INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74715, 2009, 'bio', 'Structure similarity measure with penalty for close non-equivalent residues', 'Sadreyev, Ruslan I|Shi, ShuoYong|Baker, David|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.', 'United States|United States|United States|United States', 3, 'Computational Biology|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btp148', '19321733', 'Recent improvement in homology-based structure modeling emphasizes the importance of sensitive evaluation measures that help identify and correct modest distortions in models compared with the target structures. Global Distance Test Total Score (GDT_TS), otherwise a very powerful and effective measure for model evaluation, is still insensitive to and can even reward such distortions, as observed for remote homology modeling in the latest CASP8 (Comparative Assessment of Structure Prediction).'),
(74716, 2009, 'bio', 'GetBonNie for building, analyzing and sharing rule-based models', 'Hu, Bin|Matthew Fricke, G|Faeder, James R|Posner, Richard G|Hlavacek, William S', 'Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.', 'Mexico|Mexico|Mexico|Mexico|Mexico', 0, 'Computer Simulation|Databases, Factual|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp173', '19321734', 'GetBonNie is a web-based application for building, analyzing and sharing rule-based models encoded in the BioNetGen language (BNGL). Tools accessible within the GetBonNie environment include (i) an applet for drawing graphs that correspond to BNGL code, (ii) a network-generation engine for translating a set of rules into a chemical reaction network, (iii) simulation engines that implement generate-first, on-the-fly and network-free methods for simulating rule-based models, and (iv) a database for sharing models, parameter values, annotations, simulation tasks and results.'),
(74717, 2009, 'bio', 'In silico analysis of promoter regions from cold-induced genes in rice (Oryza sativa L) and Arabidopsis thaliana reveals the importance of combinatorial control', 'Lindlöf, Angelica|Bräutigam, Marcus|Chawade, Aakash|Olsson, Olof|Olsson, Björn', 'Systems Biology Research Centre, School of Life Sciences, University of Skövde, Skövde, Sweden. angelica.lindlof@his.se|Systems Biology Research Centre, School of Life Sciences, University of Skövde, Skövde, Sweden. angelica.lindlof@his.se|Systems Biology Research Centre, School of Life Sciences, University of Skövde, Skövde, Sweden. angelica.lindlof@his.se|Systems Biology Research Centre, School of Life Sciences, University of Skövde, Skövde, Sweden. angelica.lindlof@his.se|Systems Biology Research Centre, School of Life Sciences, University of Skövde, Skövde, Sweden. angelica.lindlof@his.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 19, 'Arabidopsis|Binding Sites|Cold Temperature|Computational Biology|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|Oryza', 'Journal Article', '10.1093/bioinformatics/btp172', '19321735', 'Cold acclimation involves a number of different cellular processes that together increase the freezing tolerance of an organism. The DREB1/CBFs are transcription factors (TFs) that are prominent in the regulation of cold responses in Arabidopsis thaliana, rice and many other crops. We investigated if the expression of DREB1/CBFs and co-expressed genes relies on combinatorial control by several TFs. Our results support this notion and indicate that methods for studying the regulation of complex cellular processes should include identification of combinations of motifs, in addition to searching for individual overrepresented binding sites.'),
(74718, 2009, 'bio', 'CNVVdb: a database of copy number variations across vertebrate genomes', 'Chen, Feng-Chi|Chen, Yen-Zho|Chuang, Trees-Juen', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County 350, Taipei, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County 350, Taipei, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County 350, Taipei, Taiwan.', '-|-|-', 0, 'Animals|Databases, Genetic|Gene Dosage|Genetic Variation|Genome|Genomics|Humans|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp166', '19321736', 'CNVVdb is a web interface for identification of putative copy number variations (CNVs) among 16 vertebrate species using the-same-species self-alignments and cross-species pairwise alignments. By querying genomic coordinates in the target species, all the potential paralogous/orthologous regions that overlap > or = 80-100% (adjustable) of the query sequences with user-specified sequence identity (> or = 60% ~ > or = 90%) are returned. Additional information is also given for the genes that are included in the returned regions, including gene description, alternatively spliced transcripts, gene ontology descriptions and other biologically important information. CNVVdb also provides information of pseudogenes and single nucleotide polymorphisms (SNPs) for the CNV-related genomic regions. Moreover, multiple sequence alignments of shared CNVs across species are also provided. With the combination of CNV, SNP, pseudogene and functional information, CNVVdb can be very useful for comparative and functional studies in vertebrates.'),
(74719, 2009, 'bio', 'StatQuant: a post-quantification analysis toolbox for improving quantitative mass spectrometry', 'van Breukelen, Bas|van den Toorn, Henk W P|Drugan, Madalina M|Heck, Albert J R', 'Biomolecular Mass Spectrometry and Proteomics Group, Utrecht University, Utrech, The Netherlands. b.vanbreukelen@uu.nl|Biomolecular Mass Spectrometry and Proteomics Group, Utrecht University, Utrech, The Netherlands. b.vanbreukelen@uu.nl|Biomolecular Mass Spectrometry and Proteomics Group, Utrecht University, Utrech, The Netherlands. b.vanbreukelen@uu.nl|Biomolecular Mass Spectrometry and Proteomics Group, Utrecht University, Utrech, The Netherlands. b.vanbreukelen@uu.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 34, 'Databases, Protein|Mass Spectrometry|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp181', '19336442', 'Mass spectrometric protein quantitation has emerged as a high-throughput tool to yield large amounts of data on peptide and protein abundances. Currently, differential abundance data can be calculated from peptide intensity ratios by several automated quantitation software packages available. There is, however, still a great need for additional processing to validate and refine the quantitation results. Here, we present a software tool, termed StatQuant, that offers a set of statistical tools to process, filter, compare and represent data from several quantitative proteomics software packages such as MSQuant. StatQuant offers the researcher post-processing methods to achieve improved confidence on the obtained protein ratios.'),
(74720, 2009, 'bio', 'Hooke: an open software platform for force spectroscopy', 'Sandal, Massimo|Benedetti, Fabrizio|Brucale, Marco|Gomez-Casado, Alberto|Samorì, Bruno', 'Department of Biochemistry, University of Bologna, Bologna, Italy. massimo.sandal@unibo.it|Department of Biochemistry, University of Bologna, Bologna, Italy. massimo.sandal@unibo.it|Department of Biochemistry, University of Bologna, Bologna, Italy. massimo.sandal@unibo.it|Department of Biochemistry, University of Bologna, Bologna, Italy. massimo.sandal@unibo.it|Department of Biochemistry, University of Bologna, Bologna, Italy. massimo.sandal@unibo.it', 'Italy|Italy|Italy|Italy|Italy', 21, 'Algorithms|Computational Biology|Internet|Microscopy, Atomic Force|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp180', '19336443', 'Hooke is an open source, extensible software intended for analysis of atomic force microscope (AFM)-based single molecule force spectroscopy (SMFS) data. We propose it as a platform on which published and new algorithms for SMFS analysis can be integrated in a standard, open fashion, as a general solution to the current lack of a standard software for SMFS data analysis. Specific features and support for file formats are coded as independent plugins. Any user can code new plugins, extending the software capabilities. Basic automated dataset filtering and semi-automatic analysis facilities are included.'),
(74721, 2009, 'bio', 'Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum', 'Jurgelenaite, Rasa|Dijkstra, Tjeerd M H|Kocken, Clemens H M|Heskes, Tom', 'Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. r.jurgelenaite@cmbi.ru.nl|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. r.jurgelenaite@cmbi.ru.nl|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. r.jurgelenaite@cmbi.ru.nl|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. r.jurgelenaite@cmbi.ru.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Animals|Computational Biology|DNA, Protozoan|Erythrocytes|Gene Expression Profiling|Gene Expression Regulation|Genome, Protozoan|Plasmodium falciparum|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp179', '19336444', 'To date, there is little knowledge about one of the processes fundamental to the biology of Plasmodium falciparum, gene regulation including transcriptional control. We use noisy threshold models to identify regulatory sequence elements explaining membership to a gene expression cluster where each cluster consists of genes active during the part of the developmental cycle inside a red blood cell. Our approach is both able to capture the combinatorial nature of gene regulation and to incorporate uncertainty about the functionality of putative regulatory sequence elements.'),
(74722, 2009, 'bio', 'KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways', 'Spasic, Irena|Simeonidis, Evangelos|Messiha, Hanan L|Paton, Norman W|Kell, Douglas B', 'Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK. i.spasic@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Computational Biology|Information Systems|Metabolic Networks and Pathways|Saccharomyces cerevisiae|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp175', '19336445', 'Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models.'),
(74723, 2009, 'bio', 'Network-based multiple locus linkage analysis of expression traits', 'Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu', 'United States', 0, 'Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genetic Linkage|Mice|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btp177', '19336446', 'We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as specified a priori by a gene pathway or network.'),
(74724, 2009, 'bio', 'Variable slope normalization of reverse phase protein arrays', 'Neeley, E Shannon|Kornblau, Steven M|Coombes, Kevin R|Baggerly, Keith A', 'Department of Statistics, Rice University, Houston, TX, USA. sneeley@stats.byu.edu|Department of Statistics, Rice University, Houston, TX, USA. sneeley@stats.byu.edu|Department of Statistics, Rice University, Houston, TX, USA. sneeley@stats.byu.edu|Department of Statistics, Rice University, Houston, TX, USA. sneeley@stats.byu.edu', 'United States|United States|United States|United States', 61, 'Algorithms|Computer Simulation|Databases, Protein|Protein Array Analysis|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp174', '19336447', 'Reverse phase protein arrays (RPPA) measure the relative expression levels of a protein in many samples simultaneously. A set of identically spotted arrays can be used to measure the levels of more than one protein. Protein expression within each sample on an array is estimated by borrowing strength across all the samples, but using only within array information. When comparing across slides, it is essential to account for sample loading, the total amount of protein printed per sample. Currently, total protein is estimated using either a housekeeping protein or the sample median across all slides. When the variability in sample loading is large, these methods are suboptimal because they do not account for the fact that the protein expression for each slide is estimated separately.'),
(74725, 2009, 'bio', 'IGG3: a tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis', 'Li, Miao-Xin|Jiang, Lin|Kao, Patrick Yu-Ping|Sham, Pak-C|Song, You-Qiang', 'Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com|Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com|Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com|Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com|Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 10, 'Algorithms|Databases, Genetic|Genome|Genome-Wide Association Study|Genomics|Genotype|Information Storage and Retrieval|Meta-Analysis as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btp183', '19346322', 'There is an urgent and increasing demand for integrating large genotype datasets across genome-wide association studies and HapMap project for whole-genome imputation and individual-level meta-analysis. A new algorithm was developed to efficiently merge raw genotypes across large datasets and implemented in the latest version of IGG, IGG3. In addition, IGG3 can integrate the latest phased and unphased HapMap genotypes and can flexibly generate complete sets of input files for six popular genotype imputation tools. We demonstrated the efficiency of IGG3 by simulation tests, which could rapidly merge genotypes in tens of thousands of large genotype chips (e.g. Affymetrix Genome-Wide Human SNP Array 6.0 and Illumina Human1m-duo) and in HapMap III project on an ordinary desktop computer.'),
(74726, 2009, 'bio', 'Identification of ribosomal RNA genes in metagenomic fragments', 'Huang, Ying|Gilna, Paul|Li, Weizhong', 'California Institute for Telecommunications and Information Technology, University of California, La Jolla, San Diego, California, USA.|California Institute for Telecommunications and Information Technology, University of California, La Jolla, San Diego, California, USA.|California Institute for Telecommunications and Information Technology, University of California, La Jolla, San Diego, California, USA.', 'United States|United States|United States', 0, 'Algorithms|Genes, rRNA|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btp161', '19346323', 'Identification of genes coding for ribosomal RNA (rRNA) is considered an important goal in the analysis of data from metagenomics projects. Here, we report the development of a software program designed for the identification of rRNA genes from metagenomic fragments based on hidden Markov models (HMMs). This program provides rRNA gene predictions with high sensitivity and specificity on artificially fragmented genomic DNAs.'),
(74727, 2009, 'bio', 'Globally optimal stitching of tiled 3D microscopic image acquisitions', 'Preibisch, Stephan|Saalfeld, Stephan|Tomancak, Pavel', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.', 'Germany|Germany|Germany', 949, 'Animals|Drosophila|Imaging, Three-Dimensional|Larva|Microscopy, Confocal', 'Journal Article', '10.1093/bioinformatics/btp184', '19346324', 'Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks.'),
(74728, 2009, 'bio', 'DnaSP v5: a software for comprehensive analysis of DNA polymorphism data', 'Librado, P|Rozas, J', 'Departament de Genètica, Facultat de Biologia and Institut de Recerca de la Biodiversitat, Universitat de Barcelona, Barcelona, Spain.|Departament de Genètica, Facultat de Biologia and Institut de Recerca de la Biodiversitat, Universitat de Barcelona, Barcelona, Spain.', 'Spain|Spain', 10297, 'Computational Biology|DNA|Genome|Haplotypes|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btp187', '19346325', 'DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files, (ii) haplotype phasing, (iii) analyses on insertion/deletion polymorphism data, (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.'),
(74729, 2009, 'bio', 'ELISA-BASE: an integrated bioinformatics tool for analyzing and tracking ELISA microarray data', 'White, Amanda M|Collett, James R|Seurynck-Servoss, Shannon L|Daly, Don S|Zangar, Richard C', 'Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Enzyme-Linked Immunosorbent Assay|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp182', '19346326', 'ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system.'),
(74730, 2009, 'bio', 'TEclass--a tool for automated classification of unknown eukaryotic transposable elements', 'Abrusán, György|Grundmann, Norbert|DeMester, Luc|Makalowski, Wojciech', 'Katholieke Universiteit Leuven, Department of Biology, Laboratory of Aquatic Ecology and Evolutionary Biology, Ch. Deberiotstraat 32, 3000 Leuven, Belgium. abrusan@uni-muenster.de|Katholieke Universiteit Leuven, Department of Biology, Laboratory of Aquatic Ecology and Evolutionary Biology, Ch. Deberiotstraat 32, 3000 Leuven, Belgium. abrusan@uni-muenster.de|Katholieke Universiteit Leuven, Department of Biology, Laboratory of Aquatic Ecology and Evolutionary Biology, Ch. Deberiotstraat 32, 3000 Leuven, Belgium. abrusan@uni-muenster.de|Katholieke Universiteit Leuven, Department of Biology, Laboratory of Aquatic Ecology and Evolutionary Biology, Ch. Deberiotstraat 32, 3000 Leuven, Belgium. abrusan@uni-muenster.de', 'Belgium|Belgium|Belgium|Belgium', 100, 'Computational Biology|DNA Transposable Elements|Eukaryotic Cells|Repetitive Sequences, Nucleic Acid|Short Interspersed Nucleotide Elements|Software|Terminal Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btp084', '19349283', 'The large number of sequenced genomes required the development of software that reconstructs the consensus sequences of transposons and other repetitive elements. However, the available tools usually focus on the accurate identification of raw repeats and provide no information about the taxonomic position of the reconstructed consensi. TEclass is a tool to classify unknown transposable elements into their four main functional categories, which reflect their mode of transposition: DNA transposons, long terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs). TEclass uses machine learning support vector machine (SVM) for classification based on oligomer frequencies. It achieves 90-97% accuracy in the classification of novel DNA and LTR repeats, and 75% for LINEs and SINEs.'),
(74731, 2009, 'bio', 'Data structures and algorithms for analysis of genetics of gene expression with Bioconductor: GGtools 3x', 'Carey, Vincent J|Davis, Adam R|Lawrence, Michael F|Gentleman, Robert|Raby, Benjamin A', 'Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Womens Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu|Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Womens Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu|Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Womens Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu|Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Womens Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu|Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Womens Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu', 'United States|United States|United States|United States|United States', 12, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genotype|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp169', '19349284', 'Associations between DNA polymorphisms and mRNA abundance are a natural target of genetic investigations, and microarrays facilitate genome-wide and transcriptome-wide surveys of these associations. This work is motivated by emerging requirements for data architectures and algorithm interfaces to allow flexible exploration of public and private archives of genotyping and expression arrays. Using R/Bioconductor facilities, Phase II HapMap genotypes and Illumina 47K expression assay results archived on multiple populations may be interactively explored and analyzed using commodity hardware.'),
(74732, 2009, 'bio', 'Evaluation of genome-wide association study results through development of ontology fingerprints', 'Tsoi, Lam C|Boehnke, Michael|Klein, Richard L|Zheng, W Jim', 'Bioinformatics Graduate Program, Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA.|Bioinformatics Graduate Program, Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA.|Bioinformatics Graduate Program, Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA.|Bioinformatics Graduate Program, Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA.', 'United States|United States|United States|United States', 0, 'Genome-Wide Association Study|Genotype|Linkage Disequilibrium|Lipid Metabolism|Phenotype|Proteins|PubMed', 'Journal Article', '10.1093/bioinformatics/btp158', '19349285', 'Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important.'),
(74733, 2009, 'bio', 'Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules', 'Meng, Jia|Gao, Shou-Jiang|Huang, Yufei', 'Department of ECE, University of Texas at San Antonio, Texas, USA.|Department of ECE, University of Texas at San Antonio, Texas, USA.|Department of ECE, University of Texas at San Antonio, Texas, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Herpesvirus 8, Human|Humans|Oligonucleotide Array Sequence Analysis|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btp235', '19351618', 'Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with time-series data, most conventional clustering algorithms, however, either use one-way clustering methods, which fail to consider the heterogeneity of temporary domain, or use two-way clustering methods that do not take into account the time dependency between samples, thus producing less informative results. Furthermore, enrichment analysis is often performed independent of and after clustering and such practice, though capable of revealing biological significant clusters, cannot guide the clustering to produce biologically significant result.'),
(74734, 2009, 'bio', 'ProbeMatch: rapid alignment of oligonucleotides to genome allowing both gaps and mismatches', 'Kim, You Jung|Teletia, Nikhil|Ruotti, Victor|Maher, Christopher A|Chinnaiyan, Arul M|Stewart, Ron|Thomson, James A|Patel, Jignesh M', 'Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.|Department of EECS, The University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Genome|Genomics|Oligonucleotides|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp178', '19351619', 'We have developed a tool, called ProbeMatch, for matching a large set of oligonucleotide sequences against a genome database using gapped alignments. Unlike most of the existing tools such as ELAND which only perform ungapped alignments allowing at most two mismatches, ProbeMatch generates both ungapped and gapped alignments allowing up to three errors including insertion, deletion and mismatch. To speedup sequence alignment, ProbeMatch uses gapped q-grams and q-grams of various patterns to identify target hits to a query sequence. This approach results in fewer initial sequences to examine with no loss in sensitivity. ProbeMatch has been used to align 169,095 Illumina GAII reads against the human genome, which could not be mapped by ELAND, and found alignments for 28,625 reads of the 169,095 reads in less than 3 h.'),
(74735, 2009, 'bio', 'Reduced amino acid alphabets exhibit an improved sensitivity and selectivity in fold assignment', 'Peterson, Eric L|Kondev, Jané|Theriot, Julie A|Phillips, Rob', 'Department of Physics, California Institute of Technology, Pasadena, CA 91125, USA.|Department of Physics, California Institute of Technology, Pasadena, CA 91125, USA.|Department of Physics, California Institute of Technology, Pasadena, CA 91125, USA.|Department of Physics, California Institute of Technology, Pasadena, CA 91125, USA.', 'United States|United States|United States|United States', 35, 'Algorithms|Amino Acid Sequence|Amino Acids|Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp164', '19351620', 'Many proteins with vastly dissimilar sequences are found to share a common fold, as evidenced in the wealth of structures now available in the Protein Data Bank. One idea that has found success in various applications is the concept of a reduced amino acid alphabet, wherein similar amino acids are clustered together. Given the structural similarity exhibited by many apparently dissimilar sequences, we undertook this study looking for improvements in fold recognition by comparing protein sequences written in a reduced alphabet.'),
(74736, 2009, 'bio', 'WebGBrowse--a web server for GBrowse', 'Podicheti, Ram|Gollapudi, Rajesh|Dong, Qunfeng', 'Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.', 'India|India|India', 15, 'Computational Biology|Genome|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp239', '19357095', 'The Generic Genome Browser (GBrowse) is one of the most widely used tools for visualizing genomic features along a reference sequence. However, the installation and configuration of GBrowse is not trivial for biologists. We have developed a web server, WebGBrowse that allows users to upload genome annotation in the GFF3 format, configure the display of each genomic feature by simply using a web browser and visualize the configured genomic features with the integrated GBrowse software.'),
(74737, 2009, 'bio', 'Cross species analysis of microarray expression data', 'Lu, Yong|Huggins, Peter|Bar-Joseph, Ziv', 'School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 74, 'Animals|Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btp247', '19357096', 'Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied.'),
(74738, 2009, 'bio', 'Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy', 'Tuncbag, Nurcan|Gursoy, Attila|Keskin, Ozlem', 'Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, Sariyer Istanbul, Turkey.|Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, Sariyer Istanbul, Turkey.|Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, Sariyer Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 0, 'Alanine|Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp240', '19357097', 'Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort, therefore, there is a need for computational methods to predict hot spots in protein interfaces.'),
(74739, 2009, 'bio', 'BIPA: a database for protein-nucleic acid interaction in 3D structures', 'Lee, Semin|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, Cambridge, UK. semin@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, Cambridge, UK. semin@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom', 0, 'Computational Biology|DNA|DNA-Binding Proteins|Databases, Protein|Protein Conformation|RNA|RNA-Binding Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp243', '19357098', 'BIPA is a database for protein-nucleic acid interactions in 3D structures. The database provides various physicochemical features of protein-nucleic acid interface such as size, shape, residue propensity, secondary structure composition and intermolecular interactions. The database also contains multiple structural alignments of nucleic acid-binding protein families with annotations of local environments in order to allow definition of features that influence acceptability of mutations at a particular position in a protein family. A web interface has been designed to present the results of these analyses and facilitate navigation of protein-nucleic acid interfaces.'),
(74740, 2009, 'bio', 'CloudBurst: highly sensitive read mapping with MapReduce', 'Schatz, Michael C', 'Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. mschatz@umiacs.umd.edu', 'United States', 0, 'Algorithms|Animals|Computational Biology|DNA|Genome|Humans|Internet|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp236', '19357099', 'Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read-mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics. It is modeled after the short read-mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes.'),
(74741, 2009, 'bio', 'ModLink+: improving fold recognition by using protein-protein interactions', 'Fornes, Oriol|Aragues, Ramon|Espadaler, Jordi|Marti-Renom, Marc A|Sali, Andrej|Oliva, Baldo', 'Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es|Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es|Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es|Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es|Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es|Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Catalonia, Spain. boliva@imim.es', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Folding|Protein Interaction Domains and Motifs|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp238', '19357100', 'Several strategies have been developed to predict the fold of a target protein sequence, most of which are based on aligning the target sequence to other sequences of known structure. Previously, we demonstrated that the consideration of protein-protein interactions significantly increases the accuracy of fold assignment compared with PSI-BLAST sequence comparisons. A drawback of our method was the low number of proteins to which a fold could be assigned. Here, we present an improved version of the method that addresses this limitation. We also compare our method to other state-of-the-art fold assignment methodologies.'),
(74742, 2009, 'bio', 'Automatic identification of species-specific repetitive DNA sequences and their utilization for detecting microbial organisms', 'Koressaar, Triinu|Jõers, Kai|Remm, Maido', 'Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.', 'Estonia|Estonia|Estonia', 11, 'Algorithms|Base Sequence|Computational Biology|DNA Primers|DNA, Bacterial|Genome|Molecular Sequence Data|Polymerase Chain Reaction|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btp241', '19357101', 'The concentration of pathogen DNA in biological samples is often very low. Therefore, the sensitivity of diagnostic tests is always a critical factor.'),
(74743, 2009, 'bio', 'A graphical algorithm for fast computation of identity coefficients and generalized kinship coefficients', 'Abney, Mark', 'Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. abney@bsd.uchicago.edu', 'United States', 15, 'Algorithms|Computational Biology|Computer Graphics|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btp185', '19359355', 'Computing the probability of identity by descent sharing among n genes given only the pedigree of those genes is a computationally challenging problem, if n or the pedigree size is large. Here, I present a novel graphical algorithm for efficiently computing all generalized kinship coefficients for n genes. The graphical description transforms the problem from doing many recursion on the pedigree to doing a single traversal of a structure referred to as the kinship graph.'),
(74744, 2009, 'bio', 'MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields', 'Xia, Xuefeng|Zhang, Song|Su, Yu|Sun, Zhirong', 'Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.|Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.|Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.|Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.', 'China|China|China|China', 0, 'Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp251', '19359356', 'Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue-residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets.'),
(74745, 2009, 'bio', 'LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures', 'Ryan, Michael|Diekhans, Mark|Lien, Stephanie|Liu, Yun|Karchin, Rachel', 'Department of Bioinformatics, George Mason University, Fairfax, VA, USA.|Department of Bioinformatics, George Mason University, Fairfax, VA, USA.|Department of Bioinformatics, George Mason University, Fairfax, VA, USA.|Department of Bioinformatics, George Mason University, Fairfax, VA, USA.|Department of Bioinformatics, George Mason University, Fairfax, VA, USA.', 'United States|United States|United States|United States|United States', 52, 'Computational Biology|Databases, Genetic|Databases, Protein|Humans|Internet|Polymorphism, Single Nucleotide|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp242', '19369493', 'LS-SNP/PDB is a new WWW resource for genome-wide annotation of human non-synonymous (amino acid changing) SNPs. It serves high-quality protein graphics rendered with UCSF Chimera molecular visualization software. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features.'),
(74746, 2009, 'bio', 'ProtVirDB: a database of protozoan virulent proteins', 'Ramana, Jayashree|Gupta, Dinesh', 'Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India.|Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India.', 'India|India', 5, 'Animals|Databases, Protein|Eukaryota|Internet|Protozoan Proteins|Sequence Analysis, Protein|Software|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btp258', '19369494', 'ProtVirDB is a comprehensive and user-friendly web-based knowledgebase of virulent proteins belonging to protozoan species. The database will facilitate research and provide an integrated platform for comparative studies of virulent proteins in different parasitic protozoans and organize them under a unifying classification schema with functional categories. Remarkably, one-third of the protein sequences in the database showed presence of either mono- or hetero-repeats, or both concomitantly--hence reiterating the importance of repeats in parasite virulence mechanisms. A number of useful bioinformatics tools including BLAST and tools for phylogenetic analysis are integrated with the database. With the rapidly burgeoning interest in the pathogenesis mechanisms of protozoans and ongoing genome sequencing projects, we anticipate that the database will be a useful tool for the research community.'),
(74747, 2009, 'bio', 'Bayesian inference of protein-protein interactions from biological literature', 'Chowdhary, Rajesh|Zhang, Jinfeng|Liu, Jun S', 'Department of Statistics, Harvard University, Cambridge, MA 02138, USA. jliu@stat.harvard.edu|Department of Statistics, Harvard University, Cambridge, MA 02138, USA. jliu@stat.harvard.edu|Department of Statistics, Harvard University, Cambridge, MA 02138, USA. jliu@stat.harvard.edu', 'United States|United States|United States', 0, 'Bayes Theorem|Binding Sites|Computational Biology|Databases, Protein|Information Storage and Retrieval|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp245', '19369495', 'Protein-protein interaction (PPI) extraction from published biological articles has attracted much attention because of the importance of protein interactions in biological processes. Despite significant progress, mining PPIs from literatures still rely heavily on time- and resource-consuming manual annotations.'),
(74748, 2009, 'bio', 'Many-core algorithms for statistical phylogenetics', 'Suchard, Marc A|Rambaut, Andrew', 'Department of Biomathematics, University of California, Los Angeles, CA 90095, USA. msuchard@ucla.edu|Department of Biomathematics, University of California, Los Angeles, CA 90095, USA. msuchard@ucla.edu', 'United States|United States', 250, 'Algorithms|Bayes Theorem|Codon|Computational Biology|Evolution, Molecular|Genome, Mitochondrial|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btp244', '19369496', 'Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74749, 2009, 'bio', 'MapView: visualization of short reads alignment on a desktop computer', 'Bao, Hua|Guo, Hui|Wang, Jinwei|Zhou, Renchao|Lu, Xuemei|Shi, Suhua', 'State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com|State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com|State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com|State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com|State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com|State Key Laboratory of Biocontrol and Key Laboratory of Gene Engineering of Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China. baohua100@hotmail.com', 'China|China|China|China|China|China', 0, 'Computational Biology|Computer Graphics|Gene Expression Profiling|Genetic Variation|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btp255', '19369497', 'We introduce a new visual analytics tool named MapView to facilitate the representation of large-scale short reads alignment data and genetic variation analysis. MapView can handle hundreds of millions of short reads on a desktop computer with limited memory. It supports a compact alignment view for both single-end and paired end short reads, multiple navigation and zoom modes and multi-thread processing. Moreover, MapView offers automated genetic variation detection. MapView has been used in our lab and by over 10 research labs worldwide.'),
(74750, 2009, 'bio', 'ViaComplex: software for landscape analysis of gene expression networks in genomic context', 'Castro, Mauro A A|Filho, José L Rybarczyk|Dalmolin, Rodrigo J S|Sinigaglia, Marialva|Moreira, José C F|Mombach, José C M|de Almeida, Rita M C', 'Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br|Unidade de Bioinformática, Departamento de Bioquímica, Instituto de Física, Núcleo de Bioinformática, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. mauro@ufrgs.br', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp246', '19369498', 'ViaComplex is an open-source application that builds landscape maps of gene expression networks. The motivation for this software comes from two previous publications (Nucleic Acids Res., 35, 1859-1867, 2007, Nucleic Acids Res., 36, 6269-6283, 2008). The first article presents a network-based model of genome stability pathways where we defined a set of genes that characterizes each genetic system. In the second article we analyzed this model by projecting functional information from several experiments onto the gene network topology. In order to systematize the methods developed in these articles, ViaComplex provides tools that may help potential users to assess different high-throughput experiments in the context of six core genome maintenance mechanisms. This model illustrates how different gene networks can be analyzed by the same algorithm.'),
(74751, 2009, 'bio', 'pySolo: a complete suite for sleep analysis in Drosophila', 'Gilestro, Giorgio F|Cirelli, Chiara', 'Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA. gilestro@wisc.edu|Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA. gilestro@wisc.edu', 'United States|United States', 77, 'Animals|Computational Biology|Drosophila melanogaster|Motor Activity|Sleep|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp237', '19369499', 'pySolo is a multiplatform software for analysis of sleep and locomotor activity in Drosophila melanogaster. pySolo provides a user-friendly graphic interface and it has been developed with the specific aim of being accessible, portable, fast and easily expandable through an intuitive plug-in structure. Support for development of additional plug-ins is provided through a community website.'),
(74752, 2009, 'bio', 'PseudoViewer3: generating planar drawings of large-scale RNA structures with pseudoknots', 'Byun, Yanga|Han, Kyungsook', 'Department of Computer Science and Engineering, Inha University, Incheon, South Korea.|Department of Computer Science and Engineering, Inha University, Incheon, South Korea.', '-|-', 0, 'Computational Biology|Computer Graphics|Models, Molecular|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp252', '19369500', 'Pseudoknots in RNA structures make visualization of RNA structures difficult. Even if a pseudoknot itself is represented without a crossing, visualization of the entire RNA structure with a pseudoknot often results in a drawing with crossings between the pseudoknot and other structural elements, and requires additional intervention by the user to ensure that the structure graph is overlap-free. Many programs such as web services prefer to obtain an overlap-free graph in one-shot rather than get a graph with overlaps to be edited. There are few programs for visualizing RNA pseudoknots, and PseudoViewer has been the almost only program that automatically draws RNA secondary structures with pseudoknots. The previous version of PseudoViewer visualizes all the known types of RNA pseudoknots as planar drawings, but visualizes some hypothetical pseudoknots as non-planar drawings.'),
(74753, 2009, 'bio', 'BEsTRF: a tool for optimal resolution of terminal-restriction fragment length polymorphism analysis based on user-defined primer-enzyme-sequence databases', 'Stres, Blaz|Tiedje, James M|Murovec, Bostjan', 'Department of Animal Science, Biotechnical Faculty, University of Ljubljana, DomZale, Slovenia. blaz.stres@bfro.uni-lj.si|Department of Animal Science, Biotechnical Faculty, University of Ljubljana, DomZale, Slovenia. blaz.stres@bfro.uni-lj.si|Department of Animal Science, Biotechnical Faculty, University of Ljubljana, DomZale, Slovenia. blaz.stres@bfro.uni-lj.si', 'Slovenia|Slovenia|Slovenia', 11, 'Base Sequence|DNA Primers|DNA Restriction Enzymes|Databases, Genetic|Polymorphism, Restriction Fragment Length|Software', 'Journal Article', '10.1093/bioinformatics/btp254', '19369501', 'BEsTRF (Best Estimated T-RF) provides a standalone environment for analyzing primers-enzymes-gene section combinations used in terminal-restriction fragment length polymorphism (T-RFLP) for its optimal resolution. User-defined sequence databases of several hundred thousand DNA sequences can be explored and the resolution of user-specified sets of primers and restriction endonucleases can be analyzed on either forward or reverse terminal fragments. Sequence quality, primer mismatches, insertions and deletions can be controlled and each primer pair-specific sequence collections can be exported for downstream analyses. The configuration for a novel T-RFLP population profiling using rpoB gene (DNA-directed RNA polymerase, beta subunit) on forward fluorescently labeled primer are presented.'),
(74754, 2009, 'bio', 'Fluctuation analysis CalculatOR: a web tool for the determination of mutation rate using Luria-Delbruck fluctuation analysis', 'Hall, Brandon M|Ma, Chang-Xing|Liang, Ping|Singh, Keshav K', 'Department of Cancer Genetics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA. brandon.hall@roswellpark.org|Department of Cancer Genetics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA. brandon.hall@roswellpark.org|Department of Cancer Genetics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA. brandon.hall@roswellpark.org|Department of Cancer Genetics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA. brandon.hall@roswellpark.org', 'United States|United States|United States|United States', 251, 'Computational Biology|Databases, Genetic|Internet|Likelihood Functions|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btp253', '19369502', 'The program Fluctuation AnaLysis CalculatOR (FALCOR) is a web tool designed for use with Luria-Delbrück fluctuation analysis to calculate the frequency and rate from various mutation assays in bacteria and yeast. Three calculation methods are available through this program: (i) Ma-Sandri-Sarkar Maximum Likelihood Estimator (MSS-MLE) method, (ii) Lea-Coulson method of the median (LC) and (iii) frequency.'),
(74755, 2009, 'bio', 'Metal-MACiE: a database of metals involved in biological catalysis', 'Andreini, Claudia|Bertini, Ivano|Cavallaro, Gabriele|Holliday, Gemma L|Thornton, Janet M', 'Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it|Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy. andreini@cerm.unifi.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Biocatalysis|Computational Biology|Databases, Factual|Databases, Protein|Metalloproteins|Metals|Software', 'Journal Article', '10.1093/bioinformatics/btp256', '19369503', 'Metal-MACiE is a new publicly available web-based database, held in MySQL, which aims to organize the available information on the properties and the roles of metals in the context of the catalytic mechanisms of metalloenzymes. Metal-MACiE, which currently covers 75% of metal-dependent enzyme commission (EC) sub-sub-classes and is continuously growing, exploits the existing MACiE database for the annotation of the reaction mechanisms. The two databases constitute complementary sources of information for enzymology, biochemistry and molecular pharmacology studies.'),
(74756, 2009, 'bio', 'MeSH Up: effective MeSH text classification for improved document retrieval', 'Trieschnigg, Dolf|Pezik, Piotr|Lee, Vivian|de Jong, Franciska|Kraaij, Wessel|Rebholz-Schuhmann, Dietrich', 'European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl|European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl|European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl|European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl|European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl|European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Medical Subject Headings|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp249', '19376821', 'Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeSH concepts have been proposed to replace manual annotation, but they are either limited to a small subset of MeSH or have only been compared with a limited number of other systems.'),
(74757, 2009, 'bio', 'CATMAID: collaborative annotation toolkit for massive amounts of image data', 'Saalfeld, Stephan|Cardona, Albert|Hartenstein, Volker|Tomancak, Pavel', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.', 'Germany|Germany|Germany|Germany', 136, 'Algorithms|Computational Biology|Computer Graphics|Database Management Systems|Databases, Factual|Imaging, Three-Dimensional|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btp266', '19376822', 'High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy.'),
(74758, 2009, 'bio', 'A local multiple alignment method for detection of non-coding RNA sequences', 'Tabei, Yasuo|Asai, Kiyoshi', 'Department of Computational biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan. scarna@m.aist.go.jp|Department of Computational biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan. scarna@m.aist.go.jp', 'Japan|Japan', 14, 'Algorithms|Base Sequence|Computational Biology|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp261', '19376823', 'Non-coding RNAs (ncRNAs) show a unique evolutionary process in which the substitutions of distant bases are correlated in order to conserve the secondary structure of the ncRNA molecule. Therefore, the multiple alignment method for the detection of ncRNAs should take into account both the primary sequence and the secondary structure. Recently, there has been intense focus on multiple alignment investigations for the detection of ncRNAs, however, most of the proposed methods are designed for global multiple alignments. For this reason, these methods are not appropriate to identify locally conserved ncRNAs among genomic sequences. A more efficient local multiple alignment method for the detection of ncRNAs is required.'),
(74759, 2009, 'bio', 'FDR made easy in differential feature discovery and correlation analyses', 'Ling, Xuefeng Bruce|Cohen, Harvey|Jin, Joseph|Lau, Irwin|Schilling, James', 'Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com|Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com|Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com|Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com|Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|False Positive Reactions|Gene Expression Profiling|Software', 'Journal Article', '10.1093/bioinformatics/btp176', '19376824', 'Rapid progress in technology, particularly in high-throughput biology, allows the analysis of thousands of genes or proteins simultaneously, where the multiple comparison problems occurs. Global false discovery rate (gFDR) analysis statistically controls this error, computing the ratio of the number of false positives over the total number of rejections. Local FDR (lFDR) method can associate the corrected significance measure with each hypothesis testing for its feature-by-feature interpretation. Given the large feature number and sample size in any genomics or proteomics analysis, FDR computation, albeit critical, is both beyond the regular biologists specialty and computationally expensive, easily exceeding the capacity of desktop computers. To overcome this digital divide, a web portal has been developed that provides bench-side biologists easy access to the server-side computing capabilities to analyze for FDR, differential expressed genes or proteins, and for the correlation between molecular data and clinical measurements.'),
(74760, 2009, 'bio', 'Application and evaluation of automated semantic annotation of gene expression experiments', 'French, Leon|Lane, Suzanne|Law, Tamryn|Xu, Lydia|Pavlidis, Paul', 'University of British Columbia, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada', 9, 'Computational Biology|Databases, Genetic|Gene Expression|Oligonucleotide Array Sequence Analysis|Semantics|Software|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/btp259', '19376825', 'Many microarray datasets are available online with formalized standards describing the probe sequences and expression values. Unfortunately, the description, conditions and parameters of the experiments are less commonly formalized and often occur as natural language text. This hinders searching, high-throughput analysis, organization and integration of the datasets.'),
(74761, 2009, 'bio', 'A stochastic model for the evolution of metabolic networks with neighbor dependence', 'Mithani, Aziz|Preston, Gail M|Hein, Jotun', 'Department of Statistics, University of Oxford, Oxford, UK. mithani@stats.ox.ac.uk|Department of Statistics, University of Oxford, Oxford, UK. mithani@stats.ox.ac.uk|Department of Statistics, University of Oxford, Oxford, UK. mithani@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Statistical|Pseudomonas', 'Journal Article', '10.1093/bioinformatics/btp262', '19376826', 'Most current research in network evolution focuses on networks that follow a Duplication Attachment model where the network is only allowed to grow. The evolution of metabolic networks, however, is characterized by gain as well as loss of reactions. It would be desirable to have a biologically relevant model of network evolution that could be used to calculate the likelihood of homologous metabolic networks.'),
(74762, 2009, 'bio', 'Domain Interaction Footprint: a multi-classification approach to predict domain-peptide interactions', 'Schillinger, Christian|Boisguerin, Prisca|Krause, Gerd', 'Leibniz Institute for Molecular Pharmacology, Robert-Roessle-Strasse 10, Berlin, FU-Berlin, Germany.|Leibniz Institute for Molecular Pharmacology, Robert-Roessle-Strasse 10, Berlin, FU-Berlin, Germany.|Leibniz Institute for Molecular Pharmacology, Robert-Roessle-Strasse 10, Berlin, FU-Berlin, Germany.', 'Germany|Germany|Germany', 12, 'Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Ligands|Peptides|Protein Interaction Domains and Motifs|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp264', '19376827', 'The flow of information within cellular pathways largely relies on specific protein-protein interactions. Discovering such interactions that are mostly mediated by peptide recognition modules (PRM) is therefore a fundamental step towards unravelling the complexity of varying pathways. Since peptides can be recognized by more than one PRM and high-throughput experiments are both time consuming and expensive, it would be preferable to narrow down all potential peptide ligands for one specific PRM by a computational method. We at first present Domain Interaction Footprint (DIF) a new approach to predict binding peptides to PRMs merely based on the sequence of the peptides. Second, we show that our method is able to create a multi-classification model that assesses the binding specificity of a given peptide to all examined PRMs at once.'),
(74763, 2009, 'bio', 'Literature-based priors for gene regulatory networks', 'Steele, E|Tucker, A|t Hoen, P A C|Schuemie, M J', 'Centre for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK. emma.steele@brunel.ac.uk|Centre for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK. emma.steele@brunel.ac.uk|Centre for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK. emma.steele@brunel.ac.uk|Centre for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK. emma.steele@brunel.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 29, 'Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Proteome', 'Journal Article', '10.1093/bioinformatics/btp277', '19389730', 'The use of prior knowledge to improve gene regulatory network modelling has often been proposed. In this article we present the first research on the massive incorporation of prior knowledge from literature for Bayesian network learning of gene networks. As the publication rate of scientific papers grows, updating online databases, which have been proposed as potential prior knowledge in past research, becomes increasingly challenging. The novelty of our approach lies in the use of gene-pair association scores that describe the overlap in the contexts in which the genes are mentioned, generated from a large database of scientific literature, harnessing the information contained in a huge number of documents into a simple, clear format.'),
(74764, 2009, 'bio', 'Augmented training of hidden Markov models to recognize remote homologs via simulated evolution', 'Kumar, Anoop|Cowen, Lenore', 'Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu|Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu', 'United States|United States', 28, 'Computational Biology|Entropy|Markov Chains|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btp265', '19389731', 'While profile hidden Markov models (HMMs) are successful and powerful methods to recognize homologous proteins, they can break down when homology becomes too distant due to lack of sufficient training data. We show that we can improve the performance of HMMs in this domain by using a simple simulated model of evolution to create an augmented training set.'),
(74765, 2009, 'bio', 'KIRMES: kernel-based identification of regulatory modules in euchromatic sequences', 'Schultheiss, Sebastian J|Busch, Wolfgang|Lohmann, Jan U|Kohlbacher, Oliver|Rätsch, Gunnar', 'Friedrich Miescher Laboratory of the Max Planck Society, and Max Planck Institute for Developmental Biology, Tübingen, Germany. sebi@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society, and Max Planck Institute for Developmental Biology, Tübingen, Germany. sebi@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society, and Max Planck Institute for Developmental Biology, Tübingen, Germany. sebi@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society, and Max Planck Institute for Developmental Biology, Tübingen, Germany. sebi@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society, and Max Planck Institute for Developmental Biology, Tübingen, Germany. sebi@tuebingen.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Arabidopsis|Base Sequence|Binding Sites|Computational Biology|Euchromatin|Promoter Regions, Genetic|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp278', '19389732', 'Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules.'),
(74766, 2009, 'bio', 'Protein function annotation from sequence: prediction of residues interacting with RNA', 'Spriggs, R V|Murakami, Y|Nakamura, H|Jones, S', 'Department of Chemistry and Biochemistry, School of Life Sciences, John Maynard-Smith Building, University of Sussex, Falmer, Brighton, UK.|Department of Chemistry and Biochemistry, School of Life Sciences, John Maynard-Smith Building, University of Sussex, Falmer, Brighton, UK.|Department of Chemistry and Biochemistry, School of Life Sciences, John Maynard-Smith Building, University of Sussex, Falmer, Brighton, UK.|Department of Chemistry and Biochemistry, School of Life Sciences, John Maynard-Smith Building, University of Sussex, Falmer, Brighton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 42, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|RNA|RNA-Binding Proteins', 'Journal Article', '10.1093/bioinformatics/btp257', '19389733', 'All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein.'),
(74767, 2009, 'bio', 'MetaTISA: Metagenomic Translation Initiation Site Annotator for improving gene start prediction', 'Hu, Gang-Qing|Guo, Jiang-Tao|Liu, Yong-Chu|Zhu, Huaiqiu', 'State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering and Center for Theoretical Biology, Peking University, Beijing 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering and Center for Theoretical Biology, Peking University, Beijing 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering and Center for Theoretical Biology, Peking University, Beijing 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering and Center for Theoretical Biology, Peking University, Beijing 100871, China.', 'China|China|China|China', 0, 'Genes|Genomics|Peptide Chain Initiation, Translational|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp272', '19389734', 'We proposed a tool named MetaTISA with an aim to improve TIS prediction of current gene-finders for metagenomes. The method employs a two-step strategy to predict translation initiation sites (TISs) by first clustering metagenomic fragments into phylogenetic groups and then predicting TISs independently for each group in an unsupervised manner. As evaluated on experimentally verified TISs, MetaTISA greatly improves the accuracies of TIS prediction of current gene-finders.'),
(74768, 2009, 'bio', 'A Bayesian segmentation approach to ascertain copy number variations at the population level', 'Wu, Long Yang|Chipman, Hugh A|Bull, Shelley B|Briollais, Laurent|Wang, Kesheng', 'Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. lwu@math.uwaterloo.ca|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. lwu@math.uwaterloo.ca|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. lwu@math.uwaterloo.ca|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. lwu@math.uwaterloo.ca|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. lwu@math.uwaterloo.ca', 'Canada|Canada|Canada|Canada|Canada', 16, 'Bayes Theorem|Computational Biology|Gene Dosage|Gene Expression Profiling|Genetic Variation|Genetics, Population|Humans', 'Journal Article', '10.1093/bioinformatics/btp270', '19389735', 'Efficient and accurate ascertainment of copy number variations (CNVs) at the population level is essential to understand the evolutionary process and population genetics, and to apply CNVs in population-based genome-wide association studies for complex human diseases. We propose a novel Bayesian segmentation approach to identify CNVs in a defined population of any size. It is computationally efficient and provides statistical evidence for the detected CNVs through the Bayes factor. This approach has the unique feature of carrying out segmentation and assigning copy number status simultaneously-a desirable property that current segmentation methods do not share.'),
(74769, 2009, 'bio', 'A practical algorithm for finding maximal exact matches in large sequence datasets using sparse suffix arrays', 'Khan, Zia|Bloom, Joshua S|Kruglyak, Leonid|Singh, Mona', 'Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. zkhan@cs.princeton.edu|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. zkhan@cs.princeton.edu|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. zkhan@cs.princeton.edu|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. zkhan@cs.princeton.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Genomics|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp275', '19389736', 'High-throughput sequencing technologies place ever increasing demands on existing algorithms for sequence analysis. Algorithms for computing maximal exact matches (MEMs) between sequences appear in two contexts where high-throughput sequencing will vastly increase the volume of sequence data: (i) seeding alignments of high-throughput reads for genome assembly and (ii) designating anchor points for genome-genome comparisons.'),
(74770, 2009, 'bio', 'CROC: finding chromosomal clusters in eukaryotic genomes', 'Pignatelli, Miguel|Serras, Florenci|Moya, Andrés|Guigó, Roderic|Corominas, Montserrat', 'Instituto Cavanilles of Biodiversity and Evolutionary Biology, University of Valencia, Apdo, Valencia, Spain. miguel.pignatelli@uv.es|Instituto Cavanilles of Biodiversity and Evolutionary Biology, University of Valencia, Apdo, Valencia, Spain. miguel.pignatelli@uv.es|Instituto Cavanilles of Biodiversity and Evolutionary Biology, University of Valencia, Apdo, Valencia, Spain. miguel.pignatelli@uv.es|Instituto Cavanilles of Biodiversity and Evolutionary Biology, University of Valencia, Apdo, Valencia, Spain. miguel.pignatelli@uv.es|Instituto Cavanilles of Biodiversity and Evolutionary Biology, University of Valencia, Apdo, Valencia, Spain. miguel.pignatelli@uv.es', 'Spain|Spain|Spain|Spain|Spain', 13, 'Algorithms|Chromosomes|Genome|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp248', '19389737', 'There is increasing evidence showing that co-expression of genes that cluster along the genome is a common characteristic of eukaryotic transcriptomes. Several algorithms have been used to date in the identification of these kinds of gene organization. Here, we present a web tool called CROC that aims to help in the identification and analysis of genomic gene clusters. This method has been successfully used before in the identification of chromosomal clusters in different eukaryotic species.'),
(74771, 2009, 'bio', 'Systematic analysis of synchronized oscillatory neuronal networks reveals an enrichment for coupled direct and indirect feedback motifs', 'Dong, Chao-Yi|Lim, Jisoon|Nam, Yoonkey|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, Republic of Korea.', '-|-|-|-', 0, 'Algorithms|Computer Simulation|Neural Networks (Computer)|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp271', '19389738', 'Synchronized bursting behavior is a remarkable phenomenon in neural dynamics. So, identification of the underlying functional structure is crucial to understand its regulatory mechanism at a system level. On the other hand, we noted that feedback loops (FBLs) are commonly used basic building blocks in engineering circuit design, especially for synchronization, and they have also been considered as important regulatory network motifs in systems biology. From these motivations, we have investigated the relationship between synchronized bursting behavior and feedback motifs in neural networks.'),
(74772, 2009, 'bio', 'GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome', 'Folkersen, Lasse|Diez, Diego|Wheelock, Craig E|Haeggström, Jesper Z|Goto, Susumu|Eriksson, Per|Gabrielsen, Anders', 'Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se|Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden. lasse.folkersen@ki.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 7, 'Computer Simulation|Databases, Genetic|Genome|Genotype|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp279', '19398447', 'Whole-genome microarrays allow us to interrogate the entire transcriptome of a cell. Affymetrix microarrays are constructed using several probes that match to different regions of a gene and a summarization step reduces this complexity into a single value, representing the expression level of the gene or the expression level of an exon in the case of exon arrays. However, this simplification eliminates information that might be useful when focusing on specific genes of interest. To address these limitations, we present a software package for the R platform that allows detailed analysis of expression at the probe level. The package matches the probe sequences against a target gene sequence (either mRNA or DNA) and shows the expression levels of each probe along the gene. It also features functions to fit a linear regression based on several genetic models that enables study of the relationship between gene expression and genotype.'),
(74773, 2009, 'bio', 'VARNA: Interactive drawing and editing of the RNA secondary structure', 'Darty, Kévin|Denise, Alain|Ponty, Yann', 'LRI, UMR CNRS 8623, UMR CNRS 8621, Université Paris-Sud 11, F91405 Orsay cedex, France. ponty@lri.fr|LRI, UMR CNRS 8623, UMR CNRS 8621, Université Paris-Sud 11, F91405 Orsay cedex, France. ponty@lri.fr|LRI, UMR CNRS 8623, UMR CNRS 8621, Université Paris-Sud 11, F91405 Orsay cedex, France. ponty@lri.fr', 'France|France|France', 0, 'Computational Biology|Computer Graphics|Databases, Factual|Information Storage and Retrieval|Nucleic Acid Conformation|RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp250', '19398448', 'VARNA is a tool for the automated drawing, visualization and annotation of the secondary structure of RNA, designed as a companion software for web servers and databases.'),
(74774, 2009, 'bio', 'Optimizing static thermodynamic models of transcriptional regulation', 'Bauer, Denis C|Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia. d.bauer@uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia. d.bauer@uq.edu.au', 'Australia|Australia', 7, 'Algorithms|Computational Biology|Gene Expression Regulation|Thermodynamics|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btp283', '19398449', 'Modeling transcriptional regulation using thermo-dynamic modeling approaches has become increasingly relevant as a way to gain a detailed understanding of transcriptional regulation. Thermodynamic models are able to model the interactions between transcription factors (TFs) and DNA that lead to a specific transcriptional output of the target gene. Such models can be trained by fitting their free parameters to data on the transcription rate of a gene and the concentrations of its regulating factors. However, the parameter fitting process is computationally very expensive and this limits the number of alternative types of model that can be explored.'),
(74775, 2009, 'bio', 'Rahnuma: hypergraph-based tool for metabolic pathway prediction and network comparison', 'Mithani, Aziz|Preston, Gail M|Hein, Jotun', 'Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Metabolic Networks and Pathways|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/btp269', '19398450', 'We present a tool called Rahnuma for prediction and analysis of metabolic pathways and comparison of metabolic networks. Rahnuma represents metabolic networks as hypergraphs and computes all possible pathways between two or more metabolites. It provides an intuitive way to answer biological ques- tions focusing on differences between organisms or the evolution of different species by allowing pathway-based metabolic network comparisons at an organism as well as at a phylogenetic level.'),
(74776, 2009, 'bio', 'penalizedSVM: a R-package for feature selection SVM classification', 'Becker, Natalia|Werft, Wiebke|Toedt, Grischa|Lichter, Peter|Benner, Axel', 'Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de|Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de|Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de|Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de|Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Classification|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btp286', '19398451', 'Support vector machine (SVMs) classification is a widely used and one of the most powerful classification techniques. However, a major limitation is that SVM cannot perform automatic gene selection. To overcome this restriction, a number of penalized feature selection methods have been proposed. In the R package penalizedSVM implemented penalization functions L(1) norm and Smoothly Clipped Absolute Deviation (SCAD) provide automatic feature selection for SVM classification tasks.'),
(74777, 2009, 'bio', 'Affinity Density: a novel genomic approach to the identification of transcription factor regulatory targets', 'Hazelett, Dennis J|Lakeland, Daniel L|Weiss, Joseph B', 'Integrative Biosciences, Oregon Health and Science University, 611 SW Campus Drive, Portland, OR 97239, USA. hazelett@ohsu.edu|Integrative Biosciences, Oregon Health and Science University, 611 SW Campus Drive, Portland, OR 97239, USA. hazelett@ohsu.edu|Integrative Biosciences, Oregon Health and Science University, 611 SW Campus Drive, Portland, OR 97239, USA. hazelett@ohsu.edu', 'United States|United States|United States', 3, 'Algorithms|Animals|Binding Sites|Drosophila melanogaster|Genome|Genomics|Mice|Regulatory Elements, Transcriptional|Sequence Analysis, Protein|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp282', '19401399', 'A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation.'),
(74778, 2009, 'bio', 'List-decoding methods for inferring polynomials in finite dynamical gene network models', 'Dingel, Janis|Milenkovic, Olgica', 'Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de', 'Germany|Germany', 0, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Models, Genetic|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp281', '19401400', 'The problem of reverse engineering the dynamics of gene expression profiles is of focal importance in systems biology. Due to noise and the inherent lack of sufficiently large datasets generated via high-throughput measurements, known reconstruction frameworks based on dynamical systems models fail to provide adequate settings for network analysis. This motivates the study of new approaches that produce stochastic lists of explanations for the observed network dynamics that can be efficiently inferred from small sample sets and in the presence of errors.'),
(74779, 2009, 'bio', 'baobabLUNA: the solution space of sorting by reversals', 'Braga, Marília D V', 'Université de Lyon, F-69000, France. mdvbraga@gmail.com', 'France', 0, 'Algorithms|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btp285', '19401401', 'Computing the reversal distance and searching for an optimal sequence of reversals to transform a unichromosomal genome into another are useful algorithmic tools to analyse real evolutionary scenarios. Currently, these problems can be solved by at least two available softwares, the prominent of which are GRAPPA and GRIMM. However, the number of different optimal sequences is usually huge and taking only the distance and/or one example is often insufficient to do a proper analysis. Here, we offer an alternative and present baobabLUNA, a framework that contains an algorithm to give a compact representation of the whole space of solutions for the sorting by reversals problem.'),
(74780, 2009, 'bio', 'Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data', 'Seifert, Michael|Keilwagen, Jens|Strickert, Marc|Grosse, Ivo', 'Leibniz Institute of Plant Genetics and Crop Plant Research, Data Inspection Group, Corrensstrasse 3, 06466 Gatersleben, Germany. seifert@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research, Data Inspection Group, Corrensstrasse 3, 06466 Gatersleben, Germany. seifert@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research, Data Inspection Group, Corrensstrasse 3, 06466 Gatersleben, Germany. seifert@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research, Data Inspection Group, Corrensstrasse 3, 06466 Gatersleben, Germany. seifert@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany', 7, 'Arabidopsis|Chromatin Immunoprecipitation|Computational Biology|Databases, Genetic|Gene Expression Profiling|Markov Chains|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp276', '19401402', 'Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC), (ii) a basic method based on a Hidden Markov Model (HMM), and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA.'),
(74781, 2009, 'bio', 'apLCMS--adaptive processing of high-resolution LC/MS data', 'Yu, Tianwei|Park, Youngja|Johnson, Jennifer M|Jones, Dean P', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Chromatography, Liquid|Computational Biology|Databases, Protein|Mass Spectrometry|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp291', '19414529', 'Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74782, 2009, 'bio', 'A tool for identification of genes expressed in patterns of interest using the Allen Brain Atlas', 'Davis, Fred P|Eddy, Sean R', 'HHMI Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA. davisf@janelia.hhmi.org|HHMI Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA. davisf@janelia.hhmi.org', 'United States|United States', 0, 'Animals|Brain|Computer Graphics|Gene Expression|Gene Expression Profiling|Genome|Mice|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp288', '19414530', 'Gene expression patterns can be useful in understanding the structural organization of the brain and the regulatory logic that governs its myriad cell types. A particularly rich source of spatial expression data is the Allen Brain Atlas (ABA), a comprehensive genome-wide in situ hybridization study of the adult mouse brain. Here, we present an open-source program, ALLENMINER, that searches the ABA for genes that are expressed, enriched, patterned or graded in a user-specified region of interest.'),
(74783, 2009, 'bio', 'PESTAS: a web server for EST analysis and sequence mining', 'Nam, Seong-Hyeuk|Kim, Dae-Won|Jung, Tae-Sung|Choi, Young-Sang|Kim, Dong-Wook|Choi, Han-Suk|Choi, Sang-Haeng|Park, Hong-Seog', 'Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.|Industrial Biotechnology & Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.', '-|-|-|-|-|-|-|-', 10, 'Computational Biology|Database Management Systems|Databases, Genetic|Expressed Sequence Tags|Internet|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp293', '19414531', 'We have developed a web server for the high-throughput annotation of expressed sequence tags (ESTs) called pipeline for EST analysis service (PESTAS). PESTAS processes entire datasets with an automated pipeline of 13 analytic services, then deposits the data into the MySQL database and transforms it into three kinds of reports: preprocessing, assembling and annotation. All annotated information is provided to the scientist and can be downloaded through a web browser. To get more relevant functional annotation results, a curation function was introduced with which biologists can easily change the best-hit annotation information. We included a gene chip module that detects gene expression differences between libraries by comparing accession number counts from BLAST search results. PESTAS also provides access to the pathway information of KEGG, which is useful for mapping the relationships among networks of annotated enzymes, and is especially valuable for those researchers interested in biological pathways.'),
(74784, 2009, 'bio', 'Pairagon: a highly accurate, HMM-based cDNA-to-genome aligner', 'Lu, David V|Brown, Randall H|Arumugam, Manimozhiyan|Brent, Michael R', 'Department of Computer Science and Center for Genome Sciences, Washington University, St. Louis, MO 63130, USA. davidlu@wustl.edu|Department of Computer Science and Center for Genome Sciences, Washington University, St. Louis, MO 63130, USA. davidlu@wustl.edu|Department of Computer Science and Center for Genome Sciences, Washington University, St. Louis, MO 63130, USA. davidlu@wustl.edu|Department of Computer Science and Center for Genome Sciences, Washington University, St. Louis, MO 63130, USA. davidlu@wustl.edu', 'United States|United States|United States|United States', 6, 'Animals|Base Sequence|DNA, Complementary|Genomics|Humans|Markov Chains|Mice|Rats|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btp273', '19414532', 'The most accurate way to determine the intron-exon structures in a genome is to align spliced cDNA sequences to the genome. Thus, cDNA-to-genome alignment programs are a key component of most annotation pipelines. The scoring system used to choose the best alignment is a primary determinant of alignment accuracy, while heuristics that prevent consideration of certain alignments are a primary determinant of runtime and memory usage. Both accuracy and speed are important considerations in choosing an alignment algorithm, but scoring systems have received much less attention than heuristics.'),
(74785, 2009, 'bio', 'MetNetAligner: a web service tool for metabolic network alignments', 'Cheng, Qiong|Harrison, Robert|Zelikovsky, Alexander', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.', 'Georgia|Georgia|Georgia', 16, 'Computational Biology|Databases, Factual|Internet|Metabolic Networks and Pathways|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btp287', '19414533', 'The accumulation of high-throughput genomic, proteomic and metabolical data allows for increasingly accurate modeling and reconstruction of metabolic networks. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. In this note, we present the web service tool MetNetAligner which aligns metabolic networks, taking in account the similarity of network topology and the enzymes functions. It can be used for predicting unknown pathways, comparing and finding conserved patterns and resolving ambiguous identification of enzymes. The tool supports several alignment options including allowing or forbidding enzyme deletion and insertion. It is based on a novel scoring scheme which measures enzyme-to-enzyme functional similarity and a fast algorithm which efficiently finds optimal mappings from a directed graph with restricted cyclic structure to an arbitrary directed graph.'),
(74786, 2009, 'bio', 'Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction', 'Cheng, Chunrong|Shen, Kui|Song, Chi|Luo, Jianhua|Tseng, George C', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States', 15, 'Calibration|Computational Biology|Gene Expression Profiling|Genes|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp292', '19414534', 'Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored.'),
(74787, 2009, 'bio', 'U-Compare: share and compare text mining tools with UIMA', 'Kano, Yoshinobu|Baumgartner, William A|McCrohon, Luke|Ananiadou, Sophia|Cohen, K Bretonnel|Hunter, Lawrence|Tsujii, Junichi', 'Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp|Department of Computer Science, University of Tokyo Hongo 7-3-1, Tokyo 113-0033, Japan. kano@is.s.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 76, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp289', '19414535', 'Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the worlds largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources.'),
(74788, 2009, 'bio', 'Flexible structural protein alignment by a sequence of local transformations', 'Rocha, Jairo|Segura, Joan|Wilson, Richard C|Dasgupta, Swagata', 'Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Spain. jairo@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Spain. jairo@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Spain. jairo@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Spain. jairo@uib.es', 'Spain|Spain|Spain|Spain', 21, 'Algorithms|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp296', '19417057', 'Throughout evolution, homologous proteins have common regions that stay semi-rigid relative to each other and other parts that vary in a more noticeable way. In order to compare the increasing number of structures in the PDB, flexible geometrical alignments are needed, that are reliable and easy to use.'),
(74789, 2009, 'bio', 'Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes', 'Zhang, Min|Zhang, Lin|Zou, Jinfeng|Yao, Chen|Xiao, Hui|Liu, Qing|Wang, Jing|Wang, Dong|Wang, Chenguang|Guo, Zheng', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China', 82, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Evaluation Studies', '10.1093/bioinformatics/btp295', '19417058', 'According to current consistency metrics such as percentage of overlapping genes (POG), lists of differentially expressed genes (DEGs) detected from different microarray studies for a complex disease are often highly inconsistent. This irreproducibility problem also exists in other high-throughput post-genomic areas such as proteomics and metabolism. A complex disease is often characterized with many coordinated molecular changes, which should be considered when evaluating the reproducibility of discovery lists from different studies.'),
(74790, 2009, 'bio', 'Ulla: a program for calculating environment-specific amino acid substitution tables', 'Lee, Semin|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, Cambridge CB2 1GA, UK. semin@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, Cambridge CB2 1GA, UK. semin@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom', 12, 'Amino Acid Substitution|Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp300', '19417059', 'Amino acid residues are under various kinds of local environmental restraints, which influence substitution patterns. Ulla,(1) a program for calculating environment-specific substitution tables, reads protein sequence alignments and local environment annotations. The program produces a substitution table for every possible combination of environment features. Sparse data is handled using an entropy-based smoothing procedure to estimate robust substitution probabilities.'),
(74791, 2009, 'bio', 'RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions', 'Rueda, Oscar M|Diaz-Uriarte, Ramon', 'Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Madrid 28029, Spain. rueda.om@gmail.com|Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Madrid 28029, Spain. rueda.om@gmail.com', 'Spain|Spain', 10, 'Bayes Theorem|Comparative Genomic Hybridization|Gene Dosage|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp307', '19420051', 'Several methods have been proposed to detect copy number changes and recurrent regions of copy number variation from aCGH, but few methods return probabilities of alteration explicitly, which are the direct answer to the question is this probe/region altered? RJaCGH fits a Non-Homogeneous Hidden Markov model to the aCGH data using Markov Chain Monte Carlo with Reversible Jump, and returns the probability that each probe is gained or lost. Using these probabilites, recurrent regions (over sets of individuals) of copy number alteration can be found.'),
(74792, 2009, 'bio', 'Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency', 'Klingelhoefer, Jochen W|Moutsianas, Loukas|Holmes, Chris', 'Department of Biochemistry, University of Oxford, Oxford, UK.|Department of Biochemistry, University of Oxford, Oxford, UK.|Department of Biochemistry, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom', 24, 'Algorithms|Bayes Theorem|Models, Genetic|RNA Interference|RNA, Small Interfering|RNA-Induced Silencing Complex|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp284', '19420052', 'Short interfering RNA (siRNA)-induced RNA interference is an endogenous pathway in sequence-specific gene silencing. The potency of different siRNAs to inhibit a common target varies greatly and features affecting inhibition are of high current interest. The limited success in predicting siRNA potency being reported so far could originate in the small number and the heterogeneity of available datasets in addition to the knowledge-driven, empirical basis on which features thought to be affecting siRNA potency are often chosen. We attempt to overcome these problems by first constructing a meta-dataset of 6483 publicly available siRNAs (targeting mammalian mRNA), the largest to date, and then applying a Bayesian analysis which accommodates feature set uncertainty. A stochastic logistic regression-based algorithm is designed to explore a vast model space of 497 compositional, structural and thermodynamic features, identifying associations with siRNA potency.'),
(74793, 2009, 'bio', 'A toolbox for validation of mass spectrometry peptides identification and generation of database: IRMa', 'Dupierris, Véronique|Masselon, Christophe|Court, Magali|Kieffer-Jaquinod, Sylvie|Bruley, Christophe', 'Fondation Rhône-Alpes Futur, 69003 Lyon, France.|Fondation Rhône-Alpes Futur, 69003 Lyon, France.|Fondation Rhône-Alpes Futur, 69003 Lyon, France.|Fondation Rhône-Alpes Futur, 69003 Lyon, France.|Fondation Rhône-Alpes Futur, 69003 Lyon, France.', 'France|France|France|France|France', 100, 'Computational Biology|Databases, Factual|Mass Spectrometry|Peptides|Proteomics|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp301', '19420053', 'The IRMa toolbox provides an interactive application to assist in the validation of Mascot search results. It allows automatic filtering of Mascot identification results as well as manual confirmation or rejection of individual PSM (a match between a fragmentation mass spectrum and a peptide). Dynamic grouping and coherence of information are maintained by the software in real time. Validated results can be exported under various forms, including an identification database (MSIdb). This allows biologists to compile search results from a whole study in a unique repository in order to provide a summarized view of their project. IRMa also features a fully automated version that can be used in a high-throughput pipeline. Given filter parameters, it can delete hits with no significant PSM, regroup hits identified by the same peptide(s) and export the result to the specified format without user intervention.'),
(74794, 2009, 'bio', 'CycSim--an online tool for exploring and experimenting with genome-scale metabolic models', 'Le Fèvre, F|Smidtas, S|Combe, C|Durot, M|dAlché-Buc, Florence|Schachter, V', 'CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.|CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.|CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.|CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.|CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.|CEA, DSV, IG, Genoscope, UMR 8030, Evry F-91057, France.', 'France|France|France|France|France|France', 18, 'Computational Biology|Databases, Genetic|Genome|Genomics|Internet|Metabolism|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp268', '19420054', 'CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models.'),
(74795, 2009, 'bio', 'TANDEM: integrating automated allele binning into genetics and genomics workflows', 'Matschiner, Michael|Salzburger, Walter', 'Zoological Institute, University of Basel, 4051 Basel, Switzerland. michael.matschiner@unibas.ch|Zoological Institute, University of Basel, 4051 Basel, Switzerland. michael.matschiner@unibas.ch', 'Switzerland|Switzerland', 177, 'Alleles|Computational Biology|Databases, Genetic|Genomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp303', '19420055', 'Computer programs for the statistical analysis of microsatellite data use allele length variation to infer, e.g. population genetic parameters, to detect quantitative trait loci or selective sweeps. However, observed allele lengths are usually inaccurate and may deviate from the expected periodicity of repeats. The common practice of rounding to the nearest whole number frequently results in miscalls and underestimations of allelic richness. Manual sorting of allele lengths into discrete classes, a process called binning, is tedious and error-prone. Here, we present a new program for the automated binning of microsatellite allele lengths to overcome these problems and to facilitate high-throughput allele binning.'),
(74796, 2009, 'bio', 'Estimating the posterior probability that genome-wide association findings are true or false', 'Bukszár, József|McClay, Joseph L|van den Oord, Edwin J C G', 'Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, 1112 East Clay Street, PO Box 980533, Richmond, Virginia 23298, USA. jbukszar@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, 1112 East Clay Street, PO Box 980533, Richmond, Virginia 23298, USA. jbukszar@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, 1112 East Clay Street, PO Box 980533, Richmond, Virginia 23298, USA. jbukszar@vcu.edu', 'United States|United States|United States', 9, 'Arabidopsis|Computational Biology|Gene Expression Profiling|Genome-Wide Association Study|Probability', 'Journal Article', '10.1093/bioinformatics/btp305', '19420056', 'A limitation of current methods used to declare significance in genome-wide association studies (GWAS) is that they do not provide clear information about the probability that GWAS findings are true of false. This lack of information increases the chance of false discoveries and may result in real effects being missed.'),
(74797, 2009, 'bio', 'DASMI: exchanging, annotating and assessing molecular interaction data', 'Blankenburg, Hagen|Finn, Robert D|Prlixc4x87, Andreas|Jenkinson, Andrew M|Ramxc3xadrez, Fidel|Emig, Dorothea|Schelhorn, Sven-Eric|Bxc3xbcch, Joachim|Lengauer, Thomas|Albrecht, Mario', 'Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.|Max Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrxc3xbccken, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 14, 'Computational Biology|Database Management Systems|Databases, Genetic|Internet|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp142', '19420069', 'Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet.'),
(74798, 2009, 'bio', 'pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination', 'Lobley, Anna|Sadowski, Michael I|Jones, David T', 'Department of Computer Science, University College London, UK.|Department of Computer Science, University College London, UK.|Department of Computer Science, University College London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 187, 'Computational Biology|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp302', '19429599', 'Generation of structural models and recognition of homologous relationships for unannotated protein sequences are fundamental problems in bioinformatics. Improving the sensitivity and selectivity of methods designed for these two tasks therefore has downstream benefits for many other bioinformatics applications.'),
(74799, 2009, 'bio', 'FlexServ: an integrated tool for the analysis of protein flexibility', 'Camps, Jordi|Carrillo, Oliver|Emperador, Agustí|Orellana, Laura|Hospital, Adam|Rueda, Manuel|Cicin-Sain, Damjan|DAbramo, Marco|Gelpí, Josep Lluís|Orozco, Modesto', 'Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Barcelona Science Park, Josep Samitier 1-5, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 62, 'Computational Biology|Databases, Protein|Internet|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp304', '19429600', 'FlexServ is a web-based tool for the analysis of protein flexibility. The server incorporates powerful protocols for the coarse-grained determination of protein dynamics using different versions of Normal Mode Analysis (NMA), Brownian dynamics (BD) and Discrete Dynamics (DMD). It can also analyze user provided trajectories. The server allows a complete analysis of flexibility using a large variety of metrics, including basic geometrical analysis, B-factors, essential dynamics, stiffness analysis, collectivity measures, Lindemanns indexes, residue correlation, chain-correlations, dynamic domain determination, hinge point detections, etc. Data is presented through a web interface as plain text, 2D and 3D graphics.'),
(74800, 2009, 'bio', 'MaHCO: an ontology of the major histocompatibility complex for immunoinformatic applications and text mining', 'DeLuca, David S|Beisswanger, Elena|Wermter, Joachim|Horn, Peter A|Hahn, Udo|Blasczyk, Rainer', 'Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany. david_deluca@dfci.harvard.edu', 'Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Alleles|Computational Biology|Database Management Systems|Databases, Protein|Humans|Information Storage and Retrieval|Major Histocompatibility Complex', 'Journal Article', '10.1093/bioinformatics/btp306', '19429601', 'The high level of polymorphism associated with the major histocompatibility complex (MHC) poses a challenge to organizing associated bioinformatic data, particularly in the area of hematopoietic stem cell transplantation. Thus, this area of research has great potential to profit from the ongoing development of biomedical ontologies, which offer structure and definition to MHC-data related communication and portability issues.'),
(74801, 2009, 'bio', 'Robust synthetic biology design: stochastic game theory approach', 'Chen, Bor-Sen|Chang, Chia-Hung|Lee, Hsiao-Ching', 'Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, ROC. bschen@ee.nthu.edu.tw|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, ROC. bschen@ee.nthu.edu.tw|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, ROC. bschen@ee.nthu.edu.tw', '-|-|-', 30, 'Algorithms|Game Theory|Gene Regulatory Networks|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btp310', '19435742', 'Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology.'),
(74802, 2009, 'bio', 'ESG: extended similarity group method for automated protein function prediction', 'Chitale, Meghana|Hawkins, Troy|Park, Changsoon|Kihara, Daisuke', 'Department of Computer Science, Purdue University, IN 47907, USA.|Department of Computer Science, Purdue University, IN 47907, USA.|Department of Computer Science, Purdue University, IN 47907, USA.|Department of Computer Science, Purdue University, IN 47907, USA.', 'United States|United States|United States|United States', 65, 'Computational Biology|Databases, Protein|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp309', '19435743', 'Importance of accurate automatic protein function prediction is ever increasing in the face of a large number of newly sequenced genomes and proteomics data that are awaiting biological interpretation. Conventional methods have focused on high sequence similarity-based annotation transfer which relies on the concept of homology. However, many cases have been reported that simple transfer of function from top hits of a homology search causes erroneous annotation. New methods are required to handle the sequence similarity in a more robust way to combine together signals from strongly and weakly similar proteins for effectively predicting function for unknown proteins with high reliability.'),
(74803, 2009, 'bio', 'Generating linkage mapping files from Affymetrix SNP chip data', 'Bahlo, M|Bromhead, C J', 'Bioinformatics Division, The Walter and Eliza Hall Institute, Parkville, 3052 VIC, Australia. bahlo@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute, Parkville, 3052 VIC, Australia. bahlo@wehi.edu.au', 'Australia|Australia', 49, 'Chromosome Mapping|Genome-Wide Association Study|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp313', '19435744', 'LINKDATAGEN is a perl tool that generates linkage mapping input files for five different linkage mapping tools using data from all 11 HAPMAP Phase III populations. It provides rudimentary error checks and is easily amended for personal linkage mapping preferences.'),
(74804, 2009, 'bio', 'Cloud computing', 'Bateman, Alex|Wood, Matt', 'Bioinformatics Division, The Walter and Eliza Hall Institute, Parkville, 3052 VIC, Australia. bahlo@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute, Parkville, 3052 VIC, Australia. bahlo@wehi.edu.au', 'Australia|Australia', 49, 'Genome|Genomics|Software', 'Editorial', '10.1093/bioinformatics/btp274', '19435745', '-'),
(74805, 2009, 'bio', 'DIANA-mirPath: Integrating human and mouse microRNAs in pathways', 'Papadopoulos, G L|Alexiou, P|Maragkakis, M|Reczko, M|Hatzigeorgiou, A G', 'Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16602 Varkiza, Greece. papadopoulos@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16602 Varkiza, Greece. papadopoulos@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16602 Varkiza, Greece. papadopoulos@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16602 Varkiza, Greece. papadopoulos@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16602 Varkiza, Greece. papadopoulos@fleming.gr', 'Greece|Greece|Greece|Greece|Greece', 237, 'Animals|Computational Biology|Humans|Internet|Mice|MicroRNAs|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp299', '19435746', 'DIANA-mirPath is a web-based computational tool developed to identify molecular pathways potentially altered by the expression of single or multiple microRNAs. The software performs an enrichment analysis of multiple microRNA target genes comparing each set of microRNA targets to all known KEGG pathways. The combinatorial effect of co-expressed microRNAs in the modulation of a given pathway is taken into account by the simultaneous analysis of multiple microRNAs. The graphical output of the program provides an overview of the parts of the pathway modulated by microRNAs, facilitating the interpretation and presentation of the analysis results.'),
(74806, 2009, 'bio', 'Complex discovery from weighted PPI networks', 'Liu, Guimei|Wong, Limsoon|Chua, Hon Nian', 'School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore. liugm@comp.nus.edu.sg|School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore. liugm@comp.nus.edu.sg|School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore. liugm@comp.nus.edu.sg', 'Singapore|Singapore|Singapore', 317, 'Algorithms|Binding Sites|Cluster Analysis|Computational Biology|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp311', '19435747', 'Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, protein interaction data produced by high-throughput experiments are often associated with high false positive and false negative rates, which makes it difficult to predict complexes accurately.'),
(74807, 2009, 'bio', 'Apollo: a community resource for genome annotation editing', 'Lee, Ed|Harris, Nomi|Gibson, Mark|Chetty, Raymond|Lewis, Suzanna', 'Berkeley Bioinformatics Open Source Projects, Lawrence Berkeley National Laboratory, USA. elee@berkeleybop.org|Berkeley Bioinformatics Open Source Projects, Lawrence Berkeley National Laboratory, USA. elee@berkeleybop.org|Berkeley Bioinformatics Open Source Projects, Lawrence Berkeley National Laboratory, USA. elee@berkeleybop.org|Berkeley Bioinformatics Open Source Projects, Lawrence Berkeley National Laboratory, USA. elee@berkeleybop.org|Berkeley Bioinformatics Open Source Projects, Lawrence Berkeley National Laboratory, USA. elee@berkeleybop.org', 'United States|United States|United States|United States|United States', 34, 'Computer Graphics|Database Management Systems|Genome|Genomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp314', '19439563', 'Apollo is a genome annotation-editing tool with an easy to use graphical interface. It is a component of the GMOD project, with ongoing development driven by the community. Recent additions to the software include support for the generic feature format version 3 (GFF3), continuous transcriptome data, a full Chado database interface, integration with remote services for on-the-fly BLAST and Primer BLAST analyses, graphical interfaces for configuring user preferences and full undo of all edit operations. Apollos user community continues to grow, including its use as an educational tool for college and high-school students.'),
(74808, 2009, 'bio', 'Figure mining for biomedical research', 'Rodriguez-Esteban, Raul|Iossifov, Ivan', 'Systems Biology, Pfizer Inc., Cambridge, MA 02139, USA. raul.rodriguez-esteban@pfizer.com|Systems Biology, Pfizer Inc., Cambridge, MA 02139, USA. raul.rodriguez-esteban@pfizer.com', 'United States|United States', 16, 'Abstracting and Indexing as Topic|Biomedical Research|Computational Biology|Data Mining|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp318', '19439564', 'Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types.'),
(74809, 2009, 'bio', 'SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences', 'Monzoorul Haque, M|Ghosh, Tarini Shankar|Komanduri, Dinakar|Mande, Sharmila S', 'Bio-Sciences Division, Innovation Laboratory, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500 081, India.|Bio-Sciences Division, Innovation Laboratory, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500 081, India.|Bio-Sciences Division, Innovation Laboratory, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500 081, India.|Bio-Sciences Division, Innovation Laboratory, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad 500 081, India.', 'India|India|India|India', 93, 'Algorithms|Base Sequence|Classification|Databases, Genetic|Genomics|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btp317', '19439565', 'One of the first steps in metagenomic analysis is the assignment of reads/contigs obtained from various sequencing technologies to their correct taxonomic bins. Similarity-based binning methods assign a read to a taxon/clade, based on the pattern of significant BLAST hits generated against sequence databases. Existing methods, which use bit-score as the sole parameter to ascertain the significance of BLAST hits, have limited specificity and accuracy of binning. A new binning algorithm, called SOrt-ITEMS is introduced, which addresses these limitations. The method uses alignment parameters besides the bit score to first identify an appropriate taxonomic level where the read can be assigned. An orthology-based approach is subsequently used by the method for the final assignment.'),
(74810, 2009, 'bio', 'Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks', 'Seo, Chang H|Kim, Jeong-Rae|Kim, Man-Sun|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea. ckh@kaist.ac.kr|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea. ckh@kaist.ac.kr|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea. ckh@kaist.ac.kr|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea. ckh@kaist.ac.kr', '-|-|-|-', 23, 'Computer Simulation|Feedback, Physiological|Gene Expression Regulation, Developmental|Gene Regulatory Networks|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btp316', '19439566', 'Spatio-temporal regulation of gene expression is an indispensable characteristic in the development processes of all animals. Master switches, a central set of regulatory genes whose states (on/off or activated/deactivated) determine specific developmental fate or cell-fate specification, play a pivotal role for whole developmental processes. In this study on genome-wide integrative network analysis the underlying design principles of developmental gene regulatory networks are examined.'),
(74811, 2009, 'bio', 'Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins', 'Torres-García, Wandaliz|Zhang, Weiwen|Runger, George C|Johnson, Roger H|Meldrum, Deirdre R', 'Department of Industrial, Systems and Operations Engineering, Tempe, AZ 85287-5906, USA. weiwen.zhang@asu.edu|Department of Industrial, Systems and Operations Engineering, Tempe, AZ 85287-5906, USA. weiwen.zhang@asu.edu|Department of Industrial, Systems and Operations Engineering, Tempe, AZ 85287-5906, USA. weiwen.zhang@asu.edu|Department of Industrial, Systems and Operations Engineering, Tempe, AZ 85287-5906, USA. weiwen.zhang@asu.edu|Department of Industrial, Systems and Operations Engineering, Tempe, AZ 85287-5906, USA. weiwen.zhang@asu.edu', 'United States|United States|United States|United States|United States', 23, 'Bacterial Proteins|Databases, Protein|Desulfovibrio vulgaris|Gene Expression Profiling|Nonlinear Dynamics|Proteomics', 'Journal Article', '10.1093/bioinformatics/btp325', '19447782', 'Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems.'),
(74812, 2009, 'bio', 'Data structures and compression algorithms for genomic sequence data', 'Brandon, Marty C|Wallace, Douglas C|Baldi, Pierre', 'Department of Computer Science, UCI, Irvine, CA 92697, USA.|Department of Computer Science, UCI, Irvine, CA 92697, USA.|Department of Computer Science, UCI, Irvine, CA 92697, USA.', 'United States|United States|United States', 70, 'Algorithms|Data Compression|Genome|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp319', '19447783', 'The continuing exponential accumulation of full genome data, including full diploid human genomes, creates new challenges not only for understanding genomic structure, function and evolution, but also for the storage, navigation and privacy of genomic data. Here, we develop data structures and algorithms for the efficient storage of genomic and other sequence data that may also facilitate querying and protecting the data.'),
(74813, 2009, 'bio', 'Structure discovery in PPI networks using pattern-based network decomposition', 'Bachman, Philip|Liu, Ying', 'Department of Computer Science and Department of Molecular Biology, University of Texas at Dallas, Richardson, TX 75083-0688, USA.|Department of Computer Science and Department of Molecular Biology, University of Texas at Dallas, Richardson, TX 75083-0688, USA.', 'United States|United States', 12, 'Algorithms|Computational Biology|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btp297', '19447784', 'The large, complex networks of interactions between proteins provide a lens through which one can examine the structure and function of biological systems. Previous analyses of these continually growing networks have primarily followed either of two approaches: large-scale statistical analysis of holistic network properties, or small-scale analysis of local topological features. Meanwhile, investigation of meso-scale network structure (above that of individual functional modules, while maintaining the significance of individual proteins) has been hindered by the computational complexity of structural search in networks. Examining protein-protein interaction (PPI) networks at the meso-scale may provide insights into the presence and form of relationships between individual protein complexes and functional modules.'),
(74814, 2009, 'bio', 'Relating periodicity of nucleosome organization and gene regulation', 'Wan, Jun|Lin, Jimmy|Zack, Donald J|Qian, Jiang', 'Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.|Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.|Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.|Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.', 'United States|United States|United States|United States', 0, 'Chromatin|Cluster Analysis|Gene Expression Regulation|Humans|Nucleosomes|Promoter Regions, Genetic|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btp323', '19447785', 'The relationship between nucleosome positioning and gene regulation is fundamental yet complex. Previous studies on genomic nucleosome positions have revealed a correlation between nucleosome occupancy on promoters and gene expression levels. Many of these studies focused on individual nucleosomes, especially those proximal to transcription start sites. To study the collective effect of multiple nucleosomes on the gene expression, we developed a mathematical approach based on autocorrelation to relate genomic nucleosome organization to gene regulation.'),
(74815, 2009, 'bio', 'A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide', 'Wren, Jonathan D', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation,, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com', 'United States', 0, 'Computational Biology|Gene Expression Profiling|Genes|Humans|Oligonucleotide Array Sequence Analysis|Publications', 'Journal Article', '10.1093/bioinformatics/btp290', '19447786', 'Approximately 9334 (37%) of human genes have no publications documenting their function and, for those that are published, the number of publications per gene is highly skewed. Furthermore, for reasons not clear, the entry of new gene names into the literature has slowed in recent years. If we are to better understand human/mammalian biology and complete the catalog of human gene function, it is important to finish predicting putative functions for these genes based upon existing experimental evidence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74816, 2009, 'bio', 'Gradient lasso for Cox proportional hazards model', 'Sohn, Insuk|Kim, Jinseog|Jung, Sin-Ho|Park, Changyi', 'Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA.|Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA.|Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA.|Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA.', 'United States|United States|United States|United States', 38, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Lymphoma, B-Cell|Proportional Hazards Models', 'Journal Article', '10.1093/bioinformatics/btp322', '19447787', 'There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Coxs proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data.'),
(74817, 2009, 'bio', 'Highly accelerated feature detection in proteomics data sets using modern graphics processing units', 'Hussong, Rene|Gregorius, Barbara|Tholey, Andreas|Hildebrandt, Andreas', 'Computer Science Department, Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. rene@bioinf.uni-sb.de|Computer Science Department, Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. rene@bioinf.uni-sb.de|Computer Science Department, Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. rene@bioinf.uni-sb.de|Computer Science Department, Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. rene@bioinf.uni-sb.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Graphics|Databases, Protein|Mass Spectrometry|Pattern Recognition, Automated|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btp294', '19447788', 'Mass spectrometry (MS) is one of the most important techniques for high-throughput analysis in proteomics research. Due to the large number of different proteins and their post-translationally modified variants, the amount of data generated by a single wet-lab MS experiment can easily exceed several gigabytes. Hence, the time necessary to analyze and interpret the measured data is often significantly larger than the time spent on sample preparation and the wet-lab experiment itself. Since the automated analysis of this data is hampered by noise and baseline artifacts, more sophisticated computational techniques are required to handle the recorded mass spectra. Obviously, there is a clear tradeoff between performance and quality of the analysis, which is currently one of the most challenging problems in computational proteomics.'),
(74818, 2009, 'bio', 'Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data', 'Choi, Hyungwon|Nesvizhskii, Alexey I|Ghosh, Debashis|Qin, Zhaohui S', 'Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States', 24, 'Chromatin Immunoprecipitation|Genome|Genomics|Markov Chains|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp312', '19447789', 'Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available. Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS.'),
(74819, 2009, 'bio', 'ISMB/ECCB 2009 Stockholm', 'Sagot, Marie-France|McKay, B J Morrison|Myers, Gene', 'Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 1, 'Computational Biology|Genomics', 'Congress', '10.1093/bioinformatics/btp280', '19447790', '-'),
(74820, 2009, 'bio', 'Fast and accurate short read alignment with Burrows-Wheeler transform', 'Li, Heng|Durbin, Richard', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK.', 'United Kingdom|United Kingdom', 17706, 'Algorithms|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp324', '19451168', 'The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.'),
(74821, 2009, 'bio', 'Efficient computation of all perfect repeats in genomic sequences of up to half a gigabyte, with a case study on the human genome', 'Becher, Verónica|Deymonnaz, Alejandro|Heiber, Pablo', 'Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina. vbecher@dc.uba.ar|Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina. vbecher@dc.uba.ar|Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina. vbecher@dc.uba.ar', 'Argentina|Argentina|Argentina', 22, 'Algorithms|Genome, Human|Genomics|Humans|Repetitive Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btp321', '19451169', 'There is a significant ongoing research to identify the number and types of repetitive DNA sequences. As more genomes are sequenced, efficiency and scalability in computational tools become mandatory. Existing tools fail to find distant repeats because they cannot accommodate whole chromosomes, but segments. Also, a quantitative framework for repetitive elements inside a genome or across genomes is still missing.'),
(74822, 2009, 'bio', 'jClust: a clustering and visualization toolbox', 'Pavlopoulos, Georgios A|Moschopoulos, Charalampos N|Hooper, Sean D|Schneider, Reinhard|Kossida, Sophia', 'Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany. pavlopou@embl.de|Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany. pavlopou@embl.de|Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany. pavlopou@embl.de|Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany. pavlopou@embl.de|Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany. pavlopou@embl.de', 'Germany|Germany|Germany|Germany|Germany', 16, 'Cluster Analysis|Computational Biology|Databases, Genetic|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btp330', '19454618', 'jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron-Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside-inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction.'),
(74823, 2009, 'bio', 'An active registry for bioinformatics web services', 'Pettifer, S|Thorne, D|McDermott, P|Attwood, T|Baran, J|Bryne, J C|Hupponen, T|Mowbray, D|Vriend, G', 'School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, The University of Manchester, UK. steve.pettifer@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 28, 'Computational Biology|Databases, Factual|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp329', '19460889', 'The EMBRACE Registry is a web portal that collects and monitors web services according to test scripts provided by the their administrators. Users are able to search for, rank and annotate services, enabling them to select the most appropriate working service for inclusion in their bioinformatics analysis tasks.'),
(74824, 2009, 'bio', 'Predictor correlation impacts machine learning algorithms: implications for genomic studies', 'Nicodemus, Kristin K|Malley, James D', 'Department of Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. kristin.nicodemus@well.ox.ac.uk|Department of Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. kristin.nicodemus@well.ox.ac.uk', 'United Kingdom|United Kingdom', 83, 'Algorithms|Artificial Intelligence|Genome|Genome-Wide Association Study|Genomics|Oligonucleotide Array Sequence Analysis|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp331', '19460890', 'The advent of high-throughput genomics has produced studies with large numbers of predictors (e.g. genome-wide association, microarray studies). Machine learning algorithms (MLAs) are a computationally efficient way to identify phenotype-associated variables in high-dimensional data. There are important results from mathematical theory and numerous practical results documenting their value. One attractive feature of MLAs is that many operate in a fully multivariate environment, allowing for small-importance variables to be included when they act cooperatively. However, certain properties of MLAs under conditions common in genomic-related data have not been well-studied--in particular, correlations among predictors pose a problem.'),
(74825, 2009, 'bio', 'Using chemical organization theory for model checking', 'Kaleta, Christoph|Richter, Stephan|Dittrich, Peter', 'Department of Mathematics and Computer Science, Bio Systems Analysis Group, Jena Centre for Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. dittrich@minet.uni-jena.de|Department of Mathematics and Computer Science, Bio Systems Analysis Group, Jena Centre for Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. dittrich@minet.uni-jena.de|Department of Mathematics and Computer Science, Bio Systems Analysis Group, Jena Centre for Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. dittrich@minet.uni-jena.de', 'Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Models, Chemical|Models, Statistical|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp332', '19468053', 'The increasing number and complexity of biomodels makes automatic procedures for checking the models properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws.'),
(74826, 2009, 'bio', 'rtracklayer: an R package for interfacing with genome browsers', 'Lawrence, Michael|Gentleman, Robert|Carey, Vincent', 'Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98102, USA. mflawren@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98102, USA. mflawren@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98102, USA. mflawren@fhcrc.org', 'United States|United States|United States', 169, 'Databases, Genetic|Genome|Genomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp328', '19468054', 'The rtracklayer package supports the integration of existing genome browsers with experimental data analyses performed in R. The user may (i) transfer annotation tracks to and from a genome browser and (ii) create and manipulate browser views to focus on a particular set of annotations in a specific genomic region. Currently, the UCSC genome browser is supported.'),
(74827, 2009, 'bio', 'NTAP: for NimbleGen tiling array ChIP-chip data analysis', 'He, Kun|Li, Xueyong|Zhou, Junli|Deng, Xing-Wang|Zhao, Hongyu|Luo, Jingchu', 'College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com|College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com|College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com|College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com|College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com|College of Life Sciences, Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China. hekun78@gmail.com', 'China|China|China|China|China|China', 2, 'Chromatin Immunoprecipitation|Databases, Genetic|Epigenesis, Genetic|Gene Expression Profiling|Genome|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btp320', '19468055', 'NTAP is designed to analyze ChIP-chip data generated by the NimbleGen tiling array platform and to accomplish various pattern recognition tasks that are useful especially for epigenetic studies. The modular design of NTAP makes the data processing highly customizable. Users can either use NTAP to perform the full process of NimbleGen tiling array data analysis, or choose post-processing modules in NTAP to analyze pre-processed epigenetic data generated by other platforms. The output of NTAP can be saved in standard GFF format files and visualized in GBrowse.'),
(74828, 2009, 'bio', 'Genetic modification of flux for flux prediction of mutants', 'Zhao, Quanyu|Kurata, Hiroyuki', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan.|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan.', 'Japan|Japan', 0, 'Algorithms|Corynebacterium glutamicum|Databases, Protein|Escherichia coli|Gene Expression Regulation, Bacterial|Mutation', 'Journal Article', '10.1093/bioinformatics/btp298', '19468056', 'Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level.'),
(74829, 2009, 'bio', 'CORAL: aligning conserved core regions across domain families', 'Fong, Jessica H|Marchler-Bauer, Aron', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. fongj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. fongj@ncbi.nlm.nih.gov', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp334', '19470584', 'Homologous protein families share highly conserved sequence and structure regions that are frequent targets for comparative analysis of related proteins and families. Many protein families, such as the curated domain families in the Conserved Domain Database (CDD), exhibit similar structural cores. To improve accuracy in aligning such protein families, we propose a profile-profile method CORAL that aligns individual core regions as gap-free units.'),
(74830, 2009, 'bio', 'Treevolution: visual analysis of phylogenetic trees', 'Santamaría, Rodrigo|Therón, Roberto', 'Department of Computer Science and Automation, 37008 Salamanca, Spain. rodri@usal.es|Department of Computer Science and Automation, 37008 Salamanca, Spain. rodri@usal.es', 'Spain|Spain', 12, 'Algorithms|Computational Biology|Databases, Genetic|Internet|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp333', '19470585', 'Treevolution is a tool for the representation and exploration of phylogenetic trees that facilitates visual analysis. There are several useful tools to visualize phylogenetic trees, but their level of interaction is usually low, especially in the case of radial representations. Highly interactive visualizations can improve the exploration and understanding of phylogenetic trees. Treevolution implements strategies to interact with phylogenetic trees in order to allow a more thorough analysis by users.'),
(74831, 2009, 'bio', 'Modeling stochasticity and robustness in gene regulatory networks', 'Garg, Abhishek|Mohanram, Kartik|Di Cara, Alessandro|De Micheli, Giovanni|Xenarios, Ioannis', 'Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks', 'Journal Article', '10.1093/bioinformatics/btp214', '19477975', 'Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.'),
(74832, 2009, 'bio', 'Grouped graphical Granger modeling for gene expression regulatory networks discovery', 'Lozano, Aurélie C|Abe, Naoki|Liu, Yan|Rosset, Saharon', 'Mathematical Sciences Department, IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA. aclozano@us.ibm.com|Mathematical Sciences Department, IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA. aclozano@us.ibm.com|Mathematical Sciences Department, IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA. aclozano@us.ibm.com|Mathematical Sciences Department, IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA. aclozano@us.ibm.com', 'United States|United States|United States|United States', 84, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|HeLa Cells|Humans', 'Journal Article', '10.1093/bioinformatics/btp199', '19477976', 'We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of Granger causality to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem--the group structure among the lagged temporal variables naturally imposed by the time series they belong to. Specifically, existing methods in computational biology share this shortcoming, as well as additional computational limitations, prohibiting their effective applications to the large datasets including a large number of genes and many data points. In the present article, we propose a novel methodology which we term grouped graphical Granger modeling method, which overcomes the limitations mentioned above by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to. We demonstrate the effectiveness of the proposed methodology on both simulated and actual gene expression data, specifically the human cancer cell (HeLa S3) cycle data. The simulation results show that the proposed methodology generally exhibits higher accuracy in recovering the underlying causal structure. Those on the gene expression data demonstrate that it leads to improved accuracy with respect to prediction of known links, and also uncovers additional causal relationships uncaptured by earlier works.'),
(74833, 2009, 'bio', 'Clustered alignments of gene-expression time series data', 'Smith, Adam A|Vollrath, Aaron|Bradfield, Christopher A|Craven, Mark', 'Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, USA. aasmith@cs.wisc.edu|Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, USA. aasmith@cs.wisc.edu|Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, USA. aasmith@cs.wisc.edu|Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, USA. aasmith@cs.wisc.edu', 'United States|United States|United States|United States', 15, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Liver|Mice|Mice, Knockout', 'Journal Article', '10.1093/bioinformatics/btp206', '19477977', 'Characterizing and comparing temporal gene-expression responses is an important computational task for answering a variety of questions in biological studies. Algorithms for aligning time series represent a valuable approach for such analyses. However, previous approaches to aligning gene-expression time series have assumed that all genes should share the same alignment. Our work is motivated by the need for methods that identify sets of genes that differ in similar ways between two time series, even when their expression profiles are quite different.'),
(74834, 2009, 'bio', 'KELLER: estimating time-varying interactions between genes', 'Song, Le|Kolar, Mladen|Xing, Eric P', 'School of Computer Science, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Cell Cycle|Drosophila|Gene Expression Regulation|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btp192', '19477978', 'Gene regulatory networks underlying temporal processes, such as the cell cycle or the life cycle of an organism, can exhibit significant topological changes to facilitate the underlying dynamic regulatory functions. Thus, it is essential to develop methods that capture the temporal evolution of the regulatory networks. These methods will be an enabling first step for studying the driving forces underlying the dynamic gene regulation circuitry and predicting the future network structures in response to internal and external stimuli.'),
(74835, 2009, 'bio', 'Predicting functionality of protein-DNA interactions by integrating diverse evidence', 'Ucar, Duygu|Beyer, Andreas|Parthasarathy, Srinivasan|Workman, Christopher T', 'Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States', 29, 'Binding Sites|Chromatin Immunoprecipitation|DNA, Fungal|DNA-Binding Proteins|Databases, Genetic|Gene Expression|Gene Expression Profiling|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btp213', '19477979', 'Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant, or a known regulatory interaction might not be observed under the growth conditions tested so far. To correctly identify physical interactions between transcription factors (TFs) and genes and to determine their regulatory implications under various experimental conditions, we integrated ChIP-chip data with motif binding sites, nucleosome occupancy and mRNA expression datasets within a probabilistic framework. This framework was specifically tailored for the identification of functional and non-functional DNA binding events. Using this, we estimate that only 50% of condition-specific protein-DNA binding in budding yeast is functional. We further investigated the molecular factors determining the functionality of protein-DNA interactions under diverse growth conditions. Our analysis suggests that the functionality of binding is highly condition-specific and highly dependent on the presence of specific cofactors. Hence, the joint analysis of both, functional and non-functional DNA binding, may lend important new insights into transcriptional regulation.'),
(74836, 2009, 'bio', 'Probabilistic retrieval and visualization of biologically relevant microarray experiments', 'Caldas, José|Gehlenborg, Nils|Faisal, Ali|Brazma, Alvis|Kaski, Samuel', 'Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi|Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi|Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi|Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi|Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi', 'Finland|Finland|Finland|Finland|Finland', 41, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp215', '19477980', 'As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.'),
(74837, 2009, 'bio', 'Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum', 'Huang, Yang|Wuchty, Stefan|Ferdig, Michael T|Przytycka, Teresa M', 'National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 0, 'Animals|Databases, Genetic|Gene Expression Profiling|Models, Genetic|Plasmodium falciparum|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btp189', '19477981', 'Analysis of expression quantitative trait loci (eQTL) significantly contributes to the determination of gene regulation programs. However, the discovery and analysis of associations of gene expression levels and their underlying sequence polymorphisms continue to pose many challenges. Methods are limited in their ability to illuminate the full structure of the eQTL data. Most rely on an exhaustive, genome scale search that considers all possible locus-gene pairs and tests the linkage between each locus and gene.'),
(74838, 2009, 'bio', 'Proteome coverage prediction with infinite Markov models', 'Claassen, Manfred|Aebersold, Ruedi|Buhmann, Joachim M', 'Department of Computer Science, Institute of Molecular Systems Biology, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland. manfredc@inf.ethz.ch|Department of Computer Science, Institute of Molecular Systems Biology, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland. manfredc@inf.ethz.ch|Department of Computer Science, Institute of Molecular Systems Biology, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland. manfredc@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland', 0, 'Chromatography, Liquid|Databases, Protein|Markov Chains|Mass Spectrometry|Models, Biological|Proteins|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btp233', '19477982', 'Liquid chromatography tandem mass spectrometry (LC-MS/MS) is the predominant method to comprehensively characterize complex protein mixtures such as samples from prefractionated or complete proteomes. In order to maximize proteome coverage for the studied sample, i.e. identify as many traceable proteins as possible, LC-MS/MS experiments are typically repeated extensively and the results combined. Proteome coverage prediction is the task of estimating the number of peptide discoveries of future LC-MS/MS experiments. Proteome coverage prediction is important to enhance the design of efficient proteomics studies. To date, there does not exist any method to reliably estimate the increase of proteome coverage at an early stage.'),
(74839, 2009, 'bio', 'Fewer permutations, more accurate P-values', 'Knijnenburg, Theo A|Wessels, Lodewyk F A|Reinders, Marcel J T|Shmulevich, Ilya', 'Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btp211', '19477983', 'Permutation tests have become a standard tool to assess the statistical significance of an event under investigation. The statistical significance, as expressed in a P-value, is calculated as the fraction of permutation values that are at least as extreme as the original statistic, which was derived from non-permuted data. This empirical method directly couples both the minimal obtainable P-value and the resolution of the P-value to the number of permutations. Thereby, it imposes upon itself the need for a very large number of permutations when small P-values are to be accurately estimated. This is computationally expensive and often infeasible.'),
(74840, 2009, 'bio', 'A general computational method for robustness analysis with applications to synthetic gene networks', 'Rizk, Aurélien|Batt, Gregory|Fages, François|Soliman, Sylvain', 'INRIA Paris-Rocquencourt, 78153 Le Chesnay Cedex, France.|INRIA Paris-Rocquencourt, 78153 Le Chesnay Cedex, France.|INRIA Paris-Rocquencourt, 78153 Le Chesnay Cedex, France.|INRIA Paris-Rocquencourt, 78153 Le Chesnay Cedex, France.', 'France|France|France|France', 74, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btp200', '19477984', 'Robustness is the capacity of a system to maintain a function in the face of perturbations. It is essential for the correct functioning of natural and engineered biological systems. Robustness is generally defined in an ad hoc, problem-dependent manner, thus hampering the fruitful development of a theory of biological robustness, recently advocated by Kitano.'),
(74841, 2009, 'bio', 'E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs', 'Yamanishi, Yoshihiro|Hattori, Masahiro|Kotera, Masaaki|Goto, Susumu|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Enzymes|Internet|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btp223', '19477985', 'The IUBMBs Enzyme Nomenclature system, commonly known as the Enzyme Commission (EC) numbers, plays key roles in classifying enzymatic reactions and in linking the enzyme genes or proteins to reactions in metabolic pathways. There are numerous reactions known to be present in various pathways but without any official EC numbers, most of which have no hope to be given ones because of the lack of the published articles on enzyme assays.'),
(74842, 2009, 'bio', 'Multi-locus match probability in a finite population: a fundamental difference between the Moran and Wright-Fisher models', 'Bhaskar, Anand|Song, Yun S', 'Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.|Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.', 'United States|United States', 11, 'Computational Biology|Genetics, Population|Models, Genetic|Population Density|Population Dynamics|Probability', 'Journal Article', '10.1093/bioinformatics/btp227', '19477986', 'A fundamental problem in population genetics, which being also of importance to forensic science, is to compute the match probability (MP) that two individuals randomly chosen from a population have identical alleles at a collection of loci. At present, 11-13 unlinked autosomal microsatellite loci are typed for forensic use. In a finite population, the genealogical relationships of individuals can create statistical non-independence of alleles at unlinked loci. However, the so-called product rule, which is used in courts in the USA, computes the MP for multiple unlinked loci by assuming statistical independence, multiplying the one-locus MPs at those loci. Analytically testing the accuracy of the product rule for more than five loci has hitherto remained an open problem.'),
(74843, 2009, 'bio', 'Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space', 'Geiger, Dan|Meek, Christopher|Wexler, Ydo', 'Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel. dang@cs.technion.ac.il|Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel. dang@cs.technion.ac.il|Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel. dang@cs.technion.ac.il', 'Israel|Israel|Israel', 13, 'Algorithms|Computational Biology|Genetic Linkage|Humans|Markov Chains|Phenotype|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btp224', '19477987', 'We develop an hidden Markov model (HMM)-based algorithm for computing exact parametric and non-parametric linkage scores in larger pedigrees than was possible before. The algorithm is applicable whenever there are chains of persons in the pedigree with no genetic measurements and with unknown affection status. The algorithm is based on shrinking the state space of the HMM considerably using such chains. In a two g-degree cousins pedigree the reduction drops the state space from being exponential in g to being linear in g. For a Finnish family in which two affected children suffer from a rare cold-inducing sweating syndrome, we were able to reduce the state space by more than five orders of magnitude from 2(50) to 2(32). In another pedigree of state-space size of 2(27), used for a study of pituitary adenoma, the state space reduced by a factor of 8.5 and consequently exact linkage scores can now be computed, rather than approximated.'),
(74844, 2009, 'bio', 'A multivariate regression approach to association analysis of a quantitative trait network', 'Kim, Seyoung|Sohn, Kyung-Ah|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, USA. sssykim@cs.cmu.edu|School of Computer Science, Carnegie Mellon University, Pittsburgh, USA. sssykim@cs.cmu.edu|School of Computer Science, Carnegie Mellon University, Pittsburgh, USA. sssykim@cs.cmu.edu', 'United States|United States|United States', 0, 'Computational Biology|Gene Regulatory Networks|Genome-Wide Association Study|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btp218', '19477989', 'Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses.'),
(74845, 2009, 'bio', 'A Classifier-based approach to identify genetic similarities between diseases', 'Schaub, Marc A|Kaplow, Irene M|Sirota, Marina|Do, Chuong B|Butte, Atul J|Batzoglou, Serafim', 'Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Arthritis, Rheumatoid|Computational Biology|Diabetes Mellitus, Type 1|Gene Expression Profiling|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp226', '19477990', 'Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease.'),
(74846, 2009, 'bio', 'Inference of locus-specific ancestry in closely related populations', 'Pasaniuc, Bogdan|Sankararaman, Sriram|Kimmel, Gad|Halperin, Eran', 'International Computer Science Institute, Computer Science Division, University of California, Berkeley, CA, USA.|International Computer Science Institute, Computer Science Division, University of California, Berkeley, CA, USA.|International Computer Science Institute, Computer Science Division, University of California, Berkeley, CA, USA.|International Computer Science Institute, Computer Science Division, University of California, Berkeley, CA, USA.', 'United States|United States|United States|United States', 93, 'African Americans|Computational Biology|European Continental Ancestry Group|Gene Frequency|Genetic Variation|Genetics, Population|Genome, Human|Human Genome Project|Humans|Recombination, Genetic', 'Journal Article', '10.1093/bioinformatics/btp197', '19477991', 'A characterization of the genetic variation of recently admixed populations may reveal historical population events, and is useful for the detection of single nucleotide polymorphisms (SNPs) associated with diseases through association studies and admixture mapping. Inference of locus-specific ancestry is key to our understanding of the genetic variation of such populations. While a number of methods for the inference of locus-specific ancestry are accurate when the ancestral populations are quite distant (e.g. African-Americans), current methods incur a large error rate when inferring the locus-specific ancestry in admixed populations where the ancestral populations are closely related (e.g. Americans of European descent).'),
(74847, 2009, 'bio', 'A geometric approach for classification and comparison of structural variants', 'Sindi, Suzanne|Helman, Elena|Bashir, Ali|Raphael, Benjamin J', 'Division of Applied Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Division of Applied Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Division of Applied Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Division of Applied Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Comparative Genomic Hybridization|DNA|Genetic Variation|Genome, Human|Genomics|Humans', 'Comparative Study', '10.1093/bioinformatics/btp208', '19477992', 'Structural variants, including duplications, insertions, deletions and inversions of large blocks of DNA sequence, are an important contributor to human genome variation. Measuring structural variants in a genome sequence is typically more challenging than measuring single nucleotide changes. Current approaches for structural variant identification, including paired-end DNA sequencing/mapping and array comparative genomic hybridization (aCGH), do not identify the boundaries of variants precisely. Consequently, most reported human structural variants are poorly defined and not readily compared across different studies and measurement techniques.'),
(74848, 2009, 'bio', 'Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data', 'Yin, Junming|Jordan, Michael I|Song, Yun S', 'Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.|Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.|Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.', 'United States|United States|United States', 13, 'Animals|Computational Biology|Crossing Over, Genetic|Drosophila melanogaster|Gene Conversion|Likelihood Functions|Markov Chains|Polymorphism, Single Nucleotide|X Chromosome', 'Journal Article', '10.1093/bioinformatics/btp229', '19477993', 'Two known types of meiotic recombination are crossovers and gene conversions. Although they leave behind different footprints in the genome, it is a challenging task to tease apart their relative contributions to the observed genetic variation. In particular, for a given population SNP dataset, the joint estimation of the crossover rate, the gene conversion rate and the mean conversion tract length is widely viewed as a very difficult problem.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74849, 2009, 'bio', 'Domain-oriented edge-based alignment of protein interaction networks', 'Guo, Xin|Hartemink, Alexander J', 'Department of Computer Science, Duke University, Durham, NC 27708-0129, USA. xinguo@cs.duke.edu|Department of Computer Science, Duke University, Durham, NC 27708-0129, USA. xinguo@cs.duke.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btp202', '19477994', 'Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein-protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment.'),
(74850, 2009, 'bio', 'Network-based prediction of metabolic enzymes subcellular localization', 'Mintz-Oron, Shira|Aharoni, Asaph|Ruppin, Eytan|Shlomi, Tomer', 'Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel. shira.mintz@weizmann.ac.il|Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel. shira.mintz@weizmann.ac.il|Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel. shira.mintz@weizmann.ac.il|Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel. shira.mintz@weizmann.ac.il', 'Israel|Israel|Israel|Israel', 0, 'Computational Biology|Databases, Protein|Enzymes|Metabolic Networks and Pathways|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp209', '19477995', 'Revealing the subcellular localization of proteins within membrane-bound compartments is of a major importance for inferring protein function. Though current high-throughput localization experiments provide valuable data, they are costly and time-consuming, and due to technical difficulties not readily applicable for many Eukaryotes. Physical characteristics of proteins, such as sequence targeting signals and amino acid composition are commonly used to predict subcellular localizations using computational approaches. Recently it was shown that protein-protein interaction (PPI) networks can be used to significantly improve the prediction accuracy of protein subcellular localization. However, as high-throughput PPI data depend on costly high-throughput experiments and are currently available for only a few organisms, the scope of such methods is yet limited.'),
(74851, 2009, 'bio', 'IsoRankN: spectral methods for global alignment of multiple protein networks', 'Liao, Chung-Shou|Lu, Kanghao|Baym, Michael|Singh, Rohit|Berger, Bonnie', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.', '-|-|-|-|-', 240, 'Algorithms|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp203', '19477996', 'With the increasing availability of large protein-protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species.'),
(74852, 2009, 'bio', 'Global alignment of protein-protein interaction networks by graph matching methods', 'Zaslavskiy, Mikhail|Bach, Francis|Vert, Jean-Philippe', 'Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honoré, Fontainebleau, Institut Curie, INSERM, U900, Paris, France.|Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honoré, Fontainebleau, Institut Curie, INSERM, U900, Paris, France.|Centre for Computational Biology, Mines ParisTech, 35 rue Saint-Honoré, Fontainebleau, Institut Curie, INSERM, U900, Paris, France.', 'France|France|France', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btp196', '19477997', 'Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is, however, a difficult combinatorial problem, for which only heuristic methods have been proposed so far.'),
(74853, 2009, 'bio', 'PICKY: a novel SVD-based NMR spectra peak picking method', 'Alipanahi, Babak|Gao, Xin|Karakoc, Emre|Donaldson, Logan|Li, Ming', 'David R.Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.|David R.Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.|David R.Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.|David R.Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.|David R.Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada', 53, 'Algorithms|Nuclear Magnetic Resonance, Biomolecular|Pattern Recognition, Automated|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp225', '19477998', 'Picking peaks from experimental NMR spectra is a key unsolved problem for automated NMR protein structure determination. Such a process is a prerequisite for resonance assignment, nuclear overhauser enhancement (NOE) distance restraint assignment, and structure calculation tasks. Manual or semi-automatic peak picking, which is currently the prominent way used in NMR labs, is tedious, time consuming and costly.'),
(74854, 2009, 'bio', 'A framework to refine particle clusters produced by EMAN', 'Fan, Liya|Zhang, Fa|Wang, Gongming|Liu, Zhiyong', 'Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China. fanliya@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China. fanliya@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China. fanliya@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China. fanliya@ict.ac.cn', 'China|China|China|China', 0, 'Computational Biology|Image Processing, Computer-Assisted|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp219', '19477999', 'EMAN is one of the most popular software packages for single particle reconstruction. But the particle clusters produced during its model refining stage are of low qualities. We attempt to refine the particle clusters by more accurately determining orientations of particles, and thereby achieving higher resolutions of consequent 3D structures.'),
(74855, 2009, 'bio', 'Pokefind: a novel topological filter for use with protein structure prediction', 'Khatib, Firas|Rohl, Carol A|Karplus, Kevin', 'Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. firas@u.washington.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. firas@u.washington.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. firas@u.washington.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp198', '19478000', 'Our focus has been on detecting topological properties that are rare in real proteins, but occur more frequently in models generated by protein structure prediction methods such as Rosetta. We previously created the Knotfind algorithm, successfully decreasing the frequency of knotted Rosetta models during CASP6. We observed an additional class of knot-like loops that appeared to be equally un-protein-like and yet do not contain a mathematical knot. These topological features are commonly referred to as slip-knots and are caused by the same mechanisms that result in knotted models. Slip-knots are undetectable by the original Knotfind algorithm. We have generalized our algorithm to detect them, and analyzed CASP6 models built using the Rosetta loop modeling method.'),
(74856, 2009, 'bio', 'REPETITA: detection and discrimination of the periodicity of protein solenoid repeats by discrete Fourier transform', 'Marsella, Luca|Sirocco, Francesco|Trovato, Antonio|Seno, Flavio|Tosatto, Silvio C E', 'CECAM, Lyon, France.|CECAM, Lyon, France.|CECAM, Lyon, France.|CECAM, Lyon, France.|CECAM, Lyon, France.', 'France|France|France|France|France', 0, 'Algorithms|Computational Biology|Databases, Protein|Fourier Analysis|Protein Folding|Protein Structure, Secondary|Proteins|Repetitive Sequences, Amino Acid|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp232', '19478001', 'Proteins with solenoid repeats evolve more quickly than non-repetitive ones and their periodicity may be rapidly hidden at sequence level, while still evident in structure. In order to identify these repeats, we propose here a novel method based on a metric characterizing amino-acid properties (polarity, secondary structure, molecular volume, codon diversity, electric charge) using five previously derived numerical functions.'),
(74857, 2009, 'bio', 'Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation', 'Wallach, Izhar|Lilien, Ryan H', 'Department of Computer Science, Donnelly Centre for Cellular and Biomolecular Research and Banting and Best, University of Toronto, Toronto, Ontario, Canada. izharw@cs.toronto.edu|Department of Computer Science, Donnelly Centre for Cellular and Biomolecular Research and Banting and Best, University of Toronto, Toronto, Ontario, Canada. izharw@cs.toronto.edu', 'Canada|Canada', 17, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Drug Discovery|Ligands|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btp204', '19478002', 'The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein-ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems.'),
(74858, 2009, 'bio', 'Model-based clustering of array CGH data', 'Shah, Sohrab P|Cheung, K-John|Johnson, Nathalie A|Alain, Guillaume|Gascoyne, Randy D|Horsman, Douglas E|Ng, Raymond T|Murphy, Kevin P', 'Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. sshah@bccrc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 16, 'Cluster Analysis|Comparative Genomic Hybridization|Humans|Lymphoma, B-Cell|Lymphoma, Follicular|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp205', '19478003', 'Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population.'),
(74859, 2009, 'bio', 'A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery', 'Xie, Lei|Xie, Li|Bourne, Philip E', 'San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu', 'United States|United States|United States', 76, 'Binding Sites|Computational Biology|Drug Discovery|Genome|Ligands|Models, Statistical|Mycobacterium tuberculosis|Proteins', 'Journal Article', '10.1093/bioinformatics/btp220', '19478004', 'Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand-binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile-profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein-ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs.'),
(74860, 2009, 'bio', 'Toward a gold standard for promoter prediction evaluation', 'Abeel, Thomas|Van de Peer, Yves|Saeys, Yvan', 'Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium.|Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium.|Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium.', 'Belgium|Belgium|Belgium', 47, 'Computational Biology|Databases, Genetic|Genome, Human|Humans|Promoter Regions, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btp191', '19478005', 'Promoter prediction is an important task in genome annotation projects, and during the past years many new promoter prediction programs (PPPs) have emerged. However, many of these programs are compared inadequately to other programs. In most cases, only a small portion of the genome is used to evaluate the program, which is not a realistic setting for whole genome annotation projects. In addition, a common evaluation design to properly compare PPPs is still lacking.'),
(74861, 2009, 'bio', 'DISCOVER: a feature-based discriminative method for motif search in complex genomes', 'Fu, Wenjie|Ray, Pradipta|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 8, 'Animals|Binding Sites|Computational Biology|DNA|Drosophila|Genome|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp230', '19478006', 'Identifying transcription factor binding sites (TFBSs) encoding complex regulatory signals in metazoan genomes remains a challenging problem in computational genomics. Due to degeneracy of nucleotide content among binding site instances or motifs, and intricate grammatical organization of motifs within cis-regulatory modules (CRMs), extant pattern matching-based in silico motif search methods often suffer from impractically high false positive rates, especially in the context of analyzing large genomic datasets, and noisy position weight matrices which characterize binding sites. Here, we try to address this problem by using a framework to maximally utilize the information content of the genomic DNA in the region of query, taking cues from values of various biologically meaningful genetic and epigenetic factors in the query region such as clade-specific evolutionary parameters, presence/absence of nearby coding regions, etc. We present a new method for TFBS prediction in metazoan genomes that utilizes both the CRM architecture of sequences and a variety of features of individual motifs. Our proposed approach is based on a discriminative probabilistic model known as conditional random fields that explicitly optimizes the predictive probability of motif presence in large sequences, based on the joint effect of all such features.'),
(74862, 2009, 'bio', 'Predictions of RNA secondary structure by combining homologous sequence information', 'Hamada, Michiaki|Sato, Kengo|Kiryu, Hisanori|Mituyama, Toutai|Asai, Kiyoshi', 'Mizuho Information & Research Institute, Inc, Tokyo, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, Tokyo, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, Tokyo, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, Tokyo, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, Tokyo, Japan. hamada-michiaki@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan', 31, 'Base Sequence|Computational Biology|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp228', '19478007', 'Secondary structure prediction of RNA sequences is an important problem. There have been progresses in this area, but the accuracy of prediction from an RNA sequence is still limited. In many cases, however, homologous RNA sequences are available with the target RNA sequence whose secondary structure is to be predicted.'),
(74863, 2009, 'bio', 'Assessing phylogenetic motif models for predicting transcription factor binding sites', 'Hawkins, John|Grant, Charles|Noble, William Stafford|Bailey, Timothy L', 'Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au', 'Australia|Australia|Australia|Australia', 15, 'Algorithms|Binding Sites|Computational Biology|Databases, Genetic|Genome|Phylogeny|Saccharomyces cerevisiae|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp201', '19478008', 'A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning.'),
(74864, 2009, 'bio', 'Modeling interactions between adjacent nucleosomes improves genome-wide predictions of nucleosome occupancy', 'Lubliner, Shai|Segal, Eran', 'Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.|Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel', 16, 'Chromatin|Computational Biology|Genome|Models, Theoretical|Nucleosomes|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btp216', '19478009', 'Understanding the mechanisms that govern nucleosome positioning over genomes in vivo is essential for unraveling the role of chromatin organization in transcriptional regulation. Until now, models for predicting genome-wide nucleosome occupancy have assumed that the DNA associations of neighboring nucleosomes on the genome are independent. We present a new model that relaxes this independence assumption by modeling interactions between adjacent nucleosomes.'),
(74865, 2009, 'bio', 'Efficient exact motif discovery', 'Marschall, Tobias|Rahmann, Sven', 'Computer Science Department, Bioinformatics for High-Throughput Technologies at the Chair of Algorithm Engineering, TU Dortmund, Dortmund, Germany. tobias.marschall@tu-dortmund.de|Computer Science Department, Bioinformatics for High-Throughput Technologies at the Chair of Algorithm Engineering, TU Dortmund, Dortmund, Germany. tobias.marschall@tu-dortmund.de', 'Germany|Germany', 41, 'Algorithms|Computational Biology|Markov Chains|Mycobacterium tuberculosis|Probability|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp188', '19478010', 'The motif discovery problem consists of finding over-represented patterns in a collection of biosequences. It is one of the classical sequence analysis problems, but still has not been satisfactorily solved in an exact and efficient manner. This is partly due to the large number of possibilities of defining the motif search space and the notion of over-representation. Even for well-defined formalizations, the problem is frequently solved in an ad hoc manner with heuristics that do not guarantee to find the best motif.'),
(74866, 2009, 'bio', 'A partition function algorithm for interacting nucleic acid strands', 'Chitsaz, Hamidreza|Salari, Raheleh|Sahinalp, S Cenk|Backofen, Rolf', 'Lab for Computational Biology, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada', 77, 'Algorithms|Computational Biology|Databases, Genetic|Nucleic Acid Conformation|RNA|RNA, Antisense|RNA, Untranslated|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp212', '19478011', 'Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact.'),
(74867, 2009, 'bio', 'Predicting and understanding the stability of G-quadruplexes', 'Stegle, Oliver|Payet, Linda|Mergny, Jean-Louis|MacKay, David J C|Leon, Julian Huppert', 'Cavendish Laboratory, University of Cambridge, Cambridge, UK. os252@cam.ac.uk|Cavendish Laboratory, University of Cambridge, Cambridge, UK. os252@cam.ac.uk|Cavendish Laboratory, University of Cambridge, Cambridge, UK. os252@cam.ac.uk|Cavendish Laboratory, University of Cambridge, Cambridge, UK. os252@cam.ac.uk|Cavendish Laboratory, University of Cambridge, Cambridge, UK. os252@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 71, 'Bayes Theorem|Computational Biology|DNA|Databases, Genetic|G-Quadruplexes|Genome, Human|Humans|RNA|Telomere', 'Journal Article', '10.1093/bioinformatics/btp210', '19478012', 'G-quadruplexes are stable four-stranded guanine-rich structures that can form in DNA and RNA. They are an important component of human telomeres and play a role in the regulation of transcription and translation. The biological significance of a G-quadruplex is crucially linked with its thermodynamic stability. Hence the prediction of G-quadruplex stability is of vital interest.'),
(74868, 2009, 'bio', 'Viruses selectively mutate their CD8+ T-cell epitopes--a large-scale immunomic analysis', 'Vider-Shalit, Tal|Sarid, Ronit|Maman, Kobi|Tsaban, Lea|Levi, Ran|Louzoun, Yoram', 'Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel', 24, 'Animals|Antigens, Viral|CD8-Positive T-Lymphocytes|Epitopes, T-Lymphocyte|Humans|Models, Biological|Mutation|Viral Proteins|Virus Latency', 'Journal Article', '10.1093/bioinformatics/btp221', '19478014', 'Viruses employ various means to evade immune detection. One common evasion strategy is the removal of CD8+cytotoxic T-lymphocyte epitopes. We here use a combination of multiple bioinformatic tools and large amount of genomic data to compute the epitope repertoire presented by over 1300 viruses in many HLA alleles. We define the Size of Immune Repertoire score, which represents the ratio between the epitope density within a protein and the expected density. This score is used to study viral immune evasion.'),
(74869, 2009, 'bio', 'Family classification without domain chaining', 'Joseph, Jacob M|Durand, Dannie', 'Department of Biological and Computer Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jacobmj@cmu.edu|Department of Biological and Computer Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jacobmj@cmu.edu', 'United States|United States', 16, 'Animals|Computational Biology|Conserved Sequence|Databases, Protein|Genome|Humans|Mice|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btp207', '19478015', 'Classification of gene and protein sequences into homologous families, i.e. sets of sequences that share common ancestry, is an essential step in comparative genomic analyses. This is typically achieved by construction of a sequence homology network, followed by clustering to identify dense subgraphs corresponding to families. Accurate classification of single domain families is now within reach due to major algorithmic advances in remote homology detection and graph clustering. However, classification of multidomain families remains a significant challenge. The presence of the same domain in sequences that do not share common ancestry introduces false edges in the homology network that link unrelated families and stymy clustering algorithms.'),
(74870, 2009, 'bio', 'Identifying novel constrained elements by exploiting biased substitution patterns', 'Garber, Manuel|Guttman, Mitchell|Clamp, Michele|Zody, Michael C|Friedman, Nir|Xie, Xiaohui', 'Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.|Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.|Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.|Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.|Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.|Department of Biology, Broad Institute of MIT and Harvard, 7 Cambridge Center, MIT, Cambridge, MA 02142, USA.', 'United States|United States|United States|United States|United States|United States', 168, 'Algorithms|Base Sequence|Evolution, Molecular|Genomics|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btp190', '19478016', 'Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved sequences across species, however, existing methods only model conservation as a decrease in the rate of mutation and have ignored selection acting on the pattern of mutations.'),
(74871, 2009, 'bio', 'Constrained mixture estimation for analysis and robust classification of clinical time series', 'Costa, Ivan G|Schönhuth, Alexander|Hafemeister, Christoph|Schliep, Alexander', 'Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br', 'Brazil|Brazil|Brazil|Brazil', 26, 'Classification|Computational Biology|Gene Expression Profiling|Humans|Interferon-beta|Markov Chains|Multiple Sclerosis', 'Journal Article', '10.1093/bioinformatics/btp222', '19478017', 'Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data, most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-beta (IFNbeta) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNbeta treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects.'),
(74872, 2009, 'bio', 'From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations', 'Du, Pan|Feng, Gang|Flatow, Jared|Song, Jie|Holko, Michelle|Kibbe, Warren A|Lin, Simon M', 'The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.|The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA.', 'United States|United States|United States|United States|United States|United States|United States', 83, 'Computational Biology|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Disease|Genome|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp193', '19478018', 'Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo.'),
(74873, 2009, 'bio', 'Alignment of the UMLS semantic network with BioTop: methodology and assessment', 'Schulz, Stefan|Beisswanger, Elena|van den Hoek, László|Bodenreider, Olivier|van Mulligen, Erik M', 'Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. stschulz@uni-freiburg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Pattern Recognition, Automated|Semantics|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp194', '19478019', 'For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop.'),
(74874, 2009, 'bio', 'Ontology quality assurance through analysis of term transformations', 'Verspoor, Karin|Dvorkin, Daniel|Cohen, K Bretonnel|Hunter, Lawrence', 'Center for Computational Pharmacology, University of Colorado Denver, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu|Center for Computational Pharmacology, University of Colorado Denver, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu|Center for Computational Pharmacology, University of Colorado Denver, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu|Center for Computational Pharmacology, University of Colorado Denver, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu', 'United States|United States|United States|United States', 23, 'Cluster Analysis|Computational Biology|Databases, Factual|Information Storage and Retrieval|Quality Control|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp195', '19478020', 'It is important for the quality of biological ontologies that similar concepts be expressed consistently, or univocally. Univocality is relevant for the usability of the ontology for humans, as well as for computational tools that rely on regularity in the structure of terms. However, in practice terms are not always expressed consistently, and we must develop methods for identifying terms that are not univocal so that they can be corrected.'),
(74875, 2009, 'bio', 'Computing galled networks from real data', 'Huson, Daniel H|Rupp, Regula|Berry, Vincent|Gambette, Philippe|Paul, Christophe', 'Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany. huson@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Evolution, Molecular|Neural Networks (Computer)|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btp217', '19478021', 'Developing methods for computing phylogenetic networks from biological data is an important problem posed by molecular evolution and much work is currently being undertaken in this area. Although promising approaches exist, there are no tools available that biologists could easily and routinely use to compute rooted phylogenetic networks on real datasets containing tens or hundreds of taxa. Biologists are interested in clades, i.e. groups of monophyletic taxa, and these are usually represented by clusters in a rooted phylogenetic tree. The problem of computing an optimal rooted phylogenetic network from a set of clusters, is hard, in general. Indeed, even the problem of just determining whether a given network contains a given cluster is hard. Hence, some researchers have focused on topologically restricted classes of networks, such as galled trees and level-k networks, that are more tractable, but have the practical draw-back that a given set of clusters will usually not possess such a representation.'),
(74876, 2009, 'bio', 'Genotype-phenotype associations: substitution models to detect evolutionary associations between phenotypic variables and genotypic evolutionary rate', 'OConnor, Timothy D|Mundy, Nicholas I', 'Department of Zoology, University of Cambridge, Cambridge, UK. to252@cam.ac.uk|Department of Zoology, University of Cambridge, Cambridge, UK. to252@cam.ac.uk', 'United Kingdom|United Kingdom', 0, 'Animals|Computer Simulation|Evolution, Molecular|Genotype|Humans|Models, Genetic|Phenotype', 'Journal Article', '10.1093/bioinformatics/btp231', '19478022', 'Mapping between genotype and phenotype is one of the primary goals of evolutionary genetics but one that has received little attention at the interspecies level. Recent developments in phylogenetics and statistical modelling have typically been used to examine molecular and phenotypic evolution separately. We have used this background to develop phylogenetic substitution models to test for associations between evolutionary rate of genotype and phenotype. We do this by creating hybrid rate matrices between genotype and phenotype.'),
(74877, 2009, 'bio', 'Using dynamics-based comparisons to predict nucleic acid binding sites in proteins: an application to OB-fold domains', 'Zen, Andrea|de Chiara, Cesira|Pastore, Annalisa|Micheletti, Cristian', 'SISSA, CNR-INFM Democritos and Italian Institute of Technology, I-34151 Trieste, Italy.|SISSA, CNR-INFM Democritos and Italian Institute of Technology, I-34151 Trieste, Italy.|SISSA, CNR-INFM Democritos and Italian Institute of Technology, I-34151 Trieste, Italy.|SISSA, CNR-INFM Democritos and Italian Institute of Technology, I-34151 Trieste, Italy.', 'Italy|Italy|Italy|Italy', 22, 'Amino Acid Motifs|Binding Sites|DNA|DNA-Binding Proteins|Nucleic Acid Conformation|Protein Folding|RNA|RNA-Binding Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btp339', '19487258', 'We have previously demonstrated that proteins may be aligned not only by sequence or structural homology, but also using their dynamical properties. Dynamics-based alignments are sensitive and powerful tools to compare even structurally dissimilar protein families. Here, we propose to use this method to predict protein regions involved in the binding of nucleic acids. We have used the OB-fold, a motif known to promote protein-nucleic acid interactions, to validate our approach.'),
(74878, 2009, 'bio', 'PINE-SPARKY: graphical interface for evaluating automated probabilistic peak assignments in protein NMR spectroscopy', 'Lee, Woonghee|Westler, William M|Bahrami, Arash|Eghbalnia, Hamid R|Markley, John L', 'National Magnetic Resonance Facility at Madison and Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison and Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison and Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison and Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison and Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu', 'United States|United States|United States|United States|United States', 70, 'Algorithms|Computational Biology|Computer Graphics|Nuclear Magnetic Resonance, Biomolecular|Pattern Recognition, Automated|Probability|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp345', '19497931', 'PINE-SPARKY supports the rapid, user-friendly and efficient visualization of probabilistic assignments of NMR chemical shifts to specific atoms in the covalent structure of a protein in the context of experimental NMR spectra. PINE-SPARKY is based on the very popular SPARKY package for visualizing multidimensional NMR spectra (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco). PINE-SPARKY consists of a converter (PINE2SPARKY), which takes the output from an automated PINE-NMR analysis and transforms it into SPARKY input, plus a number of SPARKY extensions. Assignments and their probabilities obtained in the PINE-NMR step are visualized as labels in SPARKYs spectrum view. Three SPARKY extensions (PINE Assigner, PINE Graph Assigner, and Assign the Best by PINE) serve to manipulate the labels that signify the assignments and their probabilities. PINE Assigner lists all possible assignments for a peak selected in the dialog box and enables the user to choose among these. A window in PINE Graph Assigner shows all atoms in a selected residue along with all atoms in its adjacent residues, in addition, it displays a ranked list of PINE-derived connectivity assignments to any selected atom. Assign the Best-by-PINE allows the user to choose a probability threshold and to automatically accept as fixed all assignments above that threshold, following this operation, only the less certain assignments need to be examined visually. Once assignments are fixed, the output files generated by PINE-SPARKY can be used as input to PINE-NMR for further refinements.'),
(74879, 2009, 'bio', 'Detecting SNPs and estimating allele frequencies in clonal bacterial populations by sequencing pooled DNA', 'Holt, Kathryn E|Teo, Yik Y|Li, Heng|Nair, Satheesh|Dougan, Gordon|Wain, John|Parkhill, Julian', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. kh2@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Computational Biology|DNA|Databases, Genetic|Gene Frequency|Polymorphism, Single Nucleotide|Salmonella paratyphi A|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp344', '19497932', 'Here, we present a method for estimating the frequencies of SNP alleles present within pooled samples of DNA using high-throughput short-read sequencing. The method was tested on real data from six strains of the highly monomorphic pathogen Salmonella Paratyphi A, sequenced individually and in a pool. A variety of read mapping and quality-weighting procedures were tested to determine the optimal parameters, which afforded > or =80% sensitivity of SNP detection and strong correlation with true SNP frequency at poolwide read depth of 40x, declining only slightly at read depths 20-40x.'),
(74880, 2009, 'bio', 'SOAP2: an improved ultrafast tool for short read alignment', 'Li, Ruiqiang|Yu, Chang|Li, Yingrui|Lam, Tak-Wah|Yiu, Siu-Ming|Kristiansen, Karsten|Wang, Jun', 'Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn|Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China. soap@genomics.org.cn', 'China|China|China|China|China|China|China', 2442, 'Computational Biology|Genome, Human|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp336', '19497933', 'SOAP2 is a significantly improved version of the short oligonucleotide alignment program that both reduces computer memory usage and increases alignment speed at an unprecedented rate. We used a Burrows Wheeler Transformation (BWT) compression index to substitute the seed strategy for indexing the reference sequence in the main memory. We tested it on the whole human genome and found that this new algorithm reduced memory usage from 14.7 to 5.4 GB and improved alignment speed by 20-30 times. SOAP2 is compatible with both single- and paired-end reads. Additionally, this tool now supports multiple text and compressed file formats. A consensus builder has also been developed for consensus assembly and SNP detection from alignment of short reads on a reference genome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74881, 2009, 'bio', 'Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering', 'Dotan-Cohen, Dikla|Kasif, Simon|Melkman, Avraham A', 'Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel 84105. dotna@cs.bgu.ac.il|Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel 84105. dotna@cs.bgu.ac.il|Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel 84105. dotna@cs.bgu.ac.il', 'Israel|Israel|Israel', 23, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Gene Expression Profiling|Humans|Proteins', 'Journal Article', '10.1093/bioinformatics/btp327', '19497934', 'There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity.'),
(74882, 2009, 'bio', 'PROCAIN server for remote protein sequence similarity search', 'Wang, Yong|Sadreyev, Ruslan I|Grishin, Nick V', 'Biomedical Engineering Program, University of Texas Southwestern Medical Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Biomedical Engineering Program, University of Texas Southwestern Medical Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Biomedical Engineering Program, University of Texas Southwestern Medical Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.', 'United States|United States|United States', 7, 'Computational Biology|Databases, Protein|Internet|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp346', '19497935', 'Sensitive and accurate detection of distant protein homology is essential for the studies of protein structure, function and evolution. We recently developed PROCAIN, a method that is based on sequence profile comparison and involves the analysis of four signals--similarities of residue content at the profile positions combined with three types of assisting information: sequence motifs, residue conservation and predicted secondary structure. Here we present the PROCAIN web server that allows the user to submit a query sequence or multiple sequence alignment and perform the search in a profile database of choice. The output is structured similar to that of BLAST, with the list of detected homologs sorted by E-value and followed by profile-profile alignments. The front page allows the user to adjust multiple options of input processing and output formatting, as well as search settings, including the relative weights assigned to the three types of assisting information.'),
(74883, 2009, 'bio', 'ABACAS: algorithm-based automatic contiguation of assembled sequences', 'Assefa, Samuel|Keane, Thomas M|Otto, Thomas D|Newbold, Chris|Berriman, Matthew', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. sa4@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. sa4@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. sa4@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. sa4@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. sa4@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 278, 'Algorithms|Computational Biology|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp347', '19497936', 'Due to the availability of new sequencing technologies, we are now increasingly interested in sequencing closely related strains of existing finished genomes. Recently a number of de novo and mapping-based assemblers have been developed to produce high quality draft genomes from new sequencing technology reads. New tools are necessary to take contigs from a draft assembly through to a fully contiguated genome sequence. ABACAS is intended as a tool to rapidly contiguate (align, order, orientate), visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. The input to ABACAS is a set of contigs which will be aligned to the reference genome, ordered and orientated, visualized in the ACT comparative browser, and optimal primer sequences are automatically generated.'),
(74884, 2009, 'bio', 'UTGB toolkit for personalized genome browsers', 'Saito, Taro L|Yoshimura, Jun|Sasaki, Shin|Ahsan, Budrul|Sasaki, Atsushi|Kuroshu, Reginaldo|Morishita, Shinichi', 'Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Chiba 277-0882, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 11, 'Databases, Genetic|Genome|Genomics|Internet|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp350', '19497937', 'The advent of high-throughput DNA sequencers has increased the pace of collecting enormous amounts of genomic information, yielding billions of nucleotides on a weekly basis. This advance represents an improvement of two orders of magnitude over traditional Sanger sequencers in terms of the number of nucleotides per unit time, allowing even small groups of researchers to obtain huge volumes of genomic data over fairly short period. Consequently, a pressing need exists for the development of personalized genome browsers for analyzing these immense amounts of locally stored data. The UTGB (University of Tokyo Genome Browser) Toolkit is designed to meet three major requirements for personalization of genome browsers: easy installation of the system with minimum efforts, browsing locally stored data and rapid interactive design of web interfaces tailored to individual needs. The UTGB Toolkit is licensed under an open source license.'),
(74885, 2009, 'bio', 'Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity', 'Zhu, Shanfeng|Zeng, Jia|Mamitsuka, Hiroshi', 'Shanghai Key Lab of Intelligent Information Processing, Fudan University, School of Computer Science, Fudan University, Shanghai 200433, China. zhushanfeng@gmail.com|Shanghai Key Lab of Intelligent Information Processing, Fudan University, School of Computer Science, Fudan University, Shanghai 200433, China. zhushanfeng@gmail.com|Shanghai Key Lab of Intelligent Information Processing, Fudan University, School of Computer Science, Fudan University, Shanghai 200433, China. zhushanfeng@gmail.com', 'China|China|China', 51, 'Abstracting and Indexing as Topic|Algorithms|Cluster Analysis|Computational Biology|Database Management Systems|Information Storage and Retrieval|MEDLINE|Medical Subject Headings|Pattern Recognition, Automated|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp338', '19497938', 'Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH thesaurus have two serious limitations: first, important semantic information may be lost when generating MeSH concept vectors, and second, the content information of the original text has been discarded.'),
(74886, 2009, 'bio', 'A clustering approach for identification of enriched domains from histone modification ChIP-Seq data', 'Zang, Chongzhi|Schones, Dustin E|Zeng, Chen|Cui, Kairong|Zhao, Keji|Peng, Weiqun', 'Department of Physics, The George Washington University, Washington, DC 20052, USA.|Department of Physics, The George Washington University, Washington, DC 20052, USA.|Department of Physics, The George Washington University, Washington, DC 20052, USA.|Department of Physics, The George Washington University, Washington, DC 20052, USA.|Department of Physics, The George Washington University, Washington, DC 20052, USA.|Department of Physics, The George Washington University, Washington, DC 20052, USA.', 'United States|United States|United States|United States|United States|United States', 602, 'Chromatin Immunoprecipitation|Cluster Analysis|Computational Biology|Genome|Histones|Protein Structure, Tertiary|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp340', '19505939', 'Chromatin states are the key to gene regulation and cell identity. Chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-Seq) is increasingly being used to map epigenetic states across genomes of diverse species. Chromatin modification profiles are frequently noisy and diffuse, spanning regions ranging from several nucleosomes to large domains of multiple genes. Much of the early work on the identification of ChIP-enriched regions for ChIP-Seq data has focused on identifying localized regions, such as transcription factor binding sites. Bioinformatic tools to identify diffuse domains of ChIP-enriched regions have been lacking.'),
(74887, 2009, 'bio', 'Identifying the topology of protein complexes from affinity purification assays', 'Friedel, Caroline C|Zimmer, Ralf', 'Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. caroline.friedel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. caroline.friedel@bio.ifi.lmu.de', 'Germany|Germany', 14, 'Algorithms|Binding Sites|Chromatography, Affinity|Computational Biology|Databases, Protein|Multiprotein Complexes|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btp353', '19505940', 'Recent advances in high-throughput technologies have made it possible to investigate not only individual protein interactions, but also the association of these proteins in complexes. So far the focus has been on the prediction of complexes as sets of proteins from the experimental results. The modular substructure and the physical interactions within the protein complexes have been mostly ignored.'),
(74888, 2009, 'bio', 'ChromA: signal-based retention time alignment for chromatography-mass spectrometry data', 'Hoffmann, Nils|Stoye, Jens', 'Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany.|Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany.', 'Germany|Germany', 28, 'Chromatography, Liquid|Gas Chromatography-Mass Spectrometry|Mass Spectrometry|Pattern Recognition, Automated|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btp343', '19505941', 'We describe ChromA, a web-based alignment tool for chromatography-mass spectrometry data from the metabolomics and proteomics domains. Users can supply their data in open and standardized file formats for retention time alignment using dynamic time warping with different configurable local distance and similarity functions. Additionally, user-defined anchors can be used to constrain and speedup the alignment. A neighborhood around each anchor can be added to increase the flexibility of the constrained alignment. ChromA offers different visualizations of the alignment for easier qualitative interpretation and comparison of the data. For the multiple alignment of more than two data files, the center-star approximation is applied to select a reference among input files to align to.'),
(74889, 2009, 'bio', 'Importing ArrayExpress datasets into R/Bioconductor', 'Kauffmann, Audrey|Rayner, Tim F|Parkinson, Helen|Kapushesky, Misha|Lukk, Margus|Brazma, Alvis|Huber, Wolfgang', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 57, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp354', '19505942', 'ArrayExpress is one of the largest public repositories of microarray datasets. R/Bioconductor provides a comprehensive suite of microarray analysis and integrative bioinformatics software. However, easy ways for importing datasets from ArrayExpress into R/Bioconductor have been lacking. Here, we present such a tool that is suitable for both interactive and automated use.'),
(74890, 2009, 'bio', 'The Sequence Alignment/Map format and SAMtools', 'Li, Heng|Handsaker, Bob|Wysoker, Alec|Fennell, Tim|Ruan, Jue|Homer, Nils|Marth, Gabor|Abecasis, Goncalo|Durbin, Richard|1000 Genome Project Data Processing Subgroup', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 19711, 'Algorithms|Base Sequence|Computational Biology|Genome|Genomics|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp352', '19505943', 'The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.'),
(74891, 2009, 'bio', 'Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood', 'Raue, A|Kreutz, C|Maiwald, T|Bachmann, J|Schilling, M|Klingmüller, U|Timmer, J', 'Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com|Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 567, 'Algorithms|Computational Biology|Models, Biological|Probability', 'Journal Article', '10.1093/bioinformatics/btp358', '19505944', 'Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis.'),
(74892, 2009, 'bio', 'trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses', 'Capella-Gutiérrez, Salvador|Silla-Martínez, José M|Gabaldón, Toni', 'Comparative Genomics Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, 88 08003 Barcelona, Spain.|Comparative Genomics Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, 88 08003 Barcelona, Spain.|Comparative Genomics Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, 88 08003 Barcelona, Spain.', 'Spain|Spain|Spain', 2352, 'Algorithms|Computational Biology|Phylogeny|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp348', '19505945', 'Multiple sequence alignments are central to many areas of bioinformatics. It has been shown that the removal of poorly aligned regions from an alignment increases the quality of subsequent analyses. Such an alignment trimming phase is complicated in large-scale phylogenetic analyses that deal with thousands of alignments. Here, we present trimAl, a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses. trimAl can consider several parameters, alone or in multiple combinations, for selecting the most reliable positions in the alignment. These include the proportion of sequences with a gap, the level of amino acid similarity and, if several alignments for the same set of sequences are provided, the level of consistency across different alignments. Moreover, trimAl can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized.'),
(74893, 2009, 'bio', 'Adaptive intervention in probabilistic boolean networks', 'Layek, Ritwik|Datta, Aniruddha|Pal, Ranadip|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.', 'United States|United States|United States|United States', 9, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Markov Chains|Probability|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp349', '19505946', 'A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of time-course data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate full-model inference.'),
(74894, 2009, 'bio', 'Rapid detection, classification and accurate alignment of up to a million or more related protein sequences', 'Neuwald, Andrew F', 'Department of Biochemistry & Molecular Biology and The Institute for Genome Sciences, University of Maryland, School of Medicine, BioPark II, Baltimore, MD 21201, USA. aneuwald@som.umaryland.edu', 'United States', 37, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp342', '19505947', 'The patterns of sequence similarity and divergence present within functionally diverse, evolutionarily related proteins contain implicit information about corresponding biochemical similarities and differences. A first step toward accessing such information is to statistically analyze these patterns, which, in turn, requires that one first identify and accurately align a very large set of protein sequences. Ideally, the set should include many distantly related, functionally divergent subgroups. Because it is extremely difficult, if not impossible for fully automated methods to align such sequences correctly, researchers often resort to manual curation based on detailed structural and biochemical information. However, multiply-aligning vast numbers of sequences in this way is clearly impractical.'),
(74895, 2009, 'bio', 'Reordering contigs of draft genomes using the Mauve aligner', 'Rissman, Anna I|Mau, Bob|Biehl, Bryan S|Darling, Aaron E|Glasner, Jeremy D|Perna, Nicole T', 'Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu|Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu|Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu|Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu|Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu|Genome Evolution Laboratory, University of Wisconsin-Madison, 425G Henry Mall Suite 4400V, Madison, WI 53706, USA. rissman@wisc.edu', 'United States|United States|United States|United States|United States|United States', 317, 'Algorithms|Contig Mapping|Genome|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp356', '19515959', 'Mauve Contig Mover provides a new method for proposing the relative order of contigs that make up a draft genome based on comparison to a complete or draft reference genome. A novel application of the Mauve aligner and viewer provides an automated reordering algorithm coupled with a powerful drill-down display allowing detailed exploration of results.'),
(74896, 2009, 'bio', 'Visual and statistical comparison of metagenomes', 'Mitra, Suparna|Klar, Bernhard|Huson, Daniel H', 'Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 65, 'Animals|Data Interpretation, Statistical|Databases, Genetic|Environmental Microbiology|Genome|Genomics|Humans|Software', 'Comparative Study', '10.1093/bioinformatics/btp341', '19515961', 'Metagenomics is the study of the genomic content of an environmental sample of microbes. Advances in the through-put and cost-efficiency of sequencing technology is fueling a rapid increase in the number and size of metagenomic datasets being generated. Bioinformatics is faced with the problem of how to handle and analyze these datasets in an efficient and useful way. One goal of these metagenomic studies is to get a basic understanding of the microbial world both surrounding us and within us. One major challenge is how to compare multiple datasets. Furthermore, there is a need for bioinformatics tools that can process many large datasets and are easy to use.'),
(74897, 2009, 'bio', 'A pattern-based nearest neighbor search approach for promoter prediction using DNA structural profiles', 'Gan, Yanglan|Guan, Jihong|Zhou, Shuigeng', 'Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.', 'China|China|China', 21, 'Cluster Analysis|Computational Biology|CpG Islands|DNA|Promoter Regions, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp359', '19515962', 'Identification of core promoters is a key clue in understanding gene regulations. However, due to the diverse nature of promoter sequences, the accuracy of existing prediction approaches for non-CpG island (simply CGI)-related promoters is not as high as that for CGI-related promoters. This consequently leads to a low genome-wide promoter prediction accuracy.'),
(74898, 2009, 'bio', 'Executing multicellular differentiation: quantitative predictive modelling of Celegans vulval development', 'Bonzanni, Nicola|Krepska, Elzbieta|Feenstra, K Anton|Fokkink, Wan|Kielmann, Thilo|Bal, Henri|Heringa, Jaap', 'Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics and Department of Computer Science, Vrije Universiteit, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 35, 'Animals|Body Patterning|Caenorhabditis elegans|Cell Differentiation|Computational Biology|Computer Simulation|Female|MicroRNAs|Organogenesis|Vulva', 'Journal Article', '10.1093/bioinformatics/btp355', '19515963', 'Understanding the processes involved in multi-cellular pattern formation is a central problem of developmental biology, hopefully leading to many new insights, e.g. in the treatment of various diseases. Defining suitable computational techniques for development modelling, able to perform in silico simulation experiments, is an open and challenging problem.'),
(74899, 2009, 'bio', 'De novo transcriptome assembly with ABySS', 'Birol, Inanç|Jackman, Shaun D|Nielsen, Cydney B|Qian, Jenny Q|Varhol, Richard|Stazyk, Greg|Morin, Ryan D|Zhao, Yongjun|Hirst, Martin|Schein, Jacqueline E|Horsman, Doug E|Connors, Joseph M|Gascoyne, Randy D|Marra, Marco A|Jones, Steven J M', 'Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca|Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada. ibirol@bcgsc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Ca', 288, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp367', '19528083', 'Whole transcriptome shotgun sequencing data from non-normalized samples offer unique opportunities to study the metabolic states of organisms. One can deduce gene expression levels using sequence coverage as a surrogate, identify coding changes or discover novel isoforms or transcripts. Especially for discovery of novel events, de novo assembly of transcriptomes is desirable.'),
(74900, 2009, 'bio', 'Comments on the analysis of unbalanced microarray data', 'Kerr, Kathleen F', 'Department of Biostatistics, Box 357232, University of Washington, Seattle, WA 98195, USA. katiek@u.washington.edu', 'United States', 19, 'Algorithms|Animals|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp363', '19528084', 'Permutation testing is very popular for analyzing microarray data to identify differentially expressed (DE) genes, estimating false discovery rates (FDRs) is a very popular way to address the inherent multiple testing problem. However, combining these approaches may be problematic when sample sizes are unequal.'),
(74901, 2009, 'bio', 'A CitationRank algorithm inheriting Google technology designed to highlight genes responsible for serious adverse drug reaction', 'Yang, Lun|Xu, Langlai|He, Lin', 'Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, PR China.|Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, PR China.|Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, PR China.', 'China|China|China', 24, 'Algorithms|Computational Biology|Drug-Related Side Effects and Adverse Reactions|False Negative Reactions|Gene Regulatory Networks|Humans|Knowledge|Models, Genetic|ROC Curve|Rhabdomyolysis', 'Journal Article', '10.1093/bioinformatics/btp369', '19528085', 'Serious adverse drug reaction (SADR) is an urgent, world-wide problem. In the absence of any well-organized gene-oriented SADR information pool, a database should be constructed. Since the importance of a gene to a particular SADR cannot simply be defined in terms of how frequently the two are cited together in the literature, an algorithm should be devised to sort genes according to their relevance to the SADR topics.'),
(74902, 2009, 'bio', 'PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables', 'Pounds, Stan|Cheng, Cheng|Cao, Xueyuan|Crews, Kristine R|Plunkett, William|Gandhi, Varsha|Rubnitz, Jeffrey|Ribeiro, Raul C|Downing, James R|Lamba, Jatinder', 'Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. stanley.pounds@stjude.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 12, 'Biometry|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btp357', '19528086', 'In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables.'),
(74903, 2009, 'bio', 'Inferring progression models for CGH data', 'Liu, Jun|Bandyopadhyay, Nirmalya|Ranka, Sanjay|Baudis, M|Kahveci, Tamer', 'Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.', 'United States|United States|United States|United States|United States', 9, 'Biomarkers, Tumor|Comparative Genomic Hybridization|Gene Dosage|Humans|Models, Genetic|Neoplasms|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btp365', '19528087', 'One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA copy number alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies.'),
(74904, 2009, 'bio', 'InterMap3D: predicting and visualizing co-evolving protein residues', 'Gouveia-Oliveira, Rodrigo|Roque, Francisco S|Wernersson, Rasmus|Sicheritz-Ponten, Thomas|Sackett, Peter W|Mølgaard, Anne|Pedersen, Anders G', 'Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark. gorm@cbs.dtu.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 16, 'Computational Biology|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp335', '19528088', 'InterMap3D predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PDB). It can also accept a user-generated alignment. Based on the alignment, co-evolving residues are then predicted using three different methods: Row and Column Weighing of Mutual Information, Mutual Information/Entropy and Dependency. Finally, InterMap3D generates high-quality images of the protein with the predicted co-evolving residues highlighted.'),
(74905, 2009, 'bio', 'An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems', 'Lu, Chuan|King, Ross D', 'Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK.|Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK.', 'United Kingdom|United Kingdom', 34, 'Computer Simulation|Metabolome|Models, Biological|Proteins|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btp360', '19535531', 'Distribution analysis is one of the most basic forms of statistical analysis. Thanks to improved analytical methods, accurate and extensive quantitative measurements can now be made of the mRNA, protein and metabolite from biological systems. Here, we report a large-scale analysis of the population abundance distributions of the transcriptomes, proteomes and metabolomes from varied biological systems.'),
(74906, 2009, 'bio', 'Reliable prediction of protein thermostability change upon double mutation from amino acid sequence', 'Huang, Liang-Tsung|Gromiha, M Michael', 'Department of Computer Science and Information Engineering, Mingdao University, Changhua 523, Taiwan.|Department of Computer Science and Information Engineering, Mingdao University, Changhua 523, Taiwan.', '-|-', 31, 'Amino Acid Sequence|Amino Acids|Internet|Molecular Sequence Data|Mutant Proteins|Mutation|Point Mutation|Protein Stability|Protein Structure, Secondary|Proteins|ROC Curve|Solvents|Temperature', 'Journal Article', '10.1093/bioinformatics/btp370', '19535532', 'The accurate prediction of protein stability change upon mutation is one of the important issues for protein design. In this work, we have focused on the stability change of double mutations and systematically analyzed the wild-type and mutant residues, patterns in amino acid sequence and locations of mutants. Based on the sequence information of wild-type, mutant and three neighboring residues, we have presented a weighted decision table method (WET) for predicting the stability changes of 180 double mutants obtained from thermal (DeltaDeltaG) denaturation. Using 10-fold cross-validation test, our method showed a correlation of 0.75 between experimental and predicted values of stability changes, and an accuracy of 82.2% for discriminating the stabilizing and destabilizing mutants.'),
(74907, 2009, 'bio', 'Modeling multi-cellular behavior in epidermal tissue homeostasis via finite state machines in multi-agent systems', 'Sütterlin, Thomas|Huber, Simone|Dickhaus, Hartmut|Grabe, Niels', 'Medical Informatics Department, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, University Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Medical Informatics Department, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, University Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Medical Informatics Department, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, University Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Medical Informatics Department, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, University Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 21, 'Computational Biology|Databases, Factual|Epidermis|Homeostasis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp361', '19535533', 'For the efficient application of multi-agent systems to spatial and functional modeling of tissues flexible and intuitive modeling tools are needed, which allow the graphical specification of cellular behavior in a tissue context without presuming specialized programming skills.'),
(74908, 2009, 'bio', 'Automated protein (re)sequencing with MS/MS and a homologous database yields almost full coverage and accuracy', 'Liu, Xiaowen|Han, Yonghua|Yuen, Denis|Ma, Bin', 'David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.', 'Canada|Canada|Canada|Canada', 24, 'Amino Acid Sequence|Animals|Automation|Cattle|Chickens|Databases, Protein|Mass Spectrometry|Molecular Sequence Data|Muramidase|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Serum Albumin, Bovine', 'Journal Article', '10.1093/bioinformatics/btp366', '19535534', 'The bottom-up tandem mass spectrometry (MS/MS) is regularly used in proteomics nowadays for identifying proteins from a sequence database. De novo sequencing software is also available for sequencing novel peptides with relatively short sequence lengths. However, automated sequencing of novel proteins from MS/MS remains a challenging problem.'),
(74909, 2009, 'bio', 'A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6', 'Bengtsson, Henrik|Wirapati, Pratyaksha|Speed, Terence P', 'Department of Statistics, University of California, Berkeley, California, USA. hb@stat.berkeley.edu|Department of Statistics, University of California, Berkeley, California, USA. hb@stat.berkeley.edu|Department of Statistics, University of California, Berkeley, California, USA. hb@stat.berkeley.edu', 'United States|United States|United States', 111, 'Base Pairing|Chromosomes, Human, Pair 10|Gene Dosage|Genome, Human|Genotype|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btp371', '19535535', 'High-resolution copy-number (CN) analysis has in recent years gained much attention, not only for the purpose of identifying CN aberrations associated with a certain phenotype, but also for identifying CN polymorphisms. In order for such studies to be successful and cost effective, the statistical methods have to be optimized. We propose a single-array preprocessing method for estimating full-resolution total CNs. It is applicable to all Affymetrix genotyping arrays, including the recent ones that also contain non-polymorphic probes. A reference signal is only needed at the last step when calculating relative CNs.'),
(74910, 2009, 'bio', 'PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating', 'Lartillot, Nicolas|Lepage, Thomas|Blanquart, Samuel', 'Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. nicolas.lartillot@umontreal.ca|Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. nicolas.lartillot@umontreal.ca|Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. nicolas.lartillot@umontreal.ca', 'Canada|Canada|Canada', 808, 'Animals|Bayes Theorem|Computational Biology|Databases, Nucleic Acid|Phylogeny|Predictive Value of Tests|Reproducibility of Results|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btp368', '19535536', 'A variety of probabilistic models describing the evolution of DNA or protein sequences have been proposed for phylogenetic reconstruction or for molecular dating. However, there still lacks a common implementation allowing one to freely combine these independent features, so as to test their ability to jointly improve phylogenetic and dating accuracy.'),
(74911, 2009, 'bio', 'A fast hybrid short read fragment assembly algorithm', 'Schmidt, Bertil|Sinha, Ranjan|Beresford-Smith, Bryan|Puglisi, Simon J', 'School of Computer Engineering, Nanyang Technological University, Singapore. asbschmidt@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. asbschmidt@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. asbschmidt@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. asbschmidt@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 36, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Helicobacter pylori|Sequence Analysis, DNA|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btp374', '19535537', 'The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this article, we present Taipan, an assembly algorithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make better decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74912, 2009, 'bio', 'A statistical framework for protein quantitation in bottom-up MS-based proteomics', 'Karpievitch, Yuliya|Stanley, Jeff|Taverner, Thomas|Huang, Jianhua|Adkins, Joshua N|Ansong, Charles|Heffron, Fred|Metz, Thomas O|Qian, Wei-Jun|Yoon, Hyunjin|Smith, Richard D|Dabney, Alan R', 'Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.|Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, 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', 102, 'Databases, Protein|Mass Spectrometry|Models, Statistical|Proteins|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btp362', '19535538', 'Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.'),
(74913, 2009, 'bio', 'Increasing the coverage of a metapopulation consensus genome by iterative read mapping and assembly', 'Dutilh, Bas E|Huynen, Martijn A|Strous, Marc', 'Center for Molecular and Biomolecular Informatics, Nijmegen Center for Molecular Life Sciences, Radboud University Nijmegen Medical Center, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands, Germany. dutilh@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Nijmegen Center for Molecular Life Sciences, Radboud University Nijmegen Medical Center, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands, Germany. dutilh@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Nijmegen Center for Molecular Life Sciences, Radboud University Nijmegen Medical Center, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands, Germany. dutilh@cmbi.ru.nl', 'Germany|Germany|Germany', 26, 'Computational Biology|Genome|Genomics|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp377', '19542148', 'Most microbial species can not be cultured in the laboratory. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold.'),
(74914, 2009, 'bio', 'GR-Aligner: an algorithm for aligning pairwise genomic sequences containing rearrangement events', 'Chu, Te-Chin|Liu, Tsunglin|Lee, D T|Lee, Greg C|Shih, Arthur Chun-Chieh', 'Institute of Information Science, Academia Sinica, National Taiwan University, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, National Taiwan University, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, National Taiwan University, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, National Taiwan University, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, National Taiwan University, Taipei, Taiwan.', '-|-|-|-|-', 4, 'Algorithms|Animals|Base Sequence|Computer Simulation|Gene Rearrangement|Genome|Humans|Pan troglodytes|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btp372', '19542149', 'Homologous genomic sequences between species usually contain different rearrangement events. Whether some specific patterns existed in the breakpoint regions that caused such events to occur is still unclear. To resolve this question, it is necessary to determine the location of breakpoints at the nucleotide level. The availability of sequences near breakpoints would further facilitate the related studies. We thus need a tool that can identify breakpoints and align the neighboring sequences. Although local alignment tools can detect rearrangement events, they only report a set of discontinuous alignments, where the detailed alignments in the breakpoint regions are usually missing. Global alignment tools are even less appropriate for these tasks since most of them are designed to align the conserved regions between sequences in a consistent order, i.e. they do not consider rearrangement events.'),
(74915, 2009, 'bio', 'FancyGene: dynamic visualization of gene structures and protein domain architectures on genomic loci', 'Rambaldi, Davide|Ciccarelli, Francesca D', 'Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy.|Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy.', 'Italy|Italy', 91, 'Computational Biology|Genes|Humans|Internet|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btp381', '19542150', 'FancyGene is a fast and user-friendly web-based tool for producing images of one or more genes directly on the corresponding genomic locus. Starting from a variety of input formats, FancyGene rebuilds the basic components of a gene (UTRs, intron, exons). Once the initial representation is obtained, the user can superimpose additional features-such as protein domains and/or a variety of biological markers-in specific positions. FancyGene is extremely flexible allowing the user to change the resulting image dynamically, modifying colors and shapes and adding and/or removing objects. The output images are generated either in portable network graphics (PNG) or portable document format (PDF) formats and can be used for scientific presentations as well as for publications. The PDF format preserves editing capabilities, allowing picture modification using any vector graphics editor.'),
(74916, 2009, 'bio', 'VarScan: variant detection in massively parallel sequencing of individual and pooled samples', 'Koboldt, Daniel C|Chen, Ken|Wylie, Todd|Larson, David E|McLellan, Michael D|Mardis, Elaine R|Weinstock, George M|Wilson, Richard K|Ding, Li', 'The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University School of Medicine, St Louis, MO 63108, USA. dkoboldt@genome.wustl.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 702, 'Computational Biology|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp373', '19542151', 'Massively parallel sequencing technologies hold incredible promise for the study of DNA sequence variation, particularly the identification of variants affecting human disease. The unprecedented throughput and relatively short read lengths of Roche/454, Illumina/Solexa, and other platforms have spurred development of a new generation of sequence alignment algorithms. Yet detection of sequence variants based on short read alignments remains challenging, and most currently available tools are limited to a single platform or aligner type. We present VarScan, an open source tool for variant detection that is compatible with several short read aligners. We demonstrate VarScans ability to detect SNPs and indels with high sensitivity and specificity, in both Roche/454 sequencing of individuals and deep Illumina/Solexa sequencing of pooled samples.'),
(74917, 2009, 'bio', 'SHREC: a short-read error correction method', 'Schröder, Jan|Schröder, Heiko|Puglisi, Simon J|Sinha, Ranjan|Schmidt, Bertil', 'Institut für Informatik, Christian-Albrecht-Universität Kiel, Herman-Rodewald-Strasse 3, 24118 Kiel, Germany. jasc@informatik.uni-kiel.de|Institut für Informatik, Christian-Albrecht-Universität Kiel, Herman-Rodewald-Strasse 3, 24118 Kiel, Germany. jasc@informatik.uni-kiel.de|Institut für Informatik, Christian-Albrecht-Universität Kiel, Herman-Rodewald-Strasse 3, 24118 Kiel, Germany. jasc@informatik.uni-kiel.de|Institut für Informatik, Christian-Albrecht-Universität Kiel, Herman-Rodewald-Strasse 3, 24118 Kiel, Germany. jasc@informatik.uni-kiel.de|Institut für Informatik, Christian-Albrecht-Universität Kiel, Herman-Rodewald-Strasse 3, 24118 Kiel, Germany. jasc@informatik.uni-kiel.de', 'Germany|Germany|Germany|Germany|Germany', 109, 'Algorithms|Computational Biology|DNA|Databases, Nucleic Acid|Genome|Research Design|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1093/bioinformatics/btp379', '19542152', 'Second-generation sequencing technologies produce a massive amount of short reads in a single experiment. However, sequencing errors can cause major problems when using this approach for de novo sequencing applications. Moreover, existing error correction methods have been designed and optimized for shotgun sequencing. Therefore, there is an urgent need for the design of fast and accurate computational methods and tools for error correction of large amounts of short read data.'),
(74918, 2009, 'bio', 'Copy number variation has little impact on bead-array-based measures of DNA methylation', 'Houseman, E Andrés|Christensen, Brock C|Karagas, Margaret R|Wrensch, Margaret R|Nelson, Heather H|Wiemels, Joseph L|Zheng, Shichun|Wiencke, John K|Kelsey, Karl T|Marsit, Carmen J', 'Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu|Department of Community Health Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. E_Andres_Houseman@brown.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 28, 'Carcinoma, Squamous Cell|Computational Biology|DNA Methylation|Gene Dosage|Gene Expression Profiling|Genome, Human|Head and Neck Neoplasms|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp364', '19542153', 'Integration of various genome-scale measures of molecular alterations is of great interest to researchers aiming to better define disease processes or identify novel targets with clinical utility. Particularly important in cancer are measures of gene copy number DNA methylation. However, copy number variation may bias the measurement of DNA methylation. To investigate possible bias, we analyzed integrated data obtained from 19 head and neck squamous cell carcinoma (HNSCC) tumors and 23 mesothelioma tumors.'),
(74919, 2009, 'bio', 'Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks', 'Kaderali, Lars|Dazert, Eva|Zeuge, Ulf|Frese, Michael|Bartenschlager, Ralf', 'Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. lars.kaderali@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. lars.kaderali@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. lars.kaderali@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. lars.kaderali@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. lars.kaderali@bioquant.uni-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany', 32, 'Algorithms|Cell Line, Tumor|Computer Simulation|Humans|Janus Kinases|Models, Statistical|RNA Interference|STAT Transcription Factors|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btp375', '19542154', 'The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data.'),
(74920, 2009, 'bio', 'Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization', 'Yang, Xuerui|Zhou, Yang|Jin, Rong|Chan, Christina', 'Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 16, 'Algorithms|Cell Death|Cell Line, Tumor|Computational Biology|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Humans|Knowledge|Palmitates|Phenotype', 'Journal Article', '10.1093/bioinformatics/btp376', '19542155', 'Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks.'),
(74921, 2009, 'bio', 'ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing', 'Quon, Gerald|Morris, Quaid', 'Department of Computer Science, University of Toronto, Toronto, Canada. gerald.quon@utoronto.ca|Department of Computer Science, University of Toronto, Toronto, Canada. gerald.quon@utoronto.ca', 'Canada|Canada', 0, 'Computational Biology|Gene Expression Profiling|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btp378', '19542156', 'One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers.'),
(74922, 2009, 'bio', 'Prestige centrality-based functional outlier detection in gene expression analysis', 'Torkamani, Ali|Schork, Nicholas J', 'The Scripps Translational Science Institute and Scripps Genomic Medicine, Scripps Health and The Scripps Research Institute, 3344 North Torrey Pines Court, Room 306 La Jolla, CA 92037 USA. atorkama@scripps.edu|The Scripps Translational Science Institute and Scripps Genomic Medicine, Scripps Health and The Scripps Research Institute, 3344 North Torrey Pines Court, Room 306 La Jolla, CA 92037 USA. atorkama@scripps.edu', 'United States|United States', 0, 'Algorithms|CD24 Antigen|Disease-Free Survival|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Hyaluronan Receptors|Neoplasm Metastasis|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btp388', '19549629', 'Traditional gene expression analysis techniques capture an average gene expression state across sample replicates. However, the average signal across replicates will not capture activated gene networks in different states across replicates. For example, if a particular gene expression network is activated within a subset or all sample replicates, yet the activation state across the sample replicates differs by the specific genes activated in each replicate, the activation of this network will be washed out by averaging across replicates. This situation is likely to occur in single cell gene expression experiments or in noisy experimental settings where a small sub-population of cells contributes to the gene expression signature of interest.'),
(74923, 2009, 'bio', 'Swift: primary data analysis for the Illumina Solexa sequencing platform', 'Whiteford, Nava|Skelly, Tom|Curtis, Christina|Ritchie, Matt E|Löhr, Andrea|Zaranek, Alexander Wait|Abnizova, Irina|Brown, Clive', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. nava.whiteford@nanoporetech.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Base Sequence|Computational Biology|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp383', '19549630', 'Primary data analysis methods are of critical importance in second generation DNA sequencing. Improved methods have the potential to increase yield and reduce the error rates. Openly documented analysis tools enable the user to understand the primary data, this is important for the optimization and validity of their scientific work.'),
(74924, 2009, 'bio', 'A survey of across-target bioactivity results of small molecules in PubChem', 'Han, Lianyi|Wang, Yanli|Bryant, Stephen H', 'National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 0, 'Cluster Analysis|Data Collection|Databases, Factual|Drug Evaluation, Preclinical|Small Molecule Libraries', 'Journal Article', '10.1093/bioinformatics/btp380', '19549631', 'This work provides an analysis of across-target bioactivity results in the screening data deposited in PubChem. Two alternative approaches for grouping-related targets are used to examine a compounds across-target bioactivity. This analysis identifies compounds that are selectively active against groups of protein targets that are identical or similar in sequence. This analysis also identifies compounds that are bioactive across unrelated targets. Statistical distributions of compound across-target selectivity provide a survey to evaluate target specificity of compounds by deriving and analyzing bioactivity profile across a wide range of biological targets for tested small molecules in PubChem. This work enables one to select target specific inhibitors, identify promiscuous compounds and better understand the biological mechanisms of target-small molecule interactions.'),
(74925, 2009, 'bio', 'SOLpro: accurate sequence-based prediction of protein solubility', 'Magnan, Christophe N|Randall, Arlo|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA.', 'United States|United States|United States', 0, 'Artificial Intelligence|Computer Simulation|Databases, Protein|Escherichia coli|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Solubility', 'Journal Article', '10.1093/bioinformatics/btp386', '19549632', 'Protein insolubility is a major obstacle for many experimental studies. A sequence-based prediction method able to accurately predict the propensity of a protein to be soluble on overexpression could be used, for instance, to prioritize targets in large-scale proteomics projects and to identify mutations likely to increase the solubility of insoluble proteins.'),
(74926, 2009, 'bio', 'The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction', 'Huttenhower, Curtis|Hibbs, Matthew A|Myers, Chad L|Caudy, Amy A|Hess, David C|Troyanskaya, Olga G', 'Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.|Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Mitochondria|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp397', '19561015', 'Rapidly expanding repositories of highly informative genomic data have generated increasing interest in methods for protein function prediction and inference of biological networks. The successful application of supervised machine learning to these tasks requires a gold standard for protein function: a trusted set of correct examples, which can be used to assess performance through cross-validation or other statistical approaches. Since gene annotation is incomplete for even the best studied model organisms, the biological reliability of such evaluations may be called into question.'),
(74927, 2009, 'bio', 'Transcriptional landscape estimation from tiling array data using a model of signal shift and drift', 'Nicolas, Pierre|Leduc, Aurélie|Robin, Stéphane|Rasmussen, Simon|Jarmer, Hanne|Bessières, Philippe', 'INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr|INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr|INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr|INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr|INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr|INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France. pierre.nicolas@jouy.inra.fr', 'France|France|France|France|France|France', 0, 'Bacillus subtilis|Computational Biology|Gene Expression Profiling|Genome|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btp395', '19561016', 'High-density oligonucleotide tiling array technology holds the promise of a better description of the complexity and the dynamics of transcriptional landscapes. In organisms such as bacteria and yeasts, transcription can be measured on a genome-wide scale with a resolution >25 bp. The statistical models currently used to handle these data remain however very simple, the most popular being the piecewise constant Gaussian model with a fixed number of breakpoints.'),
(74928, 2009, 'bio', 'libAnnotationSBML: a library for exploiting SBML annotations', 'Swainston, Neil|Mendes, Pedro', 'Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK. neil.swainston@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK. neil.swainston@manchester.ac.uk', 'United Kingdom|United Kingdom', 20, 'Computational Biology|Programming Languages|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp392', '19561017', 'The Systems Biology Markup Language (SBML) is an established community XML format for the markup of biochemical models. With the introduction of SBML level 2 version 3, specific model entities, such as species or reactions, can now be annotated using ontological terms. These annotations, which are encoded using the resource description framework (RDF), provide the facility to specify definite terms to individual components, allowing software to unambiguously identify such components and thus link the models to existing data resources. libSBML is an application programming interface library for the manipulation of SBML files. While libSBML provides the facilities for reading and writing such annotations from and to models, it is beyond the scope of libSBML to provide interpretation of these terms. The libAnnotationSBML library introduced here acts as a layer on top of libSBML linking SBML annotations to the web services that describe these ontological terms. Two applications that use this library are described: SbmlSynonymExtractor finds name synonyms of SBML model entities and SbmlReactionBalancer checks SBML files to determine whether specifed reactions are elementally balanced.'),
(74929, 2009, 'bio', 'Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads', 'Ye, Kai|Schulz, Marcel H|Long, Quan|Apweiler, Rolf|Ning, Zemin', 'EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl|EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl|EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl|EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl|EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Chromosome Breakpoints|Computational Biology|DNA Breaks|Genome|INDEL Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp394', '19561018', 'There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging.'),
(74930, 2009, 'bio', 'A pipeline for the quantitative analysis of CG dinucleotide methylation using mass spectrometry', 'Thompson, Reid F|Suzuki, Masako|Lau, Kevin W|Greally, John M', 'Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.', 'United States|United States|United States|United States', 0, 'Animals|Cell Line|Computational Biology|DNA Methylation|Dinucleoside Phosphates|Male|Mass Spectrometry|Mice|Polymorphism, Single Nucleotide|Rats|Rats, Sprague-Dawley|Reproducibility of Results|Sulfites', 'Journal Article', '10.1093/bioinformatics/btp382', '19561019', 'DNA cytosine methylation is an important epigenetic regulator, critical for mammalian development and the control of gene expression. Numerous techniques using either restriction enzyme or affinity-based approaches have been developed to interrogate cytosine methylation status genome-wide, however these assays must be validated by a more quantitative approach, such as MALDI-TOF mass spectrometry of bisulphite-converted DNA (commercialized as Sequenoms EpiTYPER assay using the MassArray system). Here, we present an R package (MassArray) that assists in assay design and uses the standard Sequenom output file as the input to a pipeline of analyses not available as part of the commercial software. The tools in this package include bisulphite conversion efficiency calculation, sequence polymorphism flagging and visualization tools that combine multiple experimental replicates and create tracks for genome browser viewing.'),
(74931, 2009, 'bio', 'Error control variability in pathway-based microarray analysis', 'Gold, David L|Miecznikowski, Jeffrey C|Liu, Song', 'Department of Biostatistics, Roswell Park Cancer, Buffalo, NY, USA. dlgold@buffalo.edu|Department of Biostatistics, Roswell Park Cancer, Buffalo, NY, USA. dlgold@buffalo.edu|Department of Biostatistics, Roswell Park Cancer, Buffalo, NY, USA. dlgold@buffalo.edu', 'United States|United States|United States', 0, 'Computer Simulation|Down Syndrome|Female|Gene Expression Regulation, Neoplastic|Humans|Leukemia, Megakaryoblastic, Acute|Metabolic Networks and Pathways|Oligonucleotide Array Sequence Analysis|Research Design|Smoking|Uterine Cervical Neoplasms', 'Journal Article', '10.1093/bioinformatics/btp385', '19561020', 'The decision to commit some or many false positives in practice rests with the investigator. Unfortunately, not all error control procedures perform the same. Our problem is to choose an error control procedure to determine a P-value threshold for identifying differentially expressed pathways in high-throughput gene expression studies. Pathway analysis involves fewer tests than differential gene expression analysis, on the order of a few hundred. We discuss and compare methods for error control for pathway analysis with gene expression data.'),
(74932, 2009, 'bio', 'A genetic programming approach for Burkholderia pseudomallei diagnostic pattern discovery', 'Yang, Zheng Rong|Lertmemongkolchai, Ganjana|Tan, Gladys|Felgner, Philip L|Titball, Richard', 'School of Biosciences, Hatherly building, Exeter EX4 4PS, University of Exeter, UK. z.r.yang@ex.ac.uk|School of Biosciences, Hatherly building, Exeter EX4 4PS, University of Exeter, UK. z.r.yang@ex.ac.uk|School of Biosciences, Hatherly building, Exeter EX4 4PS, University of Exeter, UK. z.r.yang@ex.ac.uk|School of Biosciences, Hatherly building, Exeter EX4 4PS, University of Exeter, UK. z.r.yang@ex.ac.uk|School of Biosciences, Hatherly building, Exeter EX4 4PS, University of Exeter, UK. z.r.yang@ex.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Antigens, Bacterial|Burkholderia Infections|Burkholderia pseudomallei|Computational Biology|Evolution, Molecular|Humans|Models, Genetic|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btp390', '19561021', 'Finding diagnostic patterns for fighting diseases like Burkholderia pseudomallei using biomarkers involves two key issues. First, exhausting all subsets of testable biomarkers (antigens in this context) to find a best one is computationally infeasible. Therefore, a proper optimization approach like evolutionary computation should be investigated. Second, a properly selected function of the antigens as the diagnostic pattern which is commonly unknown is a key to the diagnostic accuracy and the diagnostic effectiveness in clinical use.'),
(74933, 2009, 'bio', 'Comparative study on ChIP-seq data: normalization and binding pattern characterization', 'Taslim, Cenny|Wu, Jiejun|Yan, Pearlly|Singer, Greg|Parvin, Jeffrey|Huang, Tim|Lin, Shili|Huang, Kun', 'Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu|Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 42, 'Binding Sites|Breast Neoplasms|Cell Line, Tumor|Chromatin Immunoprecipitation|Computational Biology|Female|Humans|RNA Polymerase II|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btp384', '19561022', 'Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.'),
(74934, 2009, 'bio', 'ITM Probe: analyzing information flow in protein networks', 'Stojmirovixc4x87, Aleksandar|Yu, Yi-Kuo', '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', 21, 'Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp398', '19561335', 'Founded upon diffusion with damping, ITM Probe is an application for modeling information flow in protein interaction networks without prior restriction to the sub-network of interest. Given a context consisting of desired origins and destinations of information, ITM Probe returns the set of most relevant proteins with weights and a graphical representation of the corresponding sub-network. With a click, the user may send the resulting protein list for enrichment analysis to facilitate hypothesis formation or confirmation.'),
(74935, 2009, 'bio', 'IMG ER: a system for microbial genome annotation expert review and curation', 'Markowitz, Victor M|Mavromatis, Konstantinos|Ivanova, Natalia N|Chen, I-Min A|Chu, Ken|Kyrpides, Nikos C', 'Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. vmmarkowitz@lbl.gov', 'United States|United States|United States|United States|United States|United States', 0, 'Bacterial Proteins|Computational Biology|Database Management Systems|Enzymes|Genes, Bacterial|Genome, Bacterial', 'Journal Article', '10.1093/bioinformatics/btp393', '19561336', 'A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct.'),
(74936, 2009, 'bio', 'HAPLOWSER: a whole-genome haplotype browser for personal genome and metagenome', 'Kim, Jong Hyun|Kim, Woo-Cheol|Waterman, Michael S|Park, Sanghyun|Li, Lei M', 'Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States', 2, 'Computational Biology|Databases, Genetic|Genome|Genomics|Haplotypes|Internet|Metagenome|Software', 'Journal Article', '10.1093/bioinformatics/btp399', '19561337', 'Haplotype assembly is becoming a very important tool in genome sequencing of human and other organisms. Although haplotypes were previously inferred from genome assemblies, there has never been a comparative haplotype browser that depicts a global picture of whole-genome alignments among haplotypes of different organisms. We introduce a whole-genome HAPLotype brOWSER (HAPLOWSER), providing evolutionary perspectives from multiple aligned haplotypes and functional annotations. Haplowser enables the comparison of haplotypes from metagenomes, and associates conserved regions or the bases at the conserved regions with functional annotations and custom tracks. The associations are quantified for further analysis and presented as pie charts. Functional annotations and custom tracks that are projected onto haplotypes are saved as multiple files in FASTA format. Haplowser provides a user-friendly interface, and can display alignments of haplotypes with functional annotations at any resolution.'),
(74937, 2009, 'bio', 'Biophysical annotation and representation of CellML models', 'Wimalaratne, S M|Halstead, M D B|Lloyd, C M|Crampin, E J|Nielsen, P F', 'Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand', 10, 'Biophysical Phenomena|Computational Biology|Internet|Models, Biological|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btp391', '19564239', 'CellML is an implementation-independent model description language for specifying and exchanging biological processes. The focus of CellML is the representation of mathematical formulations of biological processes. The language captures the mathematical and model building constructs well, but does not lend itself to capturing the biology these models represent.'),
(74938, 2009, 'bio', 'Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources', 'Dai, Xiaofeng|Yli-Harja, Olli|Ribeiro, Andre S', 'Department of Signal Processing, Tampere University of Technology, Finland. xiaofeng.dai@tut.fi|Department of Signal Processing, Tampere University of Technology, Finland. xiaofeng.dai@tut.fi|Department of Signal Processing, Tampere University of Technology, Finland. xiaofeng.dai@tut.fi', 'Finland|Finland|Finland', 7, 'Algorithms|Bacillus subtilis|Gene Regulatory Networks|Proteins|RNA', 'Journal Article', '10.1093/bioinformatics/btp411', '19570801', 'Gene regulatory networks (GRNs) are stochastic, thus, do not have attractors, but can remain in confined regions of the state space, i.e. the noisy attractors, which define the cell type and phenotype.'),
(74939, 2009, 'bio', 'Robustness considerations in selecting efficient two-color microarray designs', 'Latif, A H M Mahbub|Bretz, Frank|Brunner, Edgar', 'Institute of Statistical Research and Training, University of Dhaka, Dhaka 1000, Bangladesh. mlatif@isrt.ac.bd|Institute of Statistical Research and Training, University of Dhaka, Dhaka 1000, Bangladesh. mlatif@isrt.ac.bd|Institute of Statistical Research and Training, University of Dhaka, Dhaka 1000, Bangladesh. mlatif@isrt.ac.bd', 'Bangladesh|Bangladesh|Bangladesh', 0, 'Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btp407', '19570802', 'The main goal of microarray experiments is to select a small subset of genes that are differentially expressed among competing mRNA samples. For a given set of such mRNA samples, it is possible to consider a number of two-color cDNA microarray designs with a fixed number of arrays. Appropriate criteria can be used to select an efficient design from such a set of alternative experimental designs. In practice, however, microarray expression data often contain missing observations and the most efficient design (with complete observations) for a specific setup may not be efficient in the presence of missing observations. In this article, we propose two criteria to address the robustness of microarray designs against missing observations. We demonstrate the simultaneous use of efficiency and robustness criteria to select good microarray designs for both one-factor and multi-factor experiments.'),
(74940, 2009, 'bio', 'High-throughput minor histocompatibility antigen prediction', 'DeLuca, David S|Eiz-Vesper, Britta|Ladas, Nektarios|Khattab, Barbara Anna-Maria|Blasczyk, Rainer', 'Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany.|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany.|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany.|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany.|Institute for Transfusion Medicine, Hannover Medical School, Hannover, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Databases, Protein|Minor Histocompatibility Antigens|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp404', '19570803', 'Minor histocompatibility antigens (mHags) are a diverse collection of MHC-bound peptides that have immunological implications in the context of allogeneic transplantation because of their differential presence in donor and host, and thus play a critical role in the induction of the detrimental graft-versus-host disease (GvHD) or in the development of the beneficial graft-versus-leukemia (GvL) effect. Therefore, the search for mHags has implications not only for preventing GvHD, but also for therapeutic applications involving leukemia-specific T cells. We have created a web-based system, named PeptideCheck, which aims to augment the experimental discovery of mHags using bioinformatic means. Analyzing peptide elution data to search for mHags and predicting mHags from polymorphism and protein databases are the core features.'),
(74941, 2009, 'bio', 'Genome-wide maps of mono- and di-nucleosomes of Aspergillus fumigatus', 'Nishida, Hiromi|Motoyama, Takayuki|Yamamoto, Shogo|Aburatani, Hiroyuki|Osada, Hiroyuki', 'Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan. hnishida@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan. hnishida@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan. hnishida@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan. hnishida@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan. hnishida@iu.a.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 10, 'Aspergillus fumigatus|Base Sequence|DNA, Fungal|Genome, Fungal|Nucleosomes', 'Journal Article', '10.1093/bioinformatics/btp413', '19570804', 'We identified 5 975 567 mono- and 6 995 122 di-nucleosome positions of the fungus Aspergillus fumigatus, which was detected at high resolution based on the DNA sequence data obtained from both mono- and di-nucleosomal DNA fragments. We show that the distribution of lengths of the mono-nucleosomal DNA fragments has two peaks at 135 and 150 nt, whereas the distribution of di-nucleosomal DNA fragment lengths has a single peak at 285 nt. Although the gene bodies of the active and inactive genes and the inactive gene promoters had the two peaks of the mono-nucleosomal DNA fragment lengths, the active gene promoter lost the longer peak at 150 nt. Our findings strongly suggest that the nucleosomes protecting longer DNA fragments against MNase at the promoters, thereby inhibiting high gene expression.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74942, 2009, 'bio', 'Affy exon tissues: exon levels in normal tissues in human, mouse and rat', 'Pohl, Andrew A|Sugnet, Charles W|Clark, Tyson A|Smith, Kayla|Fujita, Pauline A|Cline, Melissa S', 'Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Genetic|Exons|Gene Expression Profiling|Genome-Wide Association Study|Humans|Mice|Oligonucleotide Array Sequence Analysis|RNA|Rats|Software', 'Journal Article', '10.1093/bioinformatics/btp414', '19570805', 'Most genes in human, mouse and rat produce more than one transcript isoform. The Affymetrix Exon Array is a tool for studying the many processes that regulate RNA production, with separate probesets measuring RNA levels at known and putative exons. For insights on how exons levels vary between normal tissues, we constructed the Affy Exon Tissues track from tissue data published by Affymetrix. This track reports exon probeset intensities as log ratios relative to median values across the dataset and renders them as colored heat maps, to yield quick visual identification of exons with intensities that vary between normal tissues.'),
(74943, 2009, 'bio', 'k-link EST clustering: evaluating error introduced by chimeric sequences under different degrees of linkage', 'Bragg, Lauren M|Stone, Glenn', 'CSIRO Mathematical and Information Sciences, North Ryde, NSW, Australia. lauren.bragg@csiro.au|CSIRO Mathematical and Information Sciences, North Ryde, NSW, Australia. lauren.bragg@csiro.au', 'Australia|Australia', 3, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Expressed Sequence Tags|Software', 'Journal Article', '10.1093/bioinformatics/btp410', '19570806', 'The clustering of expressed sequence tags (ESTs) is a crucial step in many sequence analysis studies that require a high level of redundancy. Chimeric sequences, while uncommon, can make achieving the optimal EST clustering a challenge. Single-linkage algorithms are particularly vulnerable to the effects of chimeras. To avoid chimera-facilitated erroneous merges, researchers using single-linkage algorithms are forced to use stringent sequence-similarity thresholds. Such thresholds reduce the sensitivity of the clustering algorithm.'),
(74944, 2009, 'bio', 'HI: haplotype improver using paired-end short reads', 'Long, Quan|MacArthur, Daniel|Ning, Zemin|Tyler-Smith, Chris', 'The Wellcome Trust Sanger Institute, Hinxton, Cambs, UK. ql2@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambs, UK. ql2@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambs, UK. ql2@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambs, UK. ql2@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Databases, Genetic|Haplotypes|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp412', '19570807', 'We present a program to improve haplotype reconstruction by incorporating information from paired-end reads, and demonstrate its utility on simulated data. We find that given a fixed coverage, longer reads (implying fewer of them) are preferable.'),
(74945, 2009, 'bio', 'SNP-o-matic', 'Manske, Heinrich Magnus|Kwiatkowski, Dominic P', 'Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. mm6@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. mm6@sanger.ac.uk', 'United Kingdom|United Kingdom', 18, 'Algorithms|Computational Biology|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp403', '19574284', 'High throughput sequencing technologies generate large amounts of short reads. Mapping these to a reference sequence consumes large amounts of processing time and memory, and read mapping errors can lead to noisy or incorrect alignments. SNP-o-matic is a fast, memory-efficient and stringent read mapping tool offering a variety of analytical output functions, with an emphasis on genotyping.'),
(74946, 2009, 'bio', 'Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets', 'Glazko, Galina V|Emmert-Streib, Frank', 'Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA. Galina_Glazko@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA. Galina_Glazko@urmc.rochester.edu', 'United States|United States', 80, 'Computational Biology|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1093/bioinformatics/btp406', '19574285', 'Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses.'),
(74947, 2009, 'bio', 'TTA Lynx: a web-based service for analysis of actinomycete genes containing rare TTA codon', 'Zaburannyy, Nestor|Ostash, Bohdan|Fedorenko, Victor', 'Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Lviv 79005, Ukraine. ttas@franko.lviv.ua|Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Lviv 79005, Ukraine. ttas@franko.lviv.ua|Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Lviv 79005, Ukraine. ttas@franko.lviv.ua', 'Ukraine|Ukraine|Ukraine', 0, 'Actinobacteria|Base Sequence|Codon|Computational Biology|Genome, Bacterial|Internet|Molecular Sequence Data|Software|Streptomyces', 'Journal Article', '10.1093/bioinformatics/btp402', '19574286', 'TTA Lynx is a web-based service for finding, assessing and comparing coding sequences that contain TTA codons. This codon is most notable for being a regulatory switch that governs different aspects of the physiology of several GC-rich, Gram-positive bacteria belonging to genus Streptomyces, prolific producers of clinically important natural products. The ever-increasing pace of genome sequencing is creating a huge volume of data that could be utilized to improve our understanding of rare codons in actinomycete biology (and other biological systems.) The service described here is designed to facilitate analysis of TTA-containing genes and to assess the importance of TTA-mediated regulation in an organism of interest. This service and its database of organisms with well-known or hypothetical TTA-based regulation provides an opportunity for the identification of such regulation on a genome-wide scale.'),
(74948, 2009, 'bio', 'Statistical lower bounds on protein copy number from fluorescence expression images', 'Zamparo, Lee|Perkins, Theodore J', 'Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada.|Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada.', 'Canada|Canada', 0, 'Animals|Computational Biology|Drosophila melanogaster|Fluorescence|Gene Expression Profiling|Models, Statistical|Proteins|RNA, Messenger|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp415', '19574287', 'Fluorescence imaging has become a commonplace for quantitatively measuring mRNA or protein expression in cells and tissues. However, such expression data are usually relative-absolute concentrations or molecular copy numbers are typically not known. While this is satisfactory for many applications, for certain kinds of quantitative network modeling and analysis of expression noise, absolute measures of expression are necessary.'),
(74949, 2009, 'bio', 'On the inference of spatial structure from population genetics data', 'Guillot, Gilles', 'Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo Norway. gilles.guillot@bio.uio.no', 'Norway', 35, 'Algorithms|Arabidopsis|Genetics, Population|Linkage Disequilibrium|Markov Chains', 'Journal Article', '10.1093/bioinformatics/btp267', '19574291', 'In a series of recent papers, Tess, a computer program based on the concept of hidden Markov random field, has been proposed to infer the number and locations of panmictic population units from the genotypes and spatial locations of these individuals. The method seems to be of broad appeal as it is conceptually much simpler than other competing methods and it has been reported by its authors to be fast and accurate. However, this methodology is not grounded in a formal statistical inference method and seems to rely to a large extent on arbitrary choices regarding the parameters used. The present article is an investigation of the accuracy of this method and an attempt to assess whether recent results reported on the basis of this method are genuine features of the genetic process or artefacts of the method.'),
(74950, 2009, 'bio', 'Comment on On the inference of spatial structure from population genetics data', 'Durand, Eric|Chen, Chibiao|François, Olivier', 'Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo Norway. gilles.guillot@bio.uio.no|Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo Norway. gilles.guillot@bio.uio.no|Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo Norway. gilles.guillot@bio.uio.no', 'Norway|Norway|Norway', 8, 'Genetics, Population|Models, Genetic', 'Comment', '10.1093/bioinformatics/btp337', '19574292', '-'),
(74951, 2009, 'bio', 'Efficient stochastic simulation of reaction-diffusion processes via direct compilation', 'Lis, Mieszko|Artyomov, Maxim N|Devadas, Srinivas|Chakraborty, Arup K', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mieszko@csail.mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mieszko@csail.mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mieszko@csail.mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mieszko@csail.mit.edu', 'United States|United States|United States|United States', 28, 'Algorithms|Computer Simulation|Diffusion|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btp387', '19578038', 'We present the Stochastic Simulator Compiler (SSC), a tool for exact stochastic simulations of well-mixed and spatially heterogeneous systems. SSC is the first tool to allow a readable high-level description with spatially heterogeneous simulation algorithms and complex geometries, this permits large systems to be expressed concisely. Meanwhile, direct native-code compilation allows SSC to generate very fast simulations.'),
(74952, 2009, 'bio', 'Antimony: a modular model definition language', 'Smith, Lucian P|Bergmann, Frank T|Chandran, Deepak|Sauro, Herbert M', 'Department of Bioengineering, University of Washington, Seattle, WA, USA. lpsmith@u.washington.edu|Department of Bioengineering, University of Washington, Seattle, WA, USA. lpsmith@u.washington.edu|Department of Bioengineering, University of Washington, Seattle, WA, USA. lpsmith@u.washington.edu|Department of Bioengineering, University of Washington, Seattle, WA, USA. lpsmith@u.washington.edu', 'United States|United States|United States|United States', 0, 'Databases, Factual|Information Storage and Retrieval|Internet|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp401', '19578039', 'Model exchange in systems and synthetic biology has been standardized for computers with the Systems Biology Markup Language (SBML) and CellML, but specialized software is needed for the generation of models in these formats. Text-based model definition languages allow researchers to create models simply, and then export them to a common exchange format. Modular languages allow researchers to create and combine complex models more easily. We saw a use for a modular text-based language, together with a translation library to allow other programs to read the models as well.'),
(74953, 2009, 'bio', 'TreeTime: an extensible C++ software package for Bayesian phylogeny reconstruction with time-calibration', 'Himmelmann, Lin|Metzler, Dirk', 'Department for Computer Science and Mathematics, Goethe-University, Frankfurt am Main, Germany. lin@linhi.de|Department for Computer Science and Mathematics, Goethe-University, Frankfurt am Main, Germany. lin@linhi.de', 'Germany|Germany', 0, 'Bayes Theorem|Computational Biology|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btp417', '19578040', 'For the estimation of phylogenetic trees from molecular data, it is worthwhile to take prior paleontologic knowledge into account, if available. To calibrate the branch lengths of the tree with times assigned to geo-historical events or fossils, it is necessary to select a relaxed molecular clock model to specify how mutation rates can change along the phylogeny.'),
(74954, 2009, 'bio', 'FBA-SimVis: interactive visualization of constraint-based metabolic models', 'Grafahrend-Belau, Eva|Klukas, Christian|Junker, Björn H|Schreiber, Falk', 'Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben and Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Germany. grafahr@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben and Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Germany. grafahr@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben and Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Germany. grafahr@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben and Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Germany. grafahr@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany', 34, 'Computational Biology|Information Storage and Retrieval|Metabolic Networks and Pathways|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp408', '19578041', 'FBA-SimVis is a VANTED plug-in for the constraint-based analysis of metabolic models with special focus on the visual exploration of metabolic flux data resulting from model analysis. The program provides a user-friendly environment for model reconstruction, constraint-based model analysis, and interactive visualization of the simulation results. With the ability to quantitatively analyse metabolic fluxes in an interactive and visual manner, FBA-SimVis supports a comprehensive understanding of constraint-based metabolic flux models in both overview and detail.'),
(74955, 2009, 'bio', 'DYNAMIK: a software environment for cell DYNAmics, Motility, and Information tracKing, with an application to Ras pathways', 'Jaeger, Stefan|Song, Qingfeng|Chen, Su-Shing', 'Partner Institute of Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Max-Planck Society, Shanghai 200031, China. jaeger@picb.ac.cn|Partner Institute of Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Max-Planck Society, Shanghai 200031, China. jaeger@picb.ac.cn|Partner Institute of Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Max-Planck Society, Shanghai 200031, China. jaeger@picb.ac.cn', 'China|China|China', 7, 'Animals|Cell Movement|Computational Biology|Dictyostelium|Image Processing, Computer-Assisted|Signal Transduction|Software|User-Computer Interface|ras Proteins', 'Journal Article', '10.1093/bioinformatics/btp405', '19578170', 'The emergence of new microscopy techniques in combination with the increasing resource of bioimaging data has given fresh impetus to utilizing image processing methods for studying biological processes. Cell tracking studies in particular, which are important for a wide range of biological processes such as embryonic development or the immune system, have recently become the focus of attention. These studies typically produce large volumes of data that are hard to investigate manually and therefore call for an automated approach. Due to the large variety of biological cells and the inhomogeneity of applications, however, there exists no widely accepted method or system for cell tracking until today. In this article, we present our publicly available DYNAMIK software environment that allows users to compute a suit of cell features and plot the trajectory of multiple cells over a sequence of frames. Using chemotaxis and Ras pathways as an example, we show how users can employ our software to compute statistics about cell motility and other cell information, and how to evaluate their test series based on the data computed. We see that DYNAMIKs segmentation and tracking compares favorably with the output produced by other software packages.'),
(74956, 2009, 'bio', 'Comments on sequence normalization of tiling array expression', 'Gilbert, Don|Rechtsteiner, Andreas', 'Department of Biology, Indiana University, Bloomingto, IN, USA. gilbertd@indiana.edu|Department of Biology, Indiana University, Bloomingto, IN, USA. gilbertd@indiana.edu', 'India|India', 0, 'Animals|Base Composition|Base Sequence|Daphnia|Drosophila melanogaster|Exons|Introns|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp389', '19578171', 'Methods to improve tiling array expression signals are needed to accurately detect genome features. Royce et al. provide statistical normalizations of tile signal based on probe sequence content that promises improved accuracy, and should be independently verified.'),
(74957, 2009, 'bio', 'Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model', 'Wang, Zhihui|Birch, Christina M|Sagotsky, Jonathan|Deisboeck, Thomas S', 'Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.|Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.|Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.|Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.', 'United States|United States|United States|United States', 45, 'Computational Biology|Epidermal Growth Factor|Lung Neoplasms|Signal Transduction|Transforming Growth Factor beta', 'Evaluation Studies', '10.1093/bioinformatics/btp416', '19578172', 'We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor beta (TGFbeta) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFbeta concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFbeta fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFbeta signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFbeta concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFbeta profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFbeta do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.'),
(74958, 2009, 'bio', 'FRED--a framework for T-cell epitope detection', 'Feldhahn, Magdalena|Dönnes, Pierre|Thiel, Philipp|Kohlbacher, Oliver', 'Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany. feldhahn@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany. feldhahn@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany. feldhahn@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany. feldhahn@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany', 15, 'Binding Sites|Computational Biology|Databases, Protein|Epitopes, T-Lymphocyte|Software', 'Journal Article', '10.1093/bioinformatics/btp409', '19578173', 'Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.'),
(74959, 2009, 'bio', 'Evidence for human microRNA-offset RNAs in small RNA sequencing data', 'Langenberger, David|Bermudez-Santana, Clara|Hertel, Jana|Hoffmann, Steve|Khaitovich, Philipp|Stadler, Peter F', 'Department of Computer Science, University of Leipzig, Leipzig, Germany.|Department of Computer Science, University of Leipzig, Leipzig, Germany.|Department of Computer Science, University of Leipzig, Leipzig, Germany.|Department of Computer Science, University of Leipzig, Leipzig, Germany.|Department of Computer Science, University of Leipzig, Leipzig, Germany.|Department of Computer Science, University of Leipzig, Leipzig, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 95, 'Humans|MicroRNAs|RNA|RNA, Small Interfering|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp419', '19584066', 'MicroRNA-offset-RNAs (moRNAs) were recently detected as highly abundant class of small RNAs in a basal chordate. Using short read sequencing data, we show here that moRNAs are also produced from human microRNA precursors, albeit at quite low expression levels. The expression levels of moRNAs are unrelated to those of the associated microRNAs. Surprisingly, microRNA precursors that also show moRNAs are typically evolutionarily old, comprising more than half of the microRNA families that were present in early Bilateria, while evidence for moRNAs was found only for a relative small fraction of microRNA families of recent origin.'),
(74960, 2009, 'bio', 'Identification of distant family relationships', 'Skare, Øivind|Sheehan, Nuala|Egeland, Thore', 'Norwegian Institute of Public Health, 0403 Oslo, Norway.|Norwegian Institute of Public Health, 0403 Oslo, Norway.|Norwegian Institute of Public Health, 0403 Oslo, Norway.', 'Norway|Norway|Norway', 35, 'Algorithms|Computational Biology|DNA|Likelihood Functions|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp418', '19584067', 'Family relationships can be estimated from DNA marker data. Applications arise in a large number of areas including evolution and conservation research, genealogical research in human, plant and animal populations, forensic problems and genetic mapping via linkage and association analyses. Traditionally, likelihood-based approaches to relationship estimation have used unlinked genetic markers. Due to the fact that some relationships cannot be distinguished from data at unlinked markers, and given the limited number of such markers available, there are considerable constraints on the type of identification problem that can be satisfactorily addressed with such approaches. The aim of this article is to explore the potential of linked autosomal single nucleotide polymorphism markers in this context. Throughout, we will view the problem of relationship estimation as one of pedigree identification rather than identity-by-descent, and thus focus on applications where determination of the exact relationship is important.'),
(74961, 2009, 'bio', 'ViTraM: visualization of transcriptional modules', 'Sun, Hong|Lemmens, Karen|Van den Bulcke, Tim|Engelen, Kristof|De Moor, Bart|Marchal, Kathleen', 'Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.|Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.|Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.|Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.|Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.|Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 2, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Sequence Analysis, DNA|Software|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp400', '19587224', 'We developed ViTraM, a tool that allows visualizing overlapping transcriptional modules in an intuitive way. By visualizing not only the genes and the experiments in which the genes are co-expressed, but also additional properties of the modules such as the regulators and regulatory motifs that are responsible for the observed co-expression, ViTraM can assist in the biological analysis and interpretation of the output of module detection tools.'),
(74962, 2009, 'bio', 'TileProbe: modeling tiling array probe effects using publicly available data', 'Judy, Jennifer Toolan|Ji, Hongkai', 'Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.', 'United States|United States', 14, 'Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp425', '19592393', 'Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals.'),
(74963, 2009, 'bio', 'Prediction of protein beta-residue contacts by Markov logic networks with grounding-specific weights', 'Lippi, Marco|Frasconi, Paolo', 'Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Firenze, Italy. lippi@dsi.unifi.it|Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Firenze, Italy. lippi@dsi.unifi.it', 'Italy|Italy', 35, 'Computational Biology|Databases, Protein|Markov Chains|Neural Networks (Computer)|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btp421', '19592394', 'Accurate prediction of contacts between beta-strand residues can significantly contribute towards ab initio prediction of the 3D structure of many proteins. Contacts in the same protein are highly interdependent. Therefore, significant improvements can be expected by applying statistical relational learners that overcome the usual machine learning assumption that examples are independent and identically distributed. Furthermore, the dependencies among beta-residue contacts are subject to strong regularities, many of which are known a priori. In this article, we take advantage of Markov logic, a statistical relational learning framework that is able to capture dependencies between contacts, and constrain the solution according to domain knowledge expressed by means of weighted rules in a logical language.'),
(74964, 2009, 'bio', 'aGEM: an integrative system for analyzing spatial-temporal gene-expression information', 'Jiménez-Lozano, Natalia|Segura, Joan|Macías, José Ramón|Vega, Juanjo|Carazo, José María', 'GN7 of the National Institute for Bioinformatics and Biocomputing Unit of the National Centre for Biotechnology, Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain. natalia@cnb.csic.es|GN7 of the National Institute for Bioinformatics and Biocomputing Unit of the National Centre for Biotechnology, Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain. natalia@cnb.csic.es|GN7 of the National Institute for Bioinformatics and Biocomputing Unit of the National Centre for Biotechnology, Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain. natalia@cnb.csic.es|GN7 of the National Institute for Bioinformatics and Biocomputing Unit of the National Centre for Biotechnology, Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain. natalia@cnb.csic.es|GN7 of the National Institute for Bioinformatics and Biocomputing Unit of the National Centre for Biotechnology, Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain. natalia@cnb.csic.es', 'Spain|Spain|Spain|Spain|Spain', 3, 'Computational Biology|Gene Expression|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp422', '19592395', 'The work presented here describes the anatomical Gene-Expression Mapping (aGEM) Platform, a development conceived to integrate phenotypic information with the spatial and temporal distributions of genes expressed in the mouse. The aGEM Platform has been built by extending the Distributed Annotation System (DAS) protocol, which was originally designed to share genome annotations over the WWW. DAS is a client-server system in which a single client integrates information from multiple distributed servers.'),
(74965, 2009, 'bio', 'Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition', 'Karpievitch, Yuliya V|Taverner, Thomas|Adkins, Joshua N|Callister, Stephen J|Anderson, Gordon A|Smith, Richard D|Dabney, Alan R', 'Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu|Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu', 'United States|United States|United States|United States|United States|United States|United States', 51, 'Computational Biology|Databases, Protein|Mass Spectrometry|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp426', '19602524', 'LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference. Careful normalization of MS peak intensities would enable greater accuracy and precision in quantitative comparisons of protein abundance levels.'),
(74966, 2009, 'bio', 'PIQA: pipeline for Illumina G1 genome analyzer data quality assessment', 'Martínez-Alcántara, A|Ballesteros, E|Feng, C|Rojas, M|Koshinsky, H|Fofanov, V Y|Havlak, P|Fofanov, Y', 'Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.|Department of Computer Science, University of Houston, Houston, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 33, 'Computational Biology|Genome|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp429', '19602525', 'PIQA is a quality analysis pipeline designed to examine genomic reads produced by Next Generation Sequencing technology (Illumina G1 Genome Analyzer). A short statistical summary, as well as tile-by-tile and cycle-by-cycle graphical representation of clusters density, quality scores and nucleotide frequencies allow easy identification of various technical problems including defective tiles, mistakes in sample/library preparations and abnormalities in the frequencies of appearance of sequenced genomic reads. PIQA is written in the R statistical programming language and is compatible with bustard, fastq and scarf Illumina G1 Genome Analyzer data formats.'),
(74967, 2009, 'bio', 'WebArrayDB: cross-platform microarray data analysis and public data repository', 'Xia, Xiao-Qin|McClelland, Michael|Porwollik, Steffen|Song, Wenzhi|Cong, Xianling|Wang, Yipeng', 'Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute San Diego, San Diego, CA 92121, USA. xqxia70@gmail.com', 'United States|United States|United States|United States|United States|United States', 36, 'Computational Biology|Information Storage and Retrieval|Internet|Microarray Analysis|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp430', '19602526', 'Cross-platform microarray analysis is an increasingly important research tool, but researchers still lack open source tools for storing, integrating and analyzing large amounts of microarray data obtained from different array platforms.'),
(74968, 2009, 'bio', 'A multi-dimensional evidence-based candidate gene prioritization approach for complex diseases-schizophrenia as a case', 'Sun, Jingchun|Jia, Peilin|Fanous, Ayman H|Webb, Bradley T|van den Oord, Edwin J C G|Chen, Xiangning|Bukszar, Jozsef|Kendler, Kenneth S|Zhao, Zhongming', 'Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.|Department of Biomedical Informatics and Department of Psychiatry, Vanderbilt University, Nashville, TN 37203, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 63, 'Computational Biology|Databases, Genetic|Genetic Linkage|Genome-Wide Association Study|Schizophrenia', 'Journal Article', '10.1093/bioinformatics/btp428', '19602527', 'During the past decade, we have seen an exponential growth of vast amounts of genetic data generated for complex disease studies. Currently, across a variety of complex biological problems, there is a strong trend towards the integration of data from multiple sources. So far, candidate gene prioritization approaches have been designed for specific purposes, by utilizing only some of the available sources of genetic studies, or by using a simple weight scheme. Specifically to psychiatric disorders, there has been no prioritization approach that fully utilizes all major sources of experimental data.'),
(74969, 2009, 'bio', 'Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach', 'Urban, Jan|Vanek, Jan|Soukup, Jirí|Stys, Dalibor', 'Institute of Physical Biology, University of South Bohemia, Academic and University Center, Zámek 136, 373 33 Nové Hrady, Czech Republic. urban@greentech.cz|Institute of Physical Biology, University of South Bohemia, Academic and University Center, Zámek 136, 373 33 Nové Hrady, Czech Republic. urban@greentech.cz|Institute of Physical Biology, University of South Bohemia, Academic and University Center, Zámek 136, 373 33 Nové Hrady, Czech Republic. urban@greentech.cz|Institute of Physical Biology, University of South Bohemia, Academic and University Center, Zámek 136, 373 33 Nové Hrady, Czech Republic. urban@greentech.cz', '-|-|-|-', 12, 'Chromatography, Liquid|Computational Biology|Databases, Protein|Mass Spectrometry|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp427', '19602528', 'Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment, its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds.'),
(74970, 2009, 'bio', 'ADAN: a database for prediction of protein-protein interaction of modular domains mediated by linear motifs', 'Encinar, J A|Fernandez-Ballester, G|Sánchez, I E|Hurtado-Gomez, E|Stricher, F|Beltrao, P|Serrano, L', 'Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.|Instituto de Biologia Molecular y Celular, Edificio Torregaitan, Universidad Miguel Hernandez, Elche, Alicante, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 23, 'Algorithms|Amino Acid Motifs|Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp424', '19602529', 'Most of the structures and functions of proteome globular domains are yet unknown. We can use high-resolution structures from different modular domains in combination with automatic protein design algorithms to predict genome-wide potential interactions of a protein. ADAN database and related web tools are online resources for the predictive analysis of ligand-domain complexes. ADAN database is a collection of different modular protein domains (SH2, SH3, PDZ, WW, etc.). It contains 3505 entries with extensive structural and functional information available, manually integrated, curated and annotated with cross-references to other databases, biochemical and thermodynamical data, simplified coordinate files, sequence files and alignments. Prediadan, a subset of ADAN database, offers position-specific scoring matrices for protein-protein interactions, calculated by FoldX, and predictions of optimum ligands and putative binding partners. Users can also scan a query sequence against selected matrices, or improve a ligand-domain interaction.'),
(74971, 2009, 'bio', 'DiProGB: the dinucleotide properties genome browser', 'Friedel, Maik|Nikolajewa, Swetlana|Sühnel, Jürgen|Wilhelm, Thomas', 'Biocomputing Group, Leibniz Institute for Age Research-Fritz Lipmann Institute, Jena Centre for Bioinformatics, Beutenbergstr. 11, Jena, Germany. maikfr@fli-leibniz.de|Biocomputing Group, Leibniz Institute for Age Research-Fritz Lipmann Institute, Jena Centre for Bioinformatics, Beutenbergstr. 11, Jena, Germany. maikfr@fli-leibniz.de|Biocomputing Group, Leibniz Institute for Age Research-Fritz Lipmann Institute, Jena Centre for Bioinformatics, Beutenbergstr. 11, Jena, Germany. maikfr@fli-leibniz.de|Biocomputing Group, Leibniz Institute for Age Research-Fritz Lipmann Institute, Jena Centre for Bioinformatics, Beutenbergstr. 11, Jena, Germany. maikfr@fli-leibniz.de', 'Germany|Germany|Germany|Germany', 16, 'Base Sequence|Computational Biology|Databases, Genetic|Genome|Genomics|Internet|Nucleotides|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp436', '19605418', 'DiProGB is an easy to use new genome browser that encodes the primary nucleotide sequence by thermodynamical and geometrical dinucleotide properties. The nucleotide sequence is thus converted into a sequence graph. This visualization, supported by different graph manipulation options, facilitates genome analyses, because the human brain can process visual information better than textual information. Also, DiProGB can identify genomic regions where certain physical properties are more conserved than the nucleotide sequence itself. Most of the DiProGB tools can be applied to both, the primary nucleotide sequence and the sequence graph. They include motif and repeat searches as well as statistical analyses. DiProGB adds a new dimension to the common genome analysis approaches by taking into account the physical properties of DNA and RNA.'),
(74972, 2009, 'bio', 'Motif discovery and motif finding from genome-mapped DNase footprint data', 'Kulakovskiy, Ivan V|Favorov, Alexander V|Makeev, Vsevolod J', 'Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. ivan.kulakovskiy@gmail.com|Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. ivan.kulakovskiy@gmail.com|Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. ivan.kulakovskiy@gmail.com', '-|-|-', 29, 'Algorithms|Animals|Base Sequence|Chromosome Mapping|Computational Biology|Deoxyribonucleases|Drosophila|Molecular Sequence Data|Software', 'Journal Article', '10.1093/bioinformatics/btp434', '19605419', 'Footprint data is an important source of information on transcription factor recognition motifs. However, a footprinting fragment can contain no sequences similar to known protein recognition sites. Inspection of genome fragments nearby can help to identify missing site positions.'),
(74973, 2009, 'bio', 'Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite', 'Hashimoto, Takehiro|de Hoon, Michiel J L|Grimmond, Sean M|Daub, Carsten O|Hayashizaki, Yoshihide|Faulkner, Geoffrey J', 'Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.|Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.|Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.|Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.|Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.|Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 27, 'Computational Biology|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp438', '19605420', 'Multi-mapping sequence tags are a significant impediment to short-read sequencing platforms. These tags are routinely omitted from further analysis, leading to experimental bias and reduced coverage. Here, we present MuMRescueLite, a low-resource requirement version of the MuMRescue software that has been used by several next generation sequencing projects to probabilistically reincorporate multi-mapping tags into mapped short read data.'),
(74974, 2009, 'bio', 'Supervised prediction of drug-target interactions using bipartite local models', 'Bleakley, Kevin|Yamanishi, Yoshihiro', 'Mines ParisTech, Centre for Computational Biology, Fontainebleau, France. kevbleakley@gmail.com|Mines ParisTech, Centre for Computational Biology, Fontainebleau, France. kevbleakley@gmail.com', 'France|France', 0, 'Algorithms|Binding Sites|Computational Biology|Drug Discovery|Pharmaceutical Preparations|Proteins|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/btp433', '19605421', 'In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug-target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug-target interactions.');
