrated the accuracy of PurityEst compared with DNA copy number-based estimation.'),
(77042, 2012, 'bio', 'VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment', 'Habegger, Lukas|Balasubramanian, Suganthi|Chen, David Z|Khurana, Ekta|Sboner, Andrea|Harmanci, Arif|Rozowsky, Joel|Clarke, Declan|Snyder, Michael|Gerstein, Mark', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. lukas.habegger@yale.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Genetic Variation|Genome, Human|Genomics|Genotype|Humans|Information Storage and Retrieval|Internet|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/bts368', '22743228', 'The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment.'),
(77043, 2012, 'bio', 'AutoBind: automatic extraction of protein-ligand-binding affinity data from biological literature', 'Chang, Darby Tien-Hao|Ke, Chao-Hsuan|Lin, Jung-Hsin|Chiang, Jung-Hsien', 'Department of Electrical Engineering, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.|Department of Electrical Engineering, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.|Department of Electrical Engineering, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.|Department of Electrical Engineering, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.', '-|-|-|-', 8, 'Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Ligands|Protein Binding|Software', 'Journal Article', '10.1093/bioinformatics/bts367', '22753780', 'Determination of the binding affinity of a protein-ligand complex is important to quantitatively specify whether a particular small molecule will bind to the target protein. Besides, collection of comprehensive datasets for protein-ligand complexes and their corresponding binding affinities is crucial in developing accurate scoring functions for the prediction of the binding affinities of previously unknown protein-ligand complexes. In the past decades, several databases of protein-ligand-binding affinities have been created via visual extraction from literature. However, such approaches are time-consuming and most of these databases are updated only a few times per year. Hence, there is an immediate demand for an automatic extraction method with high precision for binding affinity collection.'),
(77044, 2012, 'bio', 'OmegaPlus: a scalable tool for rapid detection of selective sweeps in whole-genome datasets', 'Alachiotis, N|Stamatakis, A|Pavlidis, P', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany. n.alachiotis@gmail.com|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany. n.alachiotis@gmail.com|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany. n.alachiotis@gmail.com', 'Germany|Germany|Germany', 0, 'Base Sequence|Biological Evolution|Genome|Genome-Wide Association Study|Genomics|Linkage Disequilibrium|Models, Genetic|Molecular Sequence Data|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts419', '22760304', 'Recent advances in sequencing technologies have led to the rapid accumulation of molecular sequence data. Analyzing whole-genome data (as obtained from next-generation sequencers) from intra-species samples allows to detect signatures of positive selection along the genome and therefore identify potentially advantageous genes in the course of the evolution of a population. We introduce OmegaPlus, an open-source tool for rapid detection of selective sweeps in whole-genome data based on linkage disequilibrium. The tool is up to two orders of magnitude faster than existing programs for this purpose and also exhibits up to two orders of magnitude smaller memory requirements.'),
(77045, 2012, 'bio', 'AraPath: a knowledgebase for pathway analysis in Arabidopsis', 'Lai, Liming|Liberzon, Arthur|Hennessey, Jason|Jiang, Gaixin|Qi, Jianli|Mesirov, Jill P|Ge, Steven X', 'Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Arabidopsis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Genome, Plant|Genomics|Knowledge Bases|Multigene Family', 'Journal Article', '10.1093/bioinformatics/bts421', '22760305', 'Studying plants using high-throughput genomics technologies is becoming routine, but interpretation of genome-wide expression data in terms of biological pathways remains a challenge, partly due to the lack of pathway databases. To create a knowledgebase for plant pathway analysis, we collected 1683 lists of differentially expressed genes from 397 gene-expression studies, which constitute a molecular signature database of various genetic and environmental perturbations of Arabidopsis. In addition, we extracted 1909 gene sets from various sources such as Gene Ontology, KEGG, AraCyc, Plant Ontology, predicted target genes of microRNAs and transcription factors, and computational gene clusters defined by meta-analysis. With this knowledgebase, we applied Gene Set Enrichment Analysis to an expression profile of cold acclimation and identified expected functional categories and pathways. Our results suggest that the AraPath database can be used to generate specific, testable hypotheses regarding plant molecular pathways from gene expression data.'),
(77046, 2012, 'bio', 'BasyLiCA: a tool for automatic processing of a Bacterial Live Cell Array', 'Aïchaoui, Leslie|Jules, Matthieu|Le Chat, Ludovic|Aymerich, Stéphane|Fromion, Vincent|Goelzer, Anne', 'INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.|INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.|INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.|INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.|INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.|INRA, UR1077 Unité Mathématique Informatique et Génome, F-78350 Jouy en Josas, France.', 'France|France|France|France|France|France', 0, 'Bacteria|Fluorescent Dyes|Genes, Reporter|Green Fluorescent Proteins|Promoter Regions, Genetic|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bts422', '22764159', 'Live Cell Array (LCA) technology allows the acquisition of high-resolution time-course profiles of bacterial gene expression by the systematic assessment of fluorescence in living cells carrying either transcriptional or translational fluorescent protein fusion. However, the direct estimation of promoter activities by time-dependent derivation of the fluorescence datasets generates high levels of noise. Here, we present BasyLiCA, a user-friendly open-source interface and database dedicated to the automatic storage and standardized treatment of LCA data. Data quality reports are generated automatically. Growth rates and promoter activities are calculated by tunable discrete Kalman filters that can be set to incorporate data from biological replicates, significantly reducing the impact of noise measurement in activity estimations.'),
(77047, 2012, 'bio', 'Voronto: mapper for expression data to ontologies', 'Santamaría, Rodrigo|Pierre, Philippe', 'Departmento de Informática y Automática, Universidad de Salamanca, 37008 Salamanca, Spain. rodri@usal.es|Departmento de Informática y Automática, Universidad de Salamanca, 37008 Salamanca, Spain. rodri@usal.es', 'Spain|Spain', 8, 'Algorithms|Chromosome Mapping|Gene Expression|Software', 'Journal Article', '10.1093/bioinformatics/bts428', '22764160', 'Several techniques analyze gene expression data from the point of view of biological ontologies. These methods focus on statistical and numerical analyses, but there is an additional need for fast, global and interactive visualizations of gene expression data in the context of biological ontologies.'),
(77048, 2012, 'bio', 'CySBML: a Cytoscape plugin for SBML', 'König, Matthias|Dräger, Andreas|Holzhütter, Hermann-Georg', 'Department of Biochemistry, University Medicine Charité Berlin, 13347, Berlin, Germany. cysbml-team@lists.sourceforge.net|Department of Biochemistry, University Medicine Charité Berlin, 13347, Berlin, Germany. cysbml-team@lists.sourceforge.net|Department of Biochemistry, University Medicine Charité Berlin, 13347, Berlin, Germany. cysbml-team@lists.sourceforge.net', 'Germany|Germany|Germany', 0, 'Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts432', '22772946', 'CySBML is a plugin designed to work with Systems Biology Markup Language (SBML) in Cytoscape having the following features: SBML import, support of the SBML layout and qualitative model packages, navigation in network layouts based on SBML structure, access to MIRIAM and SBO-based annotations and SBML validation. CySBML includes an importer for BioModels to load SBML from standard repositories.'),
(77049, 2012, 'bio', 'TLM-Tracker: software for cell segmentation, tracking and lineage analysis in time-lapse microscopy movies', 'Klein, Johannes|Leupold, Stefan|Biegler, Ilona|Biedendieck, Rebekka|Münch, Richard|Jahn, Dieter', 'Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 26, 'Bacillus megaterium|Green Fluorescent Proteins|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Microscopy, Video|Models, Biological|Models, Theoretical|Single-Cell Analysis|Software|Time-Lapse Imaging', 'Journal Article', '10.1093/bioinformatics/bts424', '22772947', 'Time-lapse imaging in combination with fluorescence microscopy techniques enable the investigation of gene regulatory circuits and uncovered phenomena like culture heterogeneity. In this context, computational image processing for the analysis of single cell behaviour plays an increasing role in systems biology and mathematical modelling approaches. Consequently, we developed a software package with graphical user interface for the analysis of single bacterial cell behaviour.'),
(77050, 2012, 'bio', 'A sequence comparison and gene expression data integration add-on for the Pathway Tools software', 'Krempl, Peter M|Mairhofer, Juergen|Striedner, Gerald|Thallinger, Gerhard G', 'ACIB, Petersgasse 14, 8010 Graz, Austria.|ACIB, Petersgasse 14, 8010 Graz, Austria.|ACIB, Petersgasse 14, 8010 Graz, Austria.|ACIB, Petersgasse 14, 8010 Graz, Austria.', 'Austria|Austria|Austria|Austria', 0, 'DNA Transposable Elements|Databases, Genetic|Gene Expression|Gene Expression Profiling|Molecular Sequence Annotation|Sequence Alignment|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/bts431', '22772948', 'We present a plug-in for Pathway Tools, an integrated systems biology software to create, maintain and query Pathway/Genome Databases. Fully integrated into the graphical user interface and menu, this plug-in extends the applications functionality by the ability to create multiple sequence alignments, systematically annotate insertion sequence (IS) elements and analyse their activity by cross-species comparison tools. Microarray probes can be automatically mapped to target genes, and expression data obtained with these arrays can be transformed into input formats needed to visualize them in the various omics viewers of Pathway Tools. The plug-in API itself allows developers to integrate their own functions into the Pathway Tools menu.'),
(77051, 2012, 'bio', 'Computational prediction of N-linked glycosylation incorporating structural properties and patterns', 'Chuang, Gwo-Yu|Boyington, Jeffrey C|Joyce, M Gordon|Zhu, Jiang|Nabel, Gary J|Kwong, Peter D|Georgiev, Ivelin', 'Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MA 20892, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Glycoproteins|Glycosylation|Models, Biological|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bts426', '22782545', 'N-linked glycosylation occurs predominantly at the N-X-T/S motif, where X is any amino acid except proline. Not all N-X-T/S sequons are glycosylated, and a number of web servers for predicting N-linked glycan occupancy using sequence and/or residue pattern information have been developed. None of the currently available servers, however, utilizes protein structural information for the prediction of N-glycan occupancy.'),
(77052, 2012, 'bio', 'CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data', 'Linderman, Michael D|Bjornson, Zach|Simonds, Erin F|Qiu, Peng|Bruggner, Robert V|Sheode, Ketaki|Meng, Teresa H|Plevritis, Sylvia K|Nolan, Garry P', 'Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 15, 'Algorithms|Computer Graphics|Flow Cytometry|Software', 'Journal Article', '10.1093/bioinformatics/bts425', '22782546', 'Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data.'),
(77053, 2012, 'bio', 'Algorithms and complexity of enumerating minimal precursor sets in genome-wide metabolic networks', 'Acuña, Vicente|Milreu, Paulo Vieira|Cottret, Ludovic|Marchetti-Spaccamela, Alberto|Stougie, Leen|Sagot, Marie-France', 'Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com|Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com|Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com|Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com|Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com|Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France. vicente77@gmail.com', 'France|France|France|France|France|France', 9, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/bts423', '22782547', 'In the context of studying whole metabolic networks and their interaction with the environment, the following question arises: given a set of target metabolites T and a set of possible external source metabolites  , which are the minimal subsets of   that are able to produce all the metabolites in T. Such subsets are called the minimal precursor sets of T. The problem is then whether we can enumerate all of them efficiently.'),
(77054, 2012, 'bio', 'Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data', 'Zoubarev, Anton|Hamer, Kelsey M|Keshav, Kiran D|McCarthy, E Luke|Santos, Joseph Roy C|Van Rossum, Thea|McDonald, Cameron|Hall, Adam|Wan, Xiang|Lim, Raymond|Gillis, Jesse|Pavlidis, Paul', 'Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Database Management Systems|Databases, Genetic|Gene Expression Profiling|Genomics|Meta-Analysis as Topic|Software', 'Journal Article', '10.1093/bioinformatics/bts430', '22782548', 'Gemma is a database, analysis software system and web site for genomics data re-use and meta-analysis. Currently, Gemma contains analyzed data from over 3300 expression profiling studies, yielding hundreds of millions of differential expression results and coexpression patterns (correlated expression) for retrieval and visualization. With optional registration users can save their own data and securely share it with other users. Web services and integration with third-party resources further increase the scope of the tools, which include a Cytoscape plugin.'),
(77055, 2012, 'bio', 'dbSNO: a database of cysteine S-nitrosylation', 'Lee, Tzong-Yi|Chen, Yi-Ju|Lu, Cheng-Tsung|Ching, Wei-Chieh|Teng, Yu-Chuan|Huang, Hsien-Da|Chen, Yu-Ju', 'Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw', '-|-|-|-|-|-|-', 56, 'Cysteine|Mass Spectrometry|Nitric Oxide|Peptides|Protein Processing, Post-Translational|Protein Structure, Tertiary|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/bts436', '22782549', 'S-nitrosylation (SNO), a selective and reversible protein post-translational modification that involves the covalent attachment of nitric oxide (NO) to the sulfur atom of cysteine, critically regulates protein activity, localization and stability. Due to its importance in regulating protein functions and cell signaling, a mass spectrometry-based proteomics method rapidly evolved to increase the dataset of experimentally determined SNO sites. However, there is currently no database dedicated to the integration of all experimentally verified S-nitrosylation sites with their structural or functional information. Thus, the dbSNO database is created to integrate all available datasets and to provide their structural analysis. Up to April 15, 2012, the dbSNO has manually accumulated >3000 experimentally verified S-nitrosylated peptides from 219 research articles using a text mining approach. To solve the heterogeneity among the data collected from different sources, the sequence identity of these reported S-nitrosylated peptides are mapped to the UniProtKB protein entries. To delineate the structural correlation and consensus motif of these SNO sites, the dbSNO database also provides structural and functional analyses, including the motifs of substrate sites, solvent accessibility, protein secondary and tertiary structures, protein domains and gene ontology.'),
(77056, 2012, 'bio', 'MSeasy: unsupervised and untargeted GC-MS data processing', 'Nicolè, Florence|Guitton, Yann|Courtois, Elodie A|Moja, Sandrine|Legendre, Laurent|Hossaert-McKey, Martine', 'Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr|Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr|Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr|Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr|Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr|Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr', 'France|France|France|France|France|France', 0, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Gas Chromatography-Mass Spectrometry|Software', 'Journal Article', '10.1093/bioinformatics/bts427', '22782550', 'MSeasy performs unsupervised data mining on gas chromatography-mass spectrometry data. It detects putative compounds within complex metabolic mixtures through the clustering of mass spectra. Retention times or retention indices are used after clustering, together with other validation criteria, for quality control of putative compounds. The package generates a fingerprinting or profiling matrix compatible with NIST mass spectral search program and ARISTO webtool (Automatic Reduction of Ion Spectra To Ontology) for molecule identification. Most commonly used file formats, NetCDF, mzXML and ASCII, are acceptable. A graphical and user-friendly interface, MSeasyTkGUI, is available for R novices.'),
(77057, 2012, 'bio', 'Efficient sampling for Bayesian inference of conjunctive Bayesian networks', 'Sakoparnig, Thomas|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.', 'Switzerland|Switzerland', 15, 'Algorithms|Bayes Theorem|Genomics|Genotype|Humans|Likelihood Functions|Models, Statistical|Mutation|Neoplasms', 'Journal Article', '10.1093/bioinformatics/bts433', '22782551', 'Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. However, new computational methods are needed to analyse these large datasets.'),
(77058, 2012, 'bio', 'MyMiner: a web application for computer-assisted biocuration and text annotation', 'Salgado, David|Krallinger, Martin|Depaule, Marc|Drula, Elodie|Tendulkar, Ashish V|Leitner, Florian|Valencia, Alfonso|Marcelle, Christophe', 'Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu|Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia. david.salgado@monash.edu', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 21, 'Data Mining|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1093/bioinformatics/bts435', '22789588', 'The exponential growth of scientific literature has resulted in a massive amount of unstructured natural language data that cannot be directly handled by means of bioinformatics tools. Such tools generally require structured data, often generated through a cumbersome process of manual literature curation. Herein, we present MyMiner, a free and user-friendly text annotation tool aimed to assist in carrying out the main biocuration tasks and to provide labelled data for the development of text mining systems. MyMiner allows easy classification and labelling of textual data according to user-specified classes as well as predefined biological entities. The usefulness and efficiency of this application have been tested for a range of real-life annotation scenarios of various research topics.'),
(77059, 2012, 'bio', 'iBBiG: iterative binary bi-clustering of gene sets', 'Gusenleitner, Daniel|Howe, Eleanor A|Bentink, Stefan|Quackenbush, John|Culhane, Aedín C', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.', 'United States|United States|United States|United States|United States', 22, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computational Biology|Computer Simulation|Female|Gene Expression Profiling|Genomics|Humans|Phenotype', 'Journal Article', '10.1093/bioinformatics/bts438', '22789589', 'Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets, however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.'),
(77060, 2012, 'bio', 'CBrowse: a SAM/BAM-based contig browser for transcriptome assembly visualization and analysis', 'Li, Pei|Ji, Guoli|Dong, Min|Schmidt, Emily|Lenox, Douglas|Chen, Liangliang|Liu, Qi|Liu, Lin|Zhang, Jie|Liang, Chun', 'Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.', 'China|China|China|China|China|China|China|China|China|China', 5, 'Computer Graphics|Contig Mapping|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts443', '22789590', 'To address the impending need for exploring rapidly increased transcriptomics data generated for non-model organisms, we developed CBrowse, an AJAX-based web browser for visualizing and analyzing transcriptome assemblies and contigs. Designed in a standard three-tier architecture with a data pre-processing pipeline, CBrowse is essentially a Rich Internet Application that offers many seamlessly integrated web interfaces and allows users to navigate, sort, filter, search and visualize data smoothly. The pre-processing pipeline takes the contig sequence file in FASTA format and its relevant SAM/BAM file as the input, detects putative polymorphisms, simple sequence repeats and sequencing errors in contigs and generates image, JSON and database-compatible CSV text files that are directly utilized by different web interfaces. CBowse is a generic visualization and analysis tool that facilitates close examination of assembly quality, genetic polymorphisms, sequence repeats and/or sequencing errors in transcriptome sequencing projects.'),
(77061, 2012, 'bio', 'RIBFIND: a web server for identifying rigid bodies in protein structures and to aid flexible fitting into cryo EM maps', 'Pandurangan, Arun Prasad|Topf, Maya', 'Department of Crystallography/Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK. a.pandurangan@mail.cryst.bbk.ac.uk|Department of Crystallography/Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK. a.pandurangan@mail.cryst.bbk.ac.uk', 'United Kingdom|United Kingdom', 11, 'Cryoelectron Microscopy|Escherichia coli Proteins|Internet|Periplasmic Binding Proteins|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/bts446', '22796953', 'To better analyze low-resolution cryo electron microscopy maps of macromolecular assemblies, component atomic structures frequently have to be flexibly fitted into them. Reaching an optimal fit and preventing the fitting process from getting trapped in local minima can be significantly improved by identifying appropriate rigid bodies (RBs) in the fitted component.'),
(77062, 2012, 'bio', 'Gene and translation initiation site prediction in metagenomic sequences', 'Hyatt, Doug|LoCascio, Philip F|Hauser, Loren J|Uberbacher, Edward C', 'Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov', 'United States|United States|United States|United States', 216, 'Algorithms|Base Sequence|Computer Simulation|Metagenomics|Models, Genetic|Mycoplasma|Open Reading Frames|Peptide Chain Initiation, Translational|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts429', '22796954', 'Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample, consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data.'),
(77063, 2012, 'bio', 'MALDIquant: a versatile R package for the analysis of mass spectrometry data', 'Gibb, Sebastian|Strimmer, Korbinian', 'Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany. mail@sebastiangibb.de|Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany. mail@sebastiangibb.de', 'Germany|Germany', 213, 'Data Interpretation, Statistical|Humans|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/bts447', '22796955', 'MALDIquant is an R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution.'),
(77064, 2012, 'bio', 'SQID-XLink: implementation of an intensity-incorporated algorithm for cross-linked peptide identification', 'Li, Wenzhou|ONeill, Heather A|Wysocki, Vicki H', 'Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, USA.|Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, USA.|Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, USA.', 'United States|United States|United States', 7, 'Algorithms|Mass Spectrometry|Peptides|Protein Conformation|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts442', '22796956', 'Peptide identification algorithm is a major bottleneck for mass spectrometry based chemical cross-linking experiments. Our lab recently developed an intensity-incorporated peptide identification algorithm, and here we implemented this scheme for cross-linked peptide discovery. Our program, SQID-XLink, searches all regular, dead-end, intra and inter cross-linked peptides simultaneously, and its effectiveness is validated by testing a published dataset. This new algorithm provides an alternative approach for high confidence cross-linking identification.'),
(77065, 2012, 'bio', 'Measuring and comparing structural fluctuation patterns in large protein datasets', 'Fuglebakk, Edvin|Echave, Julián|Reuter, Nathalie', 'Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.|Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.|Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.', 'Norway|Norway|Norway', 57, 'Computational Biology|Databases, Protein|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/bts445', '22796957', 'The function of a protein depends not only on its structure but also on its dynamics. This is at the basis of a large body of experimental and theoretical work on protein dynamics. Further insight into the dynamics-function relationship can be gained by studying the evolutionary divergence of protein motions. To investigate this, we need appropriate comparative dynamics methods. The most used dynamical similarity score is the correlation between the root mean square fluctuations (RMSF) of aligned residues. Despite its usefulness, RMSF is in general less evolutionarily conserved than the native structure. A fundamental issue is whether RMSF is not as conserved as structure because dynamics is less conserved or because RMSF is not the best property to use to study its conservation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77066, 2012, 'bio', 'A regression model for estimating DNA copy number applied to capture sequencing data', 'Rigaill, Guillem J|Cadot, Sidney|Kluin, Roelof J C|Xue, Zheng|Bernards, Rene|Majewski, Ian J|Wessels, Lodewyk F A', 'Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 15, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|DNA Copy Number Variations|Female|Genomics|Genotype|Humans|Linear Models|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts448', '22796958', 'Target enrichment, also referred to as DNA capture, provides an effective way to focus sequencing efforts on a genomic region of interest. Capture data are typically used to detect single-nucleotide variants. It can also be used to detect copy number alterations, which is particularly useful in the context of cancer, where such changes occur frequently. In copy number analysis, it is a common practice to determine log-ratios between test and control samples, but this approach results in a loss of information as it disregards the total coverage or intensity at a locus.'),
(77067, 2012, 'bio', 'Paving the future: finding suitable ISMB venues', 'Rost, Burkhard|Gaasterland, Terry|Lengauer, Thomas|Linial, Michal|Markel, Scott|Morrison McKay, B J|Schneider, Reinhard|Horton, Paul|Kelso, Janet', 'Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 2, 'Computational Biology|Congresses as Topic|Molecular Biology', 'News', '10.1093/bioinformatics/bts420', '22796959', 'The International Society for Computational Biology, ISCB, organizes the largest event in the field of computational biology and bioinformatics, namely the annual international conference on Intelligent Systems for Molecular Biology, the ISMB. This year at ISMB 2012 in Long Beach, ISCB celebrated the 20th anniversary of its flagship meeting. ISCB is a young, lean and efficient society that aspires to make a significant impact with only limited resources. Many constraints make the choice of venues for ISMB a tough challenge. Here, we describe those challenges and invite the contribution of ideas for solutions.'),
(77068, 2012, 'bio', 'GAPIT: genome association and prediction integrated tool', 'Lipka, Alexander E|Tian, Feng|Wang, Qishan|Peiffer, Jason|Li, Meng|Bradbury, Peter J|Gore, Michael A|Buckler, Edward S|Zhang, Zhiwu', 'Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.|Computational Biologist with the United States Department of Agriculture - Agricultural Research Service, Ithaca, NY 14853, USA.', 'United States|United States|United States|United States|United States|United States|United States|Un', 736, 'Genome-Wide Association Study|Genomics|Humans|Linear Models|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts444', '22796960', 'Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results.'),
(77069, 2012, 'bio', 'Bayesian sampling of evolutionarily conserved RNA secondary structures with pseudoknots', 'Doose, Gero|Metzler, Dirk', 'Department of Biology, LMU Biocenter, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany. gero@bioinf.uni-leipzig.de|Department of Biology, LMU Biocenter, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany. gero@bioinf.uni-leipzig.de', 'Germany|Germany', 5, 'Algorithms|Base Sequence|Bayes Theorem|Evolution, Molecular|Models, Genetic|Nucleic Acid Conformation|RNA|RNA, Untranslated|Ribonuclease P|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bts369', '22796961', 'Today many non-coding RNAs are known to play an active role in various important biological processes. Since RNAs functionality is correlated with specific structural motifs that are often conserved in phylogenetically related molecules, computational prediction of RNA structure should ideally be based on a set of homologous primary structures. But many available RNA secondary structure prediction programs that use sequence alignments do not consider pseudoknots or their estimations consist on a single structure without information on uncertainty.'),
(77070, 2012, 'bio', 'Fast and accurate read alignment for resequencing', 'Mu, John C|Jiang, Hui|Kiani, Amirhossein|Mohiyuddin, Marghoob|Bani Asadi, Narges|Wong, Wing H', 'Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 29, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts450', '22811546', 'Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers.'),
(77071, 2012, 'bio', 'Metabolite identification and molecular fingerprint prediction through machine learning', 'Heinonen, Markus|Shen, Huibin|Zamboni, Nicola|Rousu, Juho', 'Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland. markus.heinonen@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland. markus.heinonen@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland. markus.heinonen@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland. markus.heinonen@cs.helsinki.fi', 'Finland|Finland|Finland|Finland', 85, 'Artificial Intelligence|Databases, Chemical|Metabolomics|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bts437', '22815355', 'Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods.'),
(77072, 2012, 'bio', 'UniMoG--a unifying framework for genomic distance calculation and sorting based on DCJ', 'Hilker, Rolf|Sickinger, Corinna|Pedersen, Christian N S|Stoye, Jens', 'Computational Genomics, Bielefeld University, 33615 Bielefeld, Germany. rhilker@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, 33615 Bielefeld, Germany. rhilker@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, 33615 Bielefeld, Germany. rhilker@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, 33615 Bielefeld, Germany. rhilker@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany', 25, 'Algorithms|Computational Biology|Genomics|Internet|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts440', '22815356', 'UniMoG is a software combining five genome rearrangement models: double cut and join (DCJ), restricted DCJ, Hannenhalli and Pevzner (HP), inversion and translocation. It can compute the pairwise genomic distances and a corresponding optimal sorting scenario for an arbitrary number of genomes. All five models can be unified through the DCJ model, thus the implementation is based on DCJ and, where reasonable, uses the most efficient existing algorithms for each distance and sorting problem. Both textual and graphical output is possible for visualizing the operations.'),
(77073, 2012, 'bio', 'VegaMC: a R/bioconductor package for fast downstream analysis of large array comparative genomic hybridization datasets', 'Morganella, Sandro|Ceccarelli, Michele', 'Department of Science, University of Sannio, 82100 Benevento, Italy.|Department of Science, University of Sannio, 82100 Benevento, Italy.', 'Italy|Italy', 0, 'Algorithms|Alleles|Comparative Genomic Hybridization|Computational Biology|DNA Copy Number Variations|Gene Frequency|Humans|Neoplasms|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bts453', '22815357', 'Identification of genetic alterations of tumor cells has become a common method to detect the genes involved in development and progression of cancer. In order to detect driver genes, several samples need to be simultaneously analyzed. The Cancer Genome Atlas (TCGA) project provides access to a large amount of data for several cancer types. TGCA is an invaluable source of information, but analysis of this huge dataset possess important computational problems in terms of memory and execution times. Here, we present a R/package, called VegaMC (Vega multi-channel), that enables fast and efficient detection of significant recurrent copy number alterations in very large datasets. VegaMC is integrated with the output of the common tools that convert allele signal intensities in log R ratio and B allele frequency. It also enables the detection of loss of heterozigosity and provides in output two web pages allowing a rapid and easy navigation of the aberrant genes. Synthetic data and real datasets are used for quantitative and qualitative evaluation purposes. In particular, we demonstrate the ability of VegaMC on two large TGCA datasets: colon adenocarcinoma and glioblastoma multiforme. For both the datasets, we provide the list of aberrant genes which contain previously validated genes and can be used as basis for further investigations.'),
(77074, 2012, 'bio', 'imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel', 'Grapov, Dmitry|Newman, John W', 'Department of Nutrition, University of California, Davis, CA 95616, USA.|Department of Nutrition, University of California, Davis, CA 95616, USA.', 'United States|United States', 32, 'Cluster Analysis|Computer Graphics|Discriminant Analysis|Humans|Multivariate Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts439', '22815358', 'Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing Rs multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks.'),
(77075, 2012, 'bio', 'Quantifying uniformity of mapped reads', 'Hower, Valerie|Starfield, Richard|Roberts, Adam|Pachter, Lior', 'Department of Mathematics, University of Miami, Coral Gables, FL 33146, USA.|Department of Mathematics, University of Miami, Coral Gables, FL 33146, USA.|Department of Mathematics, University of Miami, Coral Gables, FL 33146, USA.|Department of Mathematics, University of Miami, Coral Gables, FL 33146, USA.', 'United States|United States|United States|United States', 0, 'Data Interpretation, Statistical|High-Throughput Nucleotide Sequencing|Quality Control|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts451', '22815359', 'We describe a tool for quantifying the uniformity of mapped reads in high-throughput sequencing experiments. Our statistic directly measures the uniformity of both read position and fragment length, and we explain how to compute a P-value that can be used to quantify biases arising from experimental protocols and mapping procedures. Our method is useful for comparing different protocols in experiments such as RNA-Seq.'),
(77076, 2012, 'bio', 'DanteR: an extensible R-based tool for quantitative analysis of -omics data', 'Taverner, Tom|Karpievitch, Yuliya V|Polpitiya, Ashoka D|Brown, Joseph N|Dabney, Alan R|Anderson, Gordon A|Smith, Richard D', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|Mass Spectrometry|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/bts449', '22815360', 'The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab.'),
(77077, 2012, 'bio', 'Network inference using steady-state data and Goldbeter-Koshland kinetics [corrected]', 'Oates, Chris J|Hennessy, Bryan T|Lu, Yiling|Mills, Gordon B|Mukherjee, Sach', 'Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk|Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk|Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk|Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk|Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Breast Neoplasms|Cell Line, Tumor|Female|Gene Regulatory Networks|Humans|Kinetics|MAP Kinase Signaling System|Markov Chains|Monte Carlo Method|Phosphorylation|Proteomics|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts459', '22815361', 'Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference.'),
(77078, 2012, 'bio', 'Cell-Dock: high-performance protein-protein docking', 'Pons, Carles|Jiménez-González, Daniel|González-Álvarez, Cecilia|Servat, Harald|Cabrera-Benítez, Daniel|Aguilar, Xavier|Fernández-Recio, Juan', 'Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 10, 'Algorithms|Fourier Analysis|Molecular Docking Simulation|Multiprotein Complexes|Software', 'Journal Article', '10.1093/bioinformatics/bts454', '22815362', 'The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality.'),
(77079, 2012, 'bio', 'MolBioLib: a C++11 framework for rapid development and deployment of bioinformatics tasks', 'Ohsumi, Toshiro K|Borowsky, Mark L', 'Department of Molecular Biology, Massachusetts General Hospital, Richard B. Simches Research Center, Boston, MA 02114, USA. ohsumit@molbio.mgh.harvard.edu|Department of Molecular Biology, Massachusetts General Hospital, Richard B. Simches Research Center, Boston, MA 02114, USA. ohsumit@molbio.mgh.harvard.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|Databases, Factual|Genomics|Programming Languages|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bts458', '22815363', 'We developed MolBioLib to address the need for adaptable next-generation sequencing analysis tools. The result is a compact, portable and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. MolBioLib is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib has been used to identify causative single-nucleotide polymorphisms in whole genome sequencing, detect balanced chromosomal rearrangements and compute enrichment of messenger RNAs (mRNAs) on microtubules, typically requiring applications of under 200 lines of code. MolBioLib includes programs to perform a wide variety of analysis tasks, such as computing read coverage, annotating genomic intervals and novel peak calling with a wavelet algorithm. Although MolBioLib was designed primarily for bioinformatics purposes, much of its functionality is applicable to a wide range of problems. Complete documentation and an extensive automated test suite are provided.'),
(77080, 2012, 'bio', 'SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts', 'Ryan, Michael C|Cleland, James|Kim, RyangGuk|Wong, Wing Chung|Weinstein, John N', 'In Silico Solutions, Fairfax, VA, USA. mryan@insilico.us.com|In Silico Solutions, Fairfax, VA, USA. mryan@insilico.us.com|In Silico Solutions, Fairfax, VA, USA. mryan@insilico.us.com|In Silico Solutions, Fairfax, VA, USA. mryan@insilico.us.com|In Silico Solutions, Fairfax, VA, USA. mryan@insilico.us.com', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Alternative Splicing|Computer Graphics|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts452', '22820202', 'SpliceSeq is a resource for RNA-Seq data that provides a clear view of alternative splicing and identifies potential functional changes that result from splice variation. It displays intuitive visualizations and prioritized lists of results that highlight splicing events and their biological consequences. SpliceSeq unambiguously aligns reads to gene splice graphs, facilitating accurate analysis of large, complex transcript variants that cannot be adequately represented in other formats.'),
(77081, 2012, 'bio', 'Discriminative local subspaces in gene expression data for effective gene function prediction', 'Puelma, Tomas|Gutiérrez, Rodrigo A|Soto, Alvaro', 'Department of Molecular Genetics and Microbiology, FONDAP Center for Genome Regulation, Pontificia Universidad Catolica de Chile, Santiago, Chile. tfpuelma@uc.cl|Department of Molecular Genetics and Microbiology, FONDAP Center for Genome Regulation, Pontificia Universidad Catolica de Chile, Santiago, Chile. tfpuelma@uc.cl|Department of Molecular Genetics and Microbiology, FONDAP Center for Genome Regulation, Pontificia Universidad Catolica de Chile, Santiago, Chile. tfpuelma@uc.cl', 'Chile|Chile|Chile', 0, 'Algorithms|Arabidopsis|Artificial Intelligence|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Genes|Genome, Plant|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/bts455', '22820203', 'Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning methods, such as Support Vector Machines (SVMs), have shown superior prediction accuracy. However, these methods lack the simple biological intuition provided by co-expression networks (CNs), limiting their practical usefulness.'),
(77082, 2012, 'bio', 'GenAlEx 65: genetic analysis in Excel Population genetic software for teaching and research--an update', 'Peakall, Rod|Smouse, Peter E', 'Evolution, Ecology and Genetics, Research School of Biology, The Australian National University, Canberra ACT 0200, Australia. rod.peakall@anu.edu.au|Evolution, Ecology and Genetics, Research School of Biology, The Australian National University, Canberra ACT 0200, Australia. rod.peakall@anu.edu.au', 'Australia|Australia', 0, 'Computational Biology|Gene Frequency|Genetic Markers|Genetics, Population|Haplotypes|Linkage Disequilibrium|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts460', '22820204', 'GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G(ST), G(ST), Josts D(est) and F(ST) through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised.'),
(77083, 2012, 'bio', 'ANGES: reconstructing ANcestral GEnomeS maps', 'Jones, Bradley R|Rajaraman, Ashok|Tannier, Eric|Chauve, Cedric', 'Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.|Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.|Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.|Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Chromosome Mapping|Evolution, Molecular|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/bts457', '22820205', 'ANGES is a suite of Python programs that allows reconstructing ancestral genome maps from the comparison of the organization of extant-related genomes. ANGES can reconstruct ancestral genome maps for multichromosomal linear genomes and unichromosomal circular genomes. It implements methods inspired from techniques developed to compute physical maps of extant genomes. Examples of cereal, amniote, yeast or bacteria ancestral genomes are provided, computed with ANGES.'),
(77084, 2012, 'bio', 'GeneTalk: an expert exchange platform for assessing rare sequence variants in personal genomes', 'Kamphans, Tom|Krawitz, Peter M', 'GeneTalk, Finckensteinallee 84, 12205 Berlin, Germany.|GeneTalk, Finckensteinallee 84, 12205 Berlin, Germany.', 'Germany|Germany', 38, 'Computational Biology|Exome|Genome, Human|Humans|Information Dissemination|Internet|Knowledge Management|Molecular Sequence Annotation|Polymorphism, Genetic|Precision Medicine|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts462', '22826540', 'Next-generation sequencing has become a powerful tool in personalized medicine. Exomes or even whole genomes of patients suffering from rare diseases are screened for sequence variants. After filtering out common polymorphisms, the assessment and interpretation of detected personal variants in the clinical context is an often time-consuming effort. We have developed GeneTalk, a web-based platform that serves as an expert exchange network for the assessment of personal and potentially disease-relevant sequence variants. GeneTalk assists a clinical geneticist who is searching for information about specific sequence variants and connects this user to other users with expertise for the same sequence variant.'),
(77085, 2012, 'bio', 'RILogo: visualizing RNA-RNA interactions', 'Menzel, Peter|Seemann, Stefan E|Gorodkin, Jan', 'The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen DK-2200, Denmark. pmenzel@gmail.com|The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen DK-2200, Denmark. pmenzel@gmail.com|The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen DK-2200, Denmark. pmenzel@gmail.com', 'Denmark|Denmark|Denmark', 0, 'Base Pairing|Base Sequence|Computational Biology|Internet|Molecular Sequence Annotation|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts461', '22826541', 'With the increasing amount of newly discovered non-coding RNAs, the interactions between RNA molecules become an increasingly important aspect for characterizing their functionality. Many computational tools have been developed to predict the formation of duplexes between two RNAs, either based on single sequences or alignments of homologous sequences. Here, we present RILogo, a program to visualize inter- and intramolecular base pairing between two RNA molecules. The input for RILogo is a pair of structure-annotated sequences or alignments. In the latter case, RILogo displays the alignments in the form of sequence logos, including the mutual information of base paired columns. We also introduce two novel mutual information based measures that weigh the covariance information by the evolutionary distances of the aligned sequences. We show that the new measures have an increased accuracy compared with previous mutual information measures.'),
(77086, 2012, 'bio', 'Bacterial GRAS domain proteins throw new light on gibberellic acid response mechanisms', 'Zhang, Dapeng|Iyer, Lakshminarayan M|Aravind, L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 48, 'Amino Acid Sequence|Bacteria|Bacterial Proteins|Gibberellins|Methyltransferases|Molecular Sequence Data|Phylogeny|Plant Proteins|Plants|Protein Structure, Secondary|Protein Structure, Tertiary|Sequence Alignment|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bts464', '22829623', 'Gibberellic acids (GAs) are key plant hormones, regulating various aspects of growth and development, which have been at the center of the green revolution. GRAS family proteins, the primary players in GA signaling pathways, remain poorly understood. Using sequence-profile searches, structural comparisons and phylogenetic analysis, we establish that the GRAS family first emerged in bacteria and belongs to the Rossmann fold methyltransferase superfamily. All bacterial and a subset of plant GRAS proteins are likely to function as small-molecule methylases. The remaining plant versions have lost one or more AdoMet (SAM)-binding residues while preserving their substrate-binding residues. We predict that GRAS proteins might either modify or bind small molecules such as GAs or their derivatives.'),
(77087, 2012, 'bio', 'YAHA: fast and flexible long-read alignment with optimal breakpoint detection', 'Faust, Gregory G|Hall, Ira M', 'Department of Computer Science, University of Virginia, Charlottesville, VA 22908, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts456', '22829624', 'With improved short-read assembly algorithms and the recent development of long-read sequencers, split mapping will soon be the preferred method for structural variant (SV) detection. Yet, current alignment tools are not well suited for this.'),
(77088, 2012, 'bio', 'Nebula--a web-server for advanced ChIP-seq data analysis', 'Boeva, Valentina|Lermine, Alban|Barette, Camille|Guillouf, Christel|Barillot, Emmanuel', 'Institut Curie, Paris 75248, France.|Institut Curie, Paris 75248, France.|Institut Curie, Paris 75248, France.|Institut Curie, Paris 75248, France.|Institut Curie, Paris 75248, France.', 'France|France|France|France|France', 23, 'Binding Sites|Chromatin Immunoprecipitation|Computational Biology|High-Throughput Nucleotide Sequencing|Internet|Software', 'Journal Article', '10.1093/bioinformatics/bts463', '22829625', 'ChIP-seq consists of chromatin immunoprecipitation and deep sequencing of the extracted DNA fragments. It is the technique of choice for accurate characterization of the binding sites of transcription factors and other DNA-associated proteins. We present a web service, Nebula, which allows inexperienced users to perform a complete bioinformatics analysis of ChIP-seq data.'),
(77089, 2012, 'bio', 'Folding RNA/DNA hybrid duplexes', 'Lorenz, Ronny|Hofacker, Ivo L|Bernhart, Stephan H', 'Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. ronny@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. ronny@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. ronny@tbi.univie.ac.at', 'Austria|Austria|Austria', 0, 'Algorithms|Computational Biology|DNA|Dimerization|RNA|RNA Folding|Software', 'Journal Article', '10.1093/bioinformatics/bts466', '22829626', 'While there are numerous programs that can predict RNA or DNA secondary structures, a program that predicts RNA/DNA hetero-dimers is still missing. The lack of easy to use tools for predicting their structure may be in part responsible for the small number of reports of biologically relevant RNA/DNA hetero-dimers.'),
(77090, 2012, 'bio', 'Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features', 'Bacardit, Jaume|Widera, Pawexc5x82|Mxc3xa1rquez-Chamorro, Alfonso|Divina, Federico|Aguilar-Ruiz, Jesxc3xbas S|Krasnogor, Natalio', 'Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.|Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.|Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.|Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.|Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.|Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 38, 'Algorithms|Artificial Intelligence|Caspase 8|Caspase 9|Computational Biology|Databases, Protein|Humans|Protein Interaction Domains and Motifs|Proteins', 'Comparative Study', '10.1093/bioinformatics/bts472', '22833524', 'The prediction of a proteins contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation.'),
(77091, 2012, 'bio', 'RedoxDB--a curated database for experimentally verified protein oxidative modification', 'Sun, Ming-an|Wang, Yejun|Cheng, Han|Zhang, Qing|Ge, Wei|Guo, Dianjing', 'School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.', 'China|China|China|China|China|China', 0, 'Computational Biology|Cysteine|Databases, Protein|Oxidation-Reduction|Protein Processing, Post-Translational|Proteins|Sulfhydryl Compounds|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts468', '22833525', 'Redox regulation and signaling, which are involved in various cellular processes, have become one of the research focuses in the past decade. Cysteine thiol groups are particularly susceptible to post-translational modification, and their reversible oxidation is of critical role in redox regulation and signaling. With the tremendous improvement of techniques, hundreds of redox proteins along with their redox-sensitive cysteines have been reported, and the number is still fast growing. However, until now there is no database to accommodate the rapid accumulation of information on protein oxidative modification. Here we present RedoxDB-a manually curated database for experimentally validated redox proteins. RedoxDB (version 1.0) consists of two datasets (A and B, for proteins with or without verified modified cysteines, respectively) and includes 2157 redox proteins containing 2203 cysteine residues with oxidative modification. For each modified cysteine, the exact position, modification type and flanking sequence are provided. Additional information, including gene name, organism, sequence, literature references and links to UniProt and PDB, is also supplied. The database supports several functions including data search, blast and browsing. Bulk download of the entire dataset is also available. We expect that RedoxDB will be useful for both experimental studies and computational analyses of protein oxidative modification.'),
(77092, 2012, 'bio', 'DELIMINATE--a fast and efficient method for loss-less compression of genomic sequences: sequence analysis', 'Mohammed, Monzoorul Haque|Dutta, Anirban|Bose, Tungadri|Chadaram, Sudha|Mande, Sharmila S', 'Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India.|Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India.|Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India.|Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India.|Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India.', 'India|India|India|India|India', 31, 'Algorithms|Base Sequence|Computational Biology|Data Compression|Genomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts467', '22833526', 'An unprecedented quantity of genome sequence data is currently being generated using next-generation sequencing platforms. This has necessitated the development of novel bioinformatics approaches and algorithms that not only facilitate a meaningful analysis of these data but also aid in efficient compression, storage, retrieval and transmission of huge volumes of the generated data. We present a novel compression algorithm (DELIMINATE) that can rapidly compress genomic sequence data in a loss-less fashion. Validation results indicate relatively higher compression efficiency of DELIMINATE when compared with popular general purpose compression algorithms, namely, gzip, bzip2 and lzma.'),
(77093, 2012, 'bio', 'Bayesian ontology querying for accurate and noise-tolerant semantic searches', 'Bauer, Sebastian|Köhler, Sebastian|Schulz, Marcel H|Robinson, Peter N', 'Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. Sebastian.Bauer@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. Sebastian.Bauer@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. Sebastian.Bauer@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. Sebastian.Bauer@charite.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Bayes Theorem|Computational Biology|Decision Support Techniques|Diagnosis, Differential|Humans|Models, Statistical|Semantics', 'Journal Article', '10.1093/bioinformatics/bts471', '22843981', 'Ontologies provide a structured representation of the concepts of a domain of knowledge as well as the relations between them. Attribute ontologies are used to describe the characteristics of the items of a domain, such as the functions of proteins or the signs and symptoms of disease, which opens the possibility of searching a database of items for the best match to a list of observed or desired attributes. However, naive search methods do not perform well on realistic data because of noise in the data, imprecision in typical queries and because individual items may not display all attributes of the category they belong to.'),
(77094, 2012, 'bio', 'Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood', 'Lee, S H|Yang, J|Goddard, M E|Visscher, P M|Wray, N R', 'The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia. hong.lee@uq.edu.au|The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia. hong.lee@uq.edu.au|The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia. hong.lee@uq.edu.au|The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia. hong.lee@uq.edu.au|The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia. hong.lee@uq.edu.au', 'Australia|Australia|Australia|Australia|Australia', 291, 'Computational Biology|Computer Simulation|Diabetes Mellitus, Type 2|Genetic Pleiotropy|Genome-Wide Association Study|Genomics|Humans|Hypertension|Likelihood Functions|Linear Models|Models, Genetic|Pedigree|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/bts474', '22843982', 'Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case-control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of ~0.31 (SE 0.14, P = 0.024).'),
(77095, 2012, 'bio', 'Meta-Storms: efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data', 'Su, Xiaoquan|Xu, Jian|Ning, Kang', 'Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, Shandong Province, Peoples Republic of China.|Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, Shandong Province, Peoples Republic of China.|Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, Shandong Province, Peoples Republic of China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Databases, Genetic|Humans|Metagenome|Metagenomics|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/bts470', '22843983', 'It has long been intriguing scientists to effectively compare different microbial communities (also referred as metagenomic samples here) in a large scale: given a set of unknown samples, find similar metagenomic samples from a large repository and examine how similar these samples are. With the current metagenomic samples accumulated, it is possible to build a database of metagenomic samples of interests. Any metagenomic samples could then be searched against this database to find the most similar metagenomic sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories that offer few functionalities for analysis, and on the other hand, methods to measure the similarity of metagenomic data work well only for small set of samples by pairwise comparison. It is not yet clear, how to efficiently search for metagenomic samples against a large metagenomic database.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77096, 2012, 'bio', 'TMBB-DB: a transmembrane xcexb2-barrel proteome database', 'Freeman, Thomas C|Wimley, William C', 'Department of Biochemistry, Tulane University, New Orleans, LA 70112, USA.|Department of Biochemistry, Tulane University, New Orleans, LA 70112, USA.', 'United States|United States', 13, 'Algorithms|Computational Biology|Databases, Protein|Genome, Bacterial|Membrane Proteins|Protein Structure, Secondary|Proteome', 'Journal Article', '10.1093/bioinformatics/bts478', '22843985', 'We previously reported the development of a highly accurate statistical algorithm for identifying xcexb2-barrel outer membrane proteins or transmembrane xcexb2-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26, 1965-1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes.'),
(77097, 2012, 'bio', 'zCall: a rare variant caller for array-based genotyping: genetics and population analysis', 'Goldstein, Jacqueline I|Crenshaw, Andrew|Carey, Jason|Grant, George B|Maguire, Jared|Fromer, Menachem|ODushlaine, Colm|Moran, Jennifer L|Chambert, Kimberly|Stevens, Christine|Swedish Schizophrenia Consortium|ARRA Autism Sequencing Consortium|Sklar, Pamela|Hultman, Christina M|Purcell, Shaun|McCarroll, Steven A|Sullivan, Patrick F|Daly, Mark J|Neale, Benjamin M', 'Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.|Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 0, 'Algorithms|Alleles|Cluster Analysis|Exome|Genotyping Techniques|Homozygote|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bts479', '22843986', 'zCall is a variant caller specifically designed for calling rare single-nucleotide polymorphisms from array-based technology. This caller is implemented as a post-processing step after a default calling algorithm has been applied. The algorithm uses the intensity profile of the common allele homozygote cluster to define the location of the other two genotype clusters. We demonstrate improved detection of rare alleles when applying zCall to samples that have both Illumina Infinium HumanExome BeadChip and exome sequencing data available.'),
(77098, 2012, 'bio', 'Deep architectures for protein contact map prediction', 'Di Lena, Pietro|Nagata, Ken|Baldi, Pierre', 'Department of Computer Science, University of California, Irvine, CA 92697, USA.|Department of Computer Science, University of California, Irvine, CA 92697, USA.|Department of Computer Science, University of California, Irvine, CA 92697, USA.', 'United States|United States|United States', 166, 'Algorithms|Artificial Intelligence|Computational Biology|Neural Networks (Computer)|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/bts475', '22847931', 'Residue-residue contact prediction is important for protein structure prediction and other applications. However, the accuracy of current contact predictors often barely exceeds 20% on long-range contacts, falling short of the level required for ab initio structure prediction.'),
(77099, 2012, 'bio', 'easyRNASeq: a bioconductor package for processing RNA-Seq data', 'Delhomme, Nicolas|Padioleau, Ismaël|Furlong, Eileen E|Steinmetz, Lars M', 'Genome Biology Computational Support, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. delhomme@embl.de|Genome Biology Computational Support, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. delhomme@embl.de|Genome Biology Computational Support, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. delhomme@embl.de|Genome Biology Computational Support, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. delhomme@embl.de', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts477', '22847932', 'RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous Bioconductor packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought.'),
(77100, 2012, 'bio', 'An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data', 'Walters, Raymond|Laurin, Charles|Lubke, Gitta H', 'Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA.', 'United States|United States|United States', 8, 'Algorithms|Gene Frequency|Genome, Human|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1093/bioinformatics/bts483', '22847933', 'There is growing momentum to develop statistical learning (SL) methods as an alternative to conventional genome-wide association studies (GWAS). Methods such as random forests (RF) and gradient boosting machine (GBM) result in variable importance measures that indicate how well each single-nucleotide polymorphism (SNP) predicts the phenotype. For RF, it has been shown that variable importance measures are systematically affected by minor allele frequency (MAF) and linkage disequilibrium (LD). To establish RF and GBM as viable alternatives for analyzing genome-wide data, it is necessary to address this potential bias and show that SL methods do not significantly under-perform conventional GWAS methods.'),
(77101, 2012, 'bio', 'A MATLAB toolbox for structural kinetic modeling', 'Girbig, Dorothee|Selbig, Joachim|Grimbs, Sergio', 'Bioinformatics Group, Max-Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany. girbig@mpimp-golm.mpg.de|Bioinformatics Group, Max-Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany. girbig@mpimp-golm.mpg.de|Bioinformatics Group, Max-Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany. girbig@mpimp-golm.mpg.de', 'Germany|Germany|Germany', 0, 'Kinetics|Metabolic Networks and Pathways|Models, Biological|Monte Carlo Method|Software', 'Journal Article', '10.1093/bioinformatics/bts473', '22847934', 'Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for Monte-Carlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models.'),
(77102, 2012, 'bio', 'GlycoCD: a repository for carbohydrate-related CD antigens', 'Kumar, Sonu|Lütteke, Thomas|Schwartz-Albiez, Reinhard', 'D015, Translational Immunology, German Cancer Research Center, INF 280, D-69120 Heidelberg, Germany.|D015, Translational Immunology, German Cancer Research Center, INF 280, D-69120 Heidelberg, Germany.|D015, Translational Immunology, German Cancer Research Center, INF 280, D-69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 7, 'Antigens, CD|Carbohydrates|Databases, Factual|Epitopes|Humans|Internet|Lectins|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts481', '22847935', 'The open access comprehensive GlycoCD database application is for representation and retrieval of carbohydrate-related clusters of differentiation (CDs). The main objective of this database platform is to provide information about interactions of carbohydrate moieties with proteins that are important for identification of specific cell surface molecule with a focus on the integration of data from carbohydrate microarray databases. GlycoCD database comprises two sections: the carbohydrate recognition CD and glycan CD. It allows easy access through a user-friendly web interface to all carbohydrate-defined CDs and those that interact with carbohydrates along with other relevant information.'),
(77103, 2012, 'bio', 'EuGene: maximizing synthetic gene design for heterologous expression', 'Gaspar, Paulo|Oliveira, José Luís|Frommlet, Jörg|Santos, Manuel A S|Moura, Gabriela', 'DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. paulogaspar@ua.pt|DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. paulogaspar@ua.pt|DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. paulogaspar@ua.pt|DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. paulogaspar@ua.pt|DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. paulogaspar@ua.pt', 'Portugal|Portugal|Portugal|Portugal|Portugal', 0, 'Algorithms|Base Composition|Codon|Gene Expression|Genes, Synthetic|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts465', '22847936', 'Numerous software applications exist to deal with synthetic gene design, granting the field of heterologous expression a significant support. However, their dispersion requires the access to different tools and online services in order to complete one single project. Analyzing codon usage, calculating codon adaptation index (CAI), aligning orthologs and optimizing genes are just a few examples. A software application, EuGene, was developed for the optimization of multiple gene synthetic design algorithms. In a seamless automatic form, EuGene calculates or retrieves genome data on codon usage (relative synonymous codon usage and CAI), codon context (CPS and codon pair bias), GC content, hidden stop codons, repetitions, deleterious sites, protein primary, secondary and tertiary structures, gene orthologs, species housekeeping genes, performs alignments and identifies genes and genomes. The main function of EuGene is analyzing and redesigning gene sequences using multi-objective optimization techniques that maximize the coding features of the resulting sequence.'),
(77104, 2012, 'bio', 'PRISM: pair-read informed split-read mapping for base-pair level detection of insertion, deletion and structural variants', 'Jiang, Yue|Wang, Yadong|Brudno, Michael', 'Center for Biomedical Informatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. yue.jiang.hit@gmail.com|Center for Biomedical Informatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. yue.jiang.hit@gmail.com|Center for Biomedical Informatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. yue.jiang.hit@gmail.com', 'China|China|China', 83, 'Algorithms|Base Pairing|Chromosome Mapping|DNA|Genomic Structural Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Sequence Deletion', 'Evaluation Studies', '10.1093/bioinformatics/bts484', '22851530', 'The development of high-throughput sequencing technologies has enabled novel methods for detecting structural variants (SVs). Current methods are typically based on depth of coverage or pair-end mapping clusters. However, most of these only report an approximate location for each SV, rather than exact breakpoints.'),
(77105, 2012, 'bio', 'MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data', 'Rojas-Chertó, Miguel|van Vliet, Michael|Peironcely, Julio E|van Doorn, Ronnie|Kooyman, Maarten|te Beek, Tim|van Driel, Marc A|Hankemeier, Thomas|Reijmers, Theo', 'Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl|Netherlands Metabolomics Centre, Leiden, The Netherlands. m.rojas@lacdr.leidenuniv.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Internet|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/bts486', '22851531', 'Identification of metabolites using high-resolution multi-stage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier.'),
(77106, 2012, 'bio', 'SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models', 'Moal, Iain H|Fernández-Recio, Juan', 'Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain.|Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain.', 'Spain|Spain', 139, 'Databases, Protein|Kinetics|Models, Chemical|Mutant Proteins|Protein Binding|Protein Interaction Mapping|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/bts489', '22859501', 'Empirical models for the prediction of how changes in sequence alter protein-protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation.'),
(77107, 2012, 'bio', 'A robust approach to extract biomedical events from literature', 'Bui, Quoc-Chinh|Sloot, Peter M A', 'Computational Science, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. bqchinh@gmail.com|Computational Science, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. bqchinh@gmail.com', 'Netherlands|Netherlands', 0, 'Algorithms|Data Mining|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/bts487', '22859502', 'The abundance of biomedical literature has attracted significant interest in novel methods to automatically extract biomedical relations from the literature. Until recently, most research was focused on extracting binary relations such as protein-protein interactions and drug-disease relations. However, these binary relations cannot fully represent the original biomedical data. Therefore, there is a need for methods that can extract fine-grained and complex relations known as biomedical events.'),
(77108, 2012, 'bio', 'An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection', 'Wang, Xingbin|Kang, Dongwan D|Shen, Kui|Song, Chi|Lu, Shuya|Chang, Lun-Ching|Liao, Serena G|Huo, Zhiguang|Tang, Shaowu|Ding, Ying|Kaminski, Naftali|Sibille, Etienne|Lin, Yan|Li, Jia|Tseng, George C', 'Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 120, 'Algorithms|Computational Biology|Gene Expression Profiling|Genomics|Humans|Male|Meta-Analysis as Topic|Microarray Analysis|Prostatic Neoplasms|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/bts485', '22863766', 'With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines.'),
(77109, 2012, 'bio', 'Identifying multi-layer gene regulatory modules from multi-dimensional genomic data', 'Li, Wenyuan|Zhang, Shihua|Liu, Chun-Chi|Zhou, Xianghong Jasmine', 'Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|DNA Copy Number Variations|DNA Methylation|Female|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Humans|MicroRNAs|Oncogenes|Ovarian Neoplasms|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/bts476', '22863767', 'Eukaryotic gene expression (GE) is subjected to precisely coordinated multi-layer controls, across the levels of epigenetic, transcriptional and post-transcriptional regulations. Recently, the emerging multi-dimensional genomic dataset has provided unprecedented opportunities to study the cross-layer regulatory interplay. In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME). However, suitable analysis methods for such data are currently sparse.'),
(77110, 2012, 'bio', 'BioJava: an open-source framework for bioinformatics in 2012', 'Prlixc4x87, Andreas|Yates, Andrew|Bliven, Spencer E|Rose, Peter W|Jacobsen, Julius|Troshin, Peter V|Chapman, Mark|Gao, Jianjiong|Koh, Chuan Hock|Foisy, Sylvain|Holland, Richard|Rimsa, Gediminas|Heuer, Michael L|Brandstxc3xa4tter-Mxc3xbcller, H|Bourne, Philip E|Willis, Scooter', 'San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. andreas.prlic@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Amino Acids|Computational Biology|Genomics|Protein Conformation|Protein Processing, Post-Translational|Proteins|Sequence Alignment|Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bts494', '22877863', 'BioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.'),
(77111, 2012, 'bio', 'PPfold 30: fast RNA secondary structure prediction using phylogeny and auxiliary data', 'Sükösd, Zsuzsanna|Knudsen, Bjarne|Kjems, Jørgen|Pedersen, Christian N S', 'Interdisciplinary Nanoscience Center, Aarhus University, Aarhus N, Denmark. zs@birc.au.dk|Interdisciplinary Nanoscience Center, Aarhus University, Aarhus N, Denmark. zs@birc.au.dk|Interdisciplinary Nanoscience Center, Aarhus University, Aarhus N, Denmark. zs@birc.au.dk|Interdisciplinary Nanoscience Center, Aarhus University, Aarhus N, Denmark. zs@birc.au.dk', 'Denmark|Denmark|Denmark|Denmark', 29, 'Algorithms|Models, Statistical|Nucleic Acid Conformation|Phylogeny|RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts488', '22877864', 'PPfold is a multi-threaded implementation of the Pfold algorithm for RNA secondary structure prediction. Here we present a new version of PPfold, which extends the evolutionary analysis with a flexible probabilistic model for incorporating auxiliary data, such as data from structure probing experiments. Our tests show that the accuracy of single-sequence secondary structure prediction using experimental data in PPfold 3.0 is comparable to RNAstructure. Furthermore, alignment structure prediction quality is improved even further by the addition of experimental data. PPfold 3.0 therefore has the potential of producing more accurate predictions than it was previously possible.'),
(77112, 2012, 'bio', 'Snakemake--a scalable bioinformatics workflow engine', 'Köster, Johannes|Rahmann, Sven', 'Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen and Paediatric Oncology, University Childrens Hospital, 45147 Essen, Germany. johannes.koester@uni-due.de|Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen and Paediatric Oncology, University Childrens Hospital, 45147 Essen, Germany. johannes.koester@uni-due.de', 'Germany|Germany', 0, 'Computational Biology|Electronic Data Processing|Programming Languages|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/bts480', '22908215', 'Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames.'),
(77113, 2012, 'bio', 'treePL: divergence time estimation using penalized likelihood for large phylogenies', 'Smith, Stephen A|OMeara, Brian C', 'Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. eebsmith@umich.edu|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. eebsmith@umich.edu', 'United States|United States', 193, 'Algorithms|Likelihood Functions|Phylogeny|Software', 'Evaluation Studies', '10.1093/bioinformatics/bts492', '22908216', 'Ever larger phylogenies are being constructed due to the explosion of genetic data and development of high-performance phylogenetic reconstruction algorithms. However, most methods for calculating divergence times are limited to datasets that are orders of magnitude smaller than recently published large phylogenies. Here, we present an algorithm and implementation of a divergence time method using penalized likelihood that can handle datasets of thousands of taxa. We implement a method that combines the standard derivative-based optimization with a stochastic simulated annealing approach to overcome optimization challenges. We compare this approach with existing software including r8s, PATHd8 and BEAST.'),
(77114, 2012, 'bio', 'FACETS: multi-faceted functional decomposition of protein interaction networks', 'Seah, Boon-Siew|Bhowmick, Sourav S|Dewey, C Forbes', 'School of Computer Engineering, Nanyang Technological University, Singapore. seah0097@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. seah0097@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. seah0097@ntu.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Protein Interaction Mapping|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/bts469', '22908217', 'The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity.'),
(77115, 2012, 'bio', 'SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits', 'Li, Biao|Wang, Gao|Leal, Suzanne M', 'Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States', 0, 'Genetic Association Studies|Genetic Variation|Humans|Phenotype|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/bts499', '22914216', 'Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models.'),
(77116, 2012, 'bio', 'Systematic comparison of RNA-Seq normalization methods using measurement error models', 'Sun, Zhaonan|Zhu, Yu', 'Department of Statistics, Purdue University, West Lafayette, IN 47906, USA. sunz@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47906, USA. sunz@purdue.edu', 'United States|United States', 13, 'Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Real-Time Polymerase Chain Reaction|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bioinformatics/bts497', '22914217', 'Further advancement of RNA-Seq technology and its application call for the development of effective normalization methods for RNA-Seq data. Currently, different normalization methods are compared and validated by their correlations with a certain gold standard. Gene expression measurements generated by a different technology or platform such as Real-time reverse transcription polymerase chain reaction (qRT-PCR) or Microarray are usually used as the gold standard. Although the current approach is intuitive and easy to implement, it becomes statistically inadequate when the gold standard is also subject to measurement error (ME). Furthermore, the current approach is not informative, because the correlation of a normalization method with a certain gold standard does not provide much information about the exact quality of the normalized RNA-Seq measurements.'),
(77117, 2012, 'bio', 'Qualimap: evaluating next-generation sequencing alignment data', 'García-Alcalde, Fernando|Okonechnikov, Konstantin|Carbonell, José|Cruz, Luis M|Götz, Stefan|Tarazona, Sonia|Dopazo, Joaquín|Meyer, Thomas F|Conesa, Ana', 'Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Genomics|High-Throughput Nucleotide Sequencing|Humans|Quality Control|Sequence Alignment|Software', 'Evaluation Studies', '10.1093/bioinformatics/bts503', '22914218', 'The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses.'),
(77118, 2012, 'bio', 'Intensity quantile estimation and mapping--a novel algorithm for the correction of image non-uniformity bias in HCS data', 'Lo, Ernest|Soleilhac, Emmanuelle|Martinez, Anne|Lafanechère, Laurence|Nadon, Robert', 'Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|HeLa Cells|Humans|Image Processing, Computer-Assisted|Microtubules', 'Journal Article', '10.1093/bioinformatics/bts491', '22914219', 'Image non-uniformity (NU) refers to systematic, slowly varying spatial gradients in images that result in a bias that can affect all downstream image processing, quantification and statistical analysis steps. Image NU is poorly modeled in the field of high-content screening (HCS), however, such that current conventional correction algorithms may be either inappropriate for HCS or fail to take advantage of the information available in HCS image data.'),
(77119, 2012, 'bio', 'shortran: a pipeline for small RNA-seq data analysis', 'Gupta, Vikas|Markmann, Katharina|Pedersen, Christian N S|Stougaard, Jens|Andersen, Stig U', 'Centre for Carbohydrate Recognition and Signalling, Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, Denmark. vgupta@cs.au.dk|Centre for Carbohydrate Recognition and Signalling, Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, Denmark. vgupta@cs.au.dk|Centre for Carbohydrate Recognition and Signalling, Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, Denmark. vgupta@cs.au.dk|Centre for Carbohydrate Recognition and Signalling, Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, Denmark. vgupta@cs.au.dk|Centre for Carbohydrate Recognition and Signalling, Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, Denmark. vgupta@cs.au.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 18, 'Arabidopsis|Data Mining|Genomics|Molecular Sequence Annotation|RNA, Small Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bts496', '22914220', 'High-throughput sequencing currently generates a wealth of small RNA (sRNA) data, making data mining a topical issue. Processing of these large data sets is inherently multidimensional as length, abundance, sequence composition, and genomic location all hold clues to sRNA function. Analysis can be challenging because the formulation and testing of complex hypotheses requires combined use of visualization, annotation and abundance profiling. To allow flexible generation and querying of these disparate types of information, we have developed the shortran pipeline for analysis of plant or animal short RNA sequencing data. It comprises nine modules and produces both graphical and MySQL format output.'),
(77120, 2012, 'bio', 'An empirical Bayes mixture model for SNP detection in pooled sequencing data', 'Zhou, Baiyu', 'Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA. baiyu.zhou@einstein.yu.edu', 'United States', 5, 'Bayes Theorem|Gene Frequency|Genomics|Humans|Models, Statistical|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/bts501', '22914221', 'Detecting single-nucleotide polymorphism (SNP) in pooled sequencing data is more challenging than in individual sequencing because of sampling variations across pools. To effectively differentiate SNP signal from sequencing error, appropriate estimation of the sequencing error is necessary. In this article, we propose an empirical Bayes mixture (EBM) model for SNP detection and allele frequency estimation in pooled sequencing data.'),
(77121, 2012, 'bio', 'Identification of trans-acting siRNAs and their regulatory cascades in grapevine', 'Zhang, Changqing|Li, Guangping|Wang, Jin|Fang, Jinggui', 'College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China. zhang_chq2002@sohu.com|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China. zhang_chq2002@sohu.com|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China. zhang_chq2002@sohu.com|College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China. zhang_chq2002@sohu.com', 'China|China|China|China', 35, 'Computational Biology|Gene Expression Regulation, Plant|Genes, Plant|Genetic Loci|RNA, Small Interfering|Vitis', 'Journal Article', '10.1093/bioinformatics/bts500', '22914222', 'Trans-acting small interfering RNAs (ta-siRNAs) play an essential role in the regulation of plant gene expression, but relevant reports are still limited. Bioinformatic analyses indicate that many ta-siRNA-producing loci (TASs) are present in plants, implying the existence of as yet undiscovered ta-siRNAs and related regulatory pathways. To expand our knowledge of these plant gene regulators, we applied high-throughput computational and experimental methods to grapevine (Vitis vinifera L.).'),
(77122, 2012, 'bio', 'Positive-unlabeled learning for disease gene identification', 'Yang, Peng|Li, Xiao-Li|Mei, Jian-Ping|Kwoh, Chee-Keong|Ng, See-Kiong', 'Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore. yang0293@e.ntu.edu.sg|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore. yang0293@e.ntu.edu.sg|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore. yang0293@e.ntu.edu.sg|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore. yang0293@e.ntu.edu.sg|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore. yang0293@e.ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 87, 'Algorithms|Artificial Intelligence|Disease|Genes|Humans|Support Vector Machine', 'Evaluation Studies', '10.1093/bioinformatics/bts504', '22923290', 'Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not exist) to build classifiers to identify new disease genes from the unknown genes. However, such kind of classifiers is actually built from a noisy negative set N as there can be unknown disease genes in N itself. As a result, the classifiers do not perform as well as they could be.'),
(77123, 2012, 'bio', 'EFICAz25: application of a high-precision enzyme function predictor to 396 proteomes', 'Kumar, Narendra|Skolnick, Jeffrey', 'Center for Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.', 'Georgia|Georgia', 0, 'Databases, Genetic|Enzymes|Genome|Humans|Molecular Sequence Annotation|Proteome|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bts510', '22923291', 'High-quality enzyme function annotation is essential for understanding the biochemistry, metabolism and disease processes of organisms. Previously, we developed a multi-component high-precision enzyme function predictor, EFICAz(2) (enzyme function inference by a combined approach). Here, we present an updated improved version, EFICAz(2.5), that is trained on a significantly larger data set of enzyme sequences and PROSITE patterns. We also present the results of the application of EFICAz(2.5) to the enzyme reannotation of 396 genomes cataloged in the ENSEMBL database.'),
(77124, 2012, 'bio', 'Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks', 'Feiglin, Ariel|Hacohen, Adar|Sarusi, Avital|Fisher, Jasmin|Unger, Ron|Ofran, Yanay', 'The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.|The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.|The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.|The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.|The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.|The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Databases, Protein|ErbB Receptors|Gene Regulatory Networks|Models, Biological|Phenotype|Phosphatidylinositol 3-Kinases|Signal Transduction|Tumor Necrosis Factor-alpha', 'Comparative Study', '10.1093/bioinformatics/bts517', '22923292', 'Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.'),
(77125, 2012, 'bio', 'TRFolder-W: a web server for telomerase RNA structure prediction in yeast genomes', 'Zhang, Dong|Xue, Xingran|Malmberg, Russell L|Cai, Liming', 'Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia', 1, 'Genome, Fungal|Internet|Nucleic Acid Conformation|RNA|Software|Telomerase|Yeasts', 'Journal Article', '10.1093/bioinformatics/bts506', '22923293', 'TRFolder-W is a web server capable of predicting core structures of telomerase RNA (TR) in yeast genomes. TRFolder is a command-line Python toolkit for TR-specific structure prediction. We developed a web-version built on the django web framework, leveraging the work done previously, to include enhancements to increase flexibility of usage. To date, there are five core sub-structures commonly found in TR of fungal species, which are the template region, downstream pseudoknot, boundary element, core-closing stem and triple helix. The aim of TRFolder-W is to use the five core structures as fundamental units to predict potential TR genes for yeast, and to provide a user-friendly interface. Moreover, the application of TRFolder-W can be extended to predict the characteristic structure on species other than fungal species.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(77126, 2012, 'bio', 'The GlycomeAtlas tool for visualizing and querying glycome data', 'Konishi, Yoshitsugu|Aoki-Kinoshita, Kiyoko F', 'Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan.|Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan.', 'Japan|Japan', 18, 'Algorithms|Animals|Atlases as Topic|Data Display|Data Mining|Equipment Design|Glycomics|Humans|Information Storage and Retrieval|Internet|Mass Spectrometry|Mice|Molecular Structure|Polysaccharides|Tandem Mass Spectrometry|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts516', '22923294', 'The development of glycomics technologies in recent years has produced a sufficient amount of data to begin analyzing the glycan structures present in various organisms and tissues. In particular, glycan profiling using mass spectrometry (MS) and tandem MS has generated a large amount of data that are waiting to be analyzed. The Consortium for Functional Glycomics (CFG) has provided a web resource for obtaining such glycan profiling data easily. Although an interactive spectrum viewer is provided on the website as a Java applet, it is not necessarily easy to search for particular glycans or to find commonalities between different tissues in a single organism, for example. Therefore, to allow users to better take advantage of the valuable glycome data that can be obtained from mass spectra and other leading technologies, we have developed a tool called Glycome Atlas which is pre-loaded with the data from the CFG and is also able to visualize local glycan profiling data for human and mouse.'),
(77127, 2012, 'bio', 'RazerS 3: faster, fully sensitive read mapping', 'Weese, David|Holtgrewe, Manuel|Reinert, Knut', 'Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. david.weese@fu-berlin.de|Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. david.weese@fu-berlin.de|Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. david.weese@fu-berlin.de', 'Germany|Germany|Germany', 74, 'Algorithms|Chromosome Mapping|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1093/bioinformatics/bts505', '22923295', 'During the past years, next-generation sequencing has become a key technology for many applications in the biomedical sciences. Throughput continues to increase and new protocols provide longer reads than currently available. In almost all applications, read mapping is a first step. Hence, it is crucial to have algorithms and implementations that perform fast, with high sensitivity, and are able to deal with long reads and a large absolute number of insertions and deletions.'),
(77128, 2012, 'bio', 'HiTC: exploration of high-throughput C experiments', 'Servant, Nicolas|Lajoie, Bryan R|Nora, Elphège P|Giorgetti, Luca|Chen, Chong-Jian|Heard, Edith|Dekker, Job|Barillot, Emmanuel', 'Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr|Institut Curie, F-75248 Paris, France. nicolas.servant@curie.fr', 'France|France|France|France|France|France|France|France', 72, 'Animals|Chromosome Mapping|Chromosomes, Human, Pair 14|Data Display|Humans|Mice|Molecular Conformation|Restriction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/bts521', '22923296', 'The R/Bioconductor package HiTC facilitates the exploration of high-throughput 3C-based data. It allows users to import and export C data, to transform, normalize, annotate and visualize interaction maps. The package operates within the Bioconductor framework and thus offers new opportunities for future development in this field.'),
(77129, 2012, 'bio', 'Information-theoretic analysis and prediction of protein atomic burials: on the search for an informational intermediate between sequence and structure', 'Rocha, Juliana R|van der Linden, Marx G|Ferreira, Diogo C|Azevêdo, Paulo H|Pereira de Araújo, Antônio F', 'Laboratório de Biologia Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brasília-DF 70910-900, Brazil.|Laboratório de Biologia Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brasília-DF 70910-900, Brazil.|Laboratório de Biologia Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brasília-DF 70910-900, Brazil.|Laboratório de Biologia Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brasília-DF 70910-900, Brazil.|Laboratório de Biologia Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brasília-DF 70910-900, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 5, 'Algorithms|Amino Acid Sequence|Bayes Theorem|Entropy|Markov Chains|Models, Molecular|Models, Statistical|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts512', '22923297', 'It has been recently suggested that atomic burials, as expressed by molecular central distances, contain sufficient information to determine the tertiary structure of small globular proteins. A possible approach to structural determination from sequence could therefore involve a sequence-to-burial intermediate prediction step whose accuracy, however, is theoretically limited by the mutual information between these two variables. We use a non-redundant set of globular protein structures to estimate the mutual information between local amino acid sequence and atomic burials. Discretizing central distances of or atoms in equiprobable burial levels, we estimate relevant mutual information measures that are compared with actual predictions obtained from a Naive Bayesian Classifier (NBC) and a Hidden Markov Model (HMM).'),
(77130, 2012, 'bio', 'MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis', 'Kumar, Sudhir|Stecher, Glen|Peterson, Daniel|Tamura, Koichiro', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA.', 'United States|United States|United States|United States', 0, 'Evolution, Molecular|Likelihood Functions|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts507', '22923298', 'There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGAs graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis.'),
(77131, 2012, 'bio', 'GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data', 'Feng, Jianxing|Meyer, Clifford A|Wang, Qian|Liu, Jun S|Shirley Liu, X|Zhang, Yong', 'Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.|Department of Bioinformatics, School of Life sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.', 'China|China|China|China|China|China', 201, 'Algorithms|Base Sequence|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Models, Molecular|Oligonucleotide Array Sequence Analysis|RNA|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bioinformatics/bts515', '22923299', 'RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available.'),
(77132, 2012, 'bio', 'RIsearch: fast RNA-RNA interaction search using a simplified nearest-neighbor energy model', 'Wenzel, Anne|Akbasli, Erdinç|Gorodkin, Jan', 'Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg, Denmark.|Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg, Denmark.|Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg, Denmark.', 'Denmark|Denmark|Denmark', 47, 'Algorithms|Base Pairing|Base Sequence|Binding Sites|Cluster Analysis|Computer Simulation|Genes, Duplicate|Information Storage and Retrieval|MicroRNAs|Models, Molecular|Position-Specific Scoring Matrices|RNA|RNA, Bacterial|RNA, Messenger|RNA, Untranslated|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/bts519', '22923300', 'Regulatory, non-coding RNAs often function by forming a duplex with other RNAs. It is therefore of interest to predict putative RNA-RNA duplexes in silico on a genome-wide scale. Current computational methods for predicting these interactions range from fast complementary-based searches to those that take intramolecular binding into account. Together these methods constitute a trade-off between speed and accuracy, while leaving room for improvement within the context of genome-wide screens. A fast pre-filtering of putative duplexes would therefore be desirable.'),
(77133, 2012, 'bio', 'Bayesian inference of signaling network topology in a cancer cell line', 'Hill, Steven M|Lu, Yiling|Molina, Jennifer|Heiser, Laura M|Spellman, Paul T|Speed, Terence P|Gray, Joe W|Mills, Gordon B|Mukherjee, Sach', 'Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.|Department of Biochemistry, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Area Under Curve|Bayes Theorem|Breast Neoplasms|Cell Communication|Cell Line, Tumor|Computer Simulation|Female|Humans|Models, Molecular|Models, Statistical|Probability|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bts514', '22923301', 'Protein signaling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. To shed light on signaling network topology in specific contexts, such as cancer, requires interrogation of multiple proteins through time and statistical approaches to make inferences regarding network structure.'),
(77134, 2012, 'bio', 'HSPIR: a manually annotated heat shock protein information resource', 'R, Ratheesh Kumar|N S, Nagarajan|S P, Arunraj|Sinha, Devanjan|Veedin Rajan, Vinoth Babu|Esthaki, Vinoth Kumar|DSilva, Patrick', 'Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.|Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.', 'India|India|India|India|India|India|India', 0, 'Chaperonin 60|Databases, Protein|HSP40 Heat-Shock Proteins|HSP70 Heat-Shock Proteins|HSP90 Heat-Shock Proteins|Heat-Shock Proteins|Heat-Shock Proteins, Small|Humans|Information Storage and Retrieval|Molecular Sequence Annotation|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts520', '22923302', 'Heat shock protein information resource (HSPIR) is a concerted database of six major heat shock proteins (HSPs), namely, Hsp70, Hsp40, Hsp60, Hsp90, Hsp100 and small HSP. The HSPs are essential for the survival of all living organisms, as they protect the conformations of proteins on exposure to various stress conditions. They are a highly conserved group of proteins involved in diverse physiological functions, including de novo folding, disaggregation and protein trafficking. Moreover, their critical role in the control of disease progression made them a prime target of research. Presently, limited information is available on HSPs in reference to their identification and structural classification across genera. To that extent, HSPIR provides manually curated information on sequence, structure, classification, ontology, domain organization, localization and possible biological functions extracted from UniProt, GenBank, Protein Data Bank and the literature. The database offers interactive search with incorporated tools, which enhances the analysis. HSPIR is a reliable resource for researchers exploring structure, function and evolution of HSPs.'),
(77135, 2012, 'bio', 'COMPADRE: an R and web resource for pathway activity analysis by component decompositions', 'Ramos-Rodriguez, Roberto-Rafael|Cuevas-Diaz-Duran, Raquel|Falciani, Francesco|Tamez-Peña, Jose-Gerardo|Trevino, Victor', 'Cátedra of Bioinformática and Department of Computer Sciences, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Nuevo León, México.|Cátedra of Bioinformática and Department of Computer Sciences, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Nuevo León, México.|Cátedra of Bioinformática and Department of Computer Sciences, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Nuevo León, México.|Cátedra of Bioinformática and Department of Computer Sciences, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Nuevo León, México.|Cátedra of Bioinformática and Department of Computer Sciences, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Nuevo León, México.', '-|-|-|-|-', 0, 'Algorithms|Gene Expression|Gene Expression Profiling|Genomics|Internet|Principal Component Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bts513', '22923303', 'The analysis of biological networks has become essential to study functional genomic data. Compadre is a tool to estimate pathway/gene sets activity indexes using sub-matrix decompositions for biological networks analyses. The Compadre pipeline also includes one of the direct uses of activity indexes to detect altered gene sets. For this, the gene expression sub-matrix of a gene set is decomposed into components, which are used to test differences between groups of samples. This procedure is performed with and without differentially expressed genes to decrease false calls. During this process, Compadre also performs an over-representation test. Compadre already implements four decomposition methods [principal component analysis (PCA), Isomaps, independent component analysis (ICA) and non-negative matrix factorization (NMF)], six statistical tests (t- and f-test, SAM, Kruskal-Wallis, Welch and Brown-Forsythe), several gene sets (KEGG, BioCarta, Reactome, GO and MsigDB) and can be easily expanded. Our simulation results shown in Supplementary Information suggest that Compadre detects more pathways than over-representation tools like David, Babelomics and Webgestalt and less false positives than PLAGE. The output is composed of results from decomposition and over-representation analyses providing a more complete biological picture. Examples provided in Supplementary Information show the utility, versatility and simplicity of Compadre for analyses of biological networks.'),
(77136, 2012, 'bio', 'Qualitative translation of relations from BioPAX to SBML qual', 'Büchel, Finja|Wrzodek, Clemens|Mittag, Florian|Dräger, Andreas|Eichner, Johannes|Rodriguez, Nicolas|Le Novère, Nicolas|Zell, Andreas', 'Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany. finja.buechel@uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 16, 'Databases, Factual|Humans|Internet|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bts508', '22923304', 'The biological pathway exchange language (BioPAX) and the systems biology markup language (SBML) belong to the most popular modeling and data exchange languages in systems biology. The focus of SBML is quantitative modeling and dynamic simulation of models, whereas the BioPAX specification concentrates mainly on visualization and qualitative analysis of pathway maps. BioPAX describes reactions and relations. In contrast, SBML core exclusively describes quantitative processes such as reactions. With the SBML qualitative models extension (qual), it has recently also become possible to describe relations in SBML. Before the development of SBML qual, relations could not be properly translated into SBML. Until now, there exists no BioPAX to SBML converter that is fully capable of translating both reactions and relations.'),
(77137, 2012, 'bio', 'Optimal timepoint sampling in high-throughput gene expression experiments', 'Rosa, Bruce A|Zhang, Ji|Major, Ian T|Qin, Wensheng|Chen, Jin', 'Biorefining Research Institute and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, Canada ON P7B 5E1.|Biorefining Research Institute and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, Canada ON P7B 5E1.|Biorefining Research Institute and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, Canada ON P7B 5E1.|Biorefining Research Institute and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, Canada ON P7B 5E1.|Biorefining Research Institute and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, Canada ON P7B 5E1.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Amino Acids|Arabidopsis|Bacterial Toxins|Cost-Benefit Analysis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Plant|Indenes|Multigene Family', 'Evaluation Studies', '10.1093/bioinformatics/bts511', '22923305', 'Determining the best sampling rates (which maximize information yield and minimize cost) for time-series high-throughput gene expression experiments is a challenging optimization problem. Although existing approaches provide insight into the design of optimal sampling rates, our ability to utilize existing differential gene expression data to discover optimal timepoints is compelling.'),
(77138, 2012, 'bio', 'Exploring spatial patterns of gene expression from fruit fly embryogenesis on the iPhone', 'Kumar, Sudhir|Boccia, Kelly|McCutchan, Michael|Ye, Jieping', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA. s.kumar@asu.edu', 'United States|United States|United States|United States', 4, 'Algorithms|Animals|Body Patterning|Cell Phone|Data Display|Drosophila melanogaster|Embryonic Development|Gene Expression Profiling|Gene Expression Regulation, Developmental|Information Storage and Retrieval|Multigene Family|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts518', '22923306', 'Mobile technologies provide unique opportunities for ubiquitous distribution of scientific information through user-friendly interfaces. Therefore, we have developed a new FlyExpress mobile application that makes available a growing collection (>100 000) of standardized in situ hybridization images containing spatial patterns of gene expression from Drosophila melanogaster (fruit fly) embryogenesis. Using this application, scientists can visualize and compare expression patterns of >4000 developmentally relevant genes. The FlyExpress app displays the expression patterns of the selected gene for different visual projections (e.g. lateral) and displays them according to their developmental stages, which shows a genes progression of spatial expression over developmental time. Ultimately, we envision the use of FlyExpress app in the laboratory where scientists may wish to immediately conduct a visual comparison of a known expression pattern with the one observed on the bench top or to display expression patterns of interest during scientific discussions at large.'),
(77139, 2012, 'bio', 'FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment', 'Ma, Haisu|Zhao, Hongyu', 'Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Cell Line, Tumor|Drug Evaluation, Preclinical|Gene Expression Profiling|Genome|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bts502', '22923307', 'It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to understand drugs mechanism of action. Therefore, it is of great interest to analyze this type of transcriptomic profiling data for the identification of pathways responsive to different drugs. However, few computational tools exist for this task.'),
(77140, 2012, 'bio', 'Improved gap size estimation for scaffolding algorithms', 'Sahlin, Kristoffer|Street, Nathaniel|Lundeberg, Joakim|Arvestad, Lars', 'Department of Computational Biology, mKTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, Sweden. ksahlin@csc.kth.se|Department of Computational Biology, mKTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, Sweden. ksahlin@csc.kth.se|Department of Computational Biology, mKTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, Sweden. ksahlin@csc.kth.se|Department of Computational Biology, mKTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, Sweden. ksahlin@csc.kth.se', 'Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Contig Mapping|Gene Library|Genomics|Likelihood Functions|Models, Genetic|Probability|Regression Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bts441', '22923455', 'One of the important steps of genome assembly is scaffolding, in which contigs are linked using information from read-pairs. Scaffolding provides estimates about the order, relative orientation and distance between contigs. We have found that contig distance estimates are generally strongly biased and based on false assumptions. Since erroneous distance estimates can mislead in subsequent analysis, it is important to provide unbiased estimation of contig distance.'),
(77141, 2012, 'bio', 'FSelector: a Ruby gem for feature selection', 'Cheng, Tiejun|Wang, Yanli|Bryant, Stephen H', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.', 'United States|United States|United States', 18, 'Algorithms|Artificial Intelligence|Computational Biology|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/bts528', '22942017', 'The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation.'),
(77142, 2012, 'bio', 'Antisense transcription is coupled to nucleosome occupancy in sense promoters', 'Dai, Zhiming|Dai, Xianhua', 'Department of Electronics, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China. zhimdai@gmail.com|Department of Electronics, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China. zhimdai@gmail.com', 'China|China', 0, 'Antisense Elements (Genetics)|Down-Regulation|Humans|Nucleosomes|Open Reading Frames|Promoter Regions, Genetic|RNA Polymerase II|RNA, Untranslated|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bts534', '22942018', 'Genome-wide pervasive transcription is widespread in eukaryotes, revealing an extensive array of antisense transcription that involves hundreds of previously unknown non-coding RNAs. Individual cases have shown that antisense transcription influences sense transcription, however, genome-wide mechanisms of how antisense transcription regulates sense transcription remain to be elucidated.'),
(77143, 2012, 'bio', 'A robust model for read count data in exome sequencing experiments and implications for copy number variant calling', 'Plagnol, Vincent|Curtis, James|Epstein, Michael|Mok, Kin Y|Stebbings, Emma|Grigoriadou, Sofia|Wood, Nicholas W|Hambleton, Sophie|Burns, Siobhan O|Thrasher, Adrian J|Kumararatne, Dinakantha|Doffinger, Rainer|Nejentsev, Sergey', 'UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk|UCL Genetics Institute, UCL, London, UK. v.plagnol@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Exome|False Negative Reactions|GATA2 Transcription Factor|Gene Deletion|Gene Dosage|Genetic Variation|Guanine Nucleotide Exchange Factors|Humans|Immunologic Deficiency Syndromes|Markov Chains|Models, Molecular|Models, Statistical|Molecular Sequence Data|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bts526', '22942019', 'Exome sequencing has proven to be an effective tool to discover the genetic basis of Mendelian disorders. It is well established that copy number variants (CNVs) contribute to the etiology of these disorders. However, calling CNVs from exome sequence data is challenging. A typical read depth strategy consists of using another sample (or a combination of samples) as a reference to control for the variability at the capture and sequencing steps. However, technical variability between samples complicates the analysis and can create spurious CNV calls.'),
(77144, 2012, 'bio', 'Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB', 'Xu, Qifang|Dunbrack, Roland L', 'Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA.|Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA.', 'United States|United States', 40, 'Algorithms|Amino Acid Sequence|Databases, Factual|Databases, Protein|Information Storage and Retrieval|Models, Molecular|Models, Statistical|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/bts533', '22942020', 'Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed.'),
(77145, 2012, 'bio', 'Biologistics--diffusion coefficients for complete proteome of Escherichia coli', 'Kalwarczyk, Tomasz|Tabaka, Marcin|Holyst, Robert', 'Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.|Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.|Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.', 'Poland|Poland|Poland', 0, 'Biological Transport|Databases, Factual|Databases, Protein|Diffusion|Escherichia coli|Escherichia coli Proteins|Eukaryotic Cells|Molecular Weight|Proteome', 'Journal Article', '10.1093/bioinformatics/bts537', '22942021', 'Biologistics provides data for quantitative analysis of transport (diffusion) processes and their spatio-temporal correlations in cells. Mobility of proteins is one of the few parameters necessary to describe reaction rates for gene regulation. Although understanding of diffusion-limited biochemical reactions in vivo requires mobility data for the largest possible number of proteins in their native forms, currently, there is no database that would contain the complete information about the diffusion coefficients (DCs) of proteins in a given cell type.'),
(77146, 2012, 'bio', 'Statistical challenges associated with detecting copy number variations with next-generation sequencing', 'Teo, Shu Mei|Pawitan, Yudi|Ku, Chee Seng|Chia, Kee Seng|Salim, Agus', 'Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.|Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.|Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.|Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.|Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 121, 'Algorithms|Base Composition|Base Sequence|Chromosome Mapping|DNA Copy Number Variations|Gene Deletion|Gene Duplication|Quality Control|Selection Bias|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bts535', '22942022', 'Analysing next-generation sequencing (NGS) data for copy number variations (CNVs) detection is a relatively new and challenging field, with no accepted standard protocols or quality control measures so far. There are by now several algorithms developed for each of the four broad methods for CNV detection using NGS, namely the depth of coverage (DOC), read-pair, split-read and assembly-based methods. However, because of the complexity of the genome and the short read lengths from NGS technology, there are still many challenges associated with the analysis of NGS data for CNVs, no matter which method or algorithm is used.'),
(77147, 2012, 'bio', 'CSB: a Python framework for structural bioinformatics', 'Kalev, Ivan|Mechelke, Martin|Kopec, Klaus O|Holder, Thomas|Carstens, Simeon|Habeck, Michael', 'Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com|Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com|Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com|Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com|Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com|Department of Protein Evolution, Max Planck Institute for Developmental Biology, 3572076 Tübingen, Germany. ivan.kalev@gmail.com', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Computational Biology|DNA|Humans|Programming Languages|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bts538', '22942023', 'Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice.'),
(77148, 2012, 'bio', 'Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads', 'Chong, Zechen|Ruan, Jue|Wu, Chung-I', 'Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, Peoples Republic of China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, Peoples Republic of China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, Peoples Republic of China.', 'China|China|China', 0, 'Abstracting and Indexing as Topic|Algorithms|Base Sequence|Cluster Analysis|Evolution, Molecular|Genetics, Population|Haplotypes|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/bts482', '22942077', 'The innovation of restriction-site associated DNA sequencing (RAD-seq) method takes full advantage of next-generation sequencing technology. By clustering paired-end short reads into groups with their own unique tags, RAD-seq assembly problem is divided into subproblems. Fast and accurately clustering and assembling millions of RAD-seq reads with sequencing errors, different levels of heterozygosity and repetitive sequences is a challenging question.'),
(77149, 2012, 'bio', 'Interaction-based feature selection and classification for high-dimensional biological data', 'Wang, Haitian|Lo, Shaw-Hwa|Zheng, Tian|Hu, Inchi', 'Department of ISOM, HKUST, Clear Water Bay, Kowloon, Hong Kong.|Department of ISOM, HKUST, Clear Water Bay, Kowloon, Hong Kong.|Department of ISOM, HKUST, Clear Water Bay, Kowloon, Hong Kong.|Department of ISOM, HKUST, Clear Water Bay, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 24, 'Algorithms|Breast Neoplasms|Epistasis, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Testing|Humans|Logistic Models|Models, Statistical|Multigene Family|Neoplasm Recurrence, Local', 'Journal Article', '10.1093/bioinformatics/bts531', '22945786', 'Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion.'),
(77150, 2012, 'bio', 'Updating annotations with the distributed annotation system and the automated sequence annotation pipeline', 'Speier, William|Ochs, Michael F', 'Medical Imaging Informatics Group, University of California, Los Angeles, CA, USA. Speier@mii.ucla.edu|Medical Imaging Informatics Group, University of California, Los Angeles, CA, USA. Speier@mii.ucla.edu', 'United States|United States', 4, 'Algorithms|Computational Biology|Databases, Factual|High-Throughput Nucleotide Sequencing|Information Storage and Retrieval|Knowledge Bases|Molecular Sequence Annotation|Programming Languages|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bts530', '22945787', 'The integration between BioDAS ProServer and Automated Sequence Annotation Pipeline (ASAP) provides an interface for querying diverse annotation sources, chaining and linking results, and standardizing the output using the Distributed Annotation System (DAS) protocol. This interface allows pipeline plans in ASAP to be integrated into any system using HTTP and also allows the information returned by ASAP to be included in the DAS registry for use in any DAS-aware system. Three example implementations have been developed: the first accesses TRANSFAC information to automatically create gene sets for the Coordinated Gene Activity in Pattern Sets (CoGAPS) algorithm, the second integrates annotations from multiple array platforms and provides unified annotations in an R environment, and the third wraps the UniProt database for integration with the SPICE DAS client.'),
(77151, 2012, 'bio', 'Causal stability ranking', 'Stekhoven, Daniel J|Moraes, Izabel|Sveinbjörnsson, Gardar|Hennig, Lars|Maathuis, Marloes H|Bühlmann, Peter', 'Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch|Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch|Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch|Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch|Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch|Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland. stekhoven@stat.math.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 38, 'Arabidopsis|False Positive Reactions|Flowers|Gene Expression Profiling|Gene Knockout Techniques|Genes, Regulator|Genomic Instability|Genotype|Models, Genetic|Phenotype|ROC Curve|Regression Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/bts523', '22945788', 'Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method that can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time, we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Second, we compared CStaR to established regression-based methods on a gene expression dataset of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications.'),
(77152, 2012, 'bio', 'JEnsembl: a version-aware Java API to Ensembl data systems', 'Paterson, Trevor|Law, Andy', 'Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK. trevor.paterson@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK. trevor.paterson@roslin.ed.ac.uk', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Genomic Library|Indonesia|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/bts525', '22945789', 'The Ensembl Project provides release-specific Perl APIs for efficient high-level programmatic access to data stored in various Ensembl database schema. Although Perl scripts are perfectly suited for processing large volumes of text-based data, Perl is not ideal for developing large-scale software applications nor embedding in graphical interfaces. The provision of a novel Java API would facilitate type-safe, modular, object-orientated development of new Bioinformatics tools with which to access, analyse and visualize Ensembl data.'),
(77153, 2012, 'bio', 'Post-translational modifications induce significant yet not extreme changes to protein structure', 'Xin, Fuxiao|Radivojac, Predrag', 'School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.', 'India|India', 77, 'Acetylation|Animals|Databases, Protein|Glycosylation|Humans|Methylation|Phosphorylation|Protein Processing, Post-Translational|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/bts541', '22947645', 'A number of studies of individual proteins have shown that post-translational modifications (PTMs) are associated with structural rearrangements of their target proteins. Although such studies provide critical insights into the mechanics behind the dynamic regulation of protein function, they usually feature examples with relatively large conformational changes. However, with the steady growth of Protein Data Bank (PDB) and available PTM sites, it is now possible to more systematically characterize the role of PTMs as conformational switches. In this study, we ask (1) what is the expected extent of structural change upon PTM, (2) how often are those changes in fact substantial, (3) whether the structural impact is spatially localized or global and (4) whether different PTMs have different signatures.'),
(77154, 2012, 'bio', 'A linear programming model for protein inference problem in shotgun proteomics', 'Huang, Ting|He, Zengyou', 'School of Software, Dalian University of Technology, Dalian 116621, China.|School of Software, Dalian University of Technology, Dalian 116621, China.', 'China|China', 16, 'Algorithms|Databases, Protein|Humans|Linear Models|Mass Spectrometry|Peptides|Probability|Programming, Linear|Proteins|Proteomics|Yeasts', 'Journal Article', '10.1093/bioinformatics/bts540', '22954624', 'Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved.'),
(77155, 2012, 'bio', 'Performance reproducibility index for classification', 'Yousefi, Mohammadmahdi R|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 0, 'Algorithms|Bias|Biomarkers|Follow-Up Studies|Gene Expression Profiling|Genetic Markers|Humans|Models, Statistical|Pattern Recognition, Automated|Precision Medicine|Probability|Regression Analysis|Reproducibility of Results|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/bts509', '22954625', 'A common practice in biomarker discovery is to decide whether a large laboratory experiment should be carried out based on the results of a preliminary study on a small set of specimens. Consideration of the efficacy of this approach motivates the introduction of a probabilistic measure, for whether a classifier showing promising results in a small-sample preliminary study will perform similarly on a large independent sample. Given the error estimate from the preliminary study, if the probability of reproducible error is low, then there is really no purpose in substantially allocating more resources to a large follow-on study. Indeed, if the probability of the preliminary study providing likely reproducible results is small, then why even perform the preliminary study?');
