INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76075, 2011, 'bio', 'Human variation database: an open-source database template for genomic discovery', 'Fejes, Anthony P|Khodabakhshi, Alireza Hadj|Birol, Inanc|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada V5Z 4S6. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada V5Z 4S6. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada V5Z 4S6. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada V5Z 4S6. afejes@bcgsc.ca', 'Canada|Canada|Canada|Canada', 0, 'Databases, Nucleic Acid|Genetic Variation|Genome, Human|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btr100', '21367872', 'Current public variation databases are based upon collaboratively pooling data into a single database with a single interface available to the public. This gives little control to the collaborator to mine the database and requires that they freely share their data with the owners of the repository. We aim to provide an alternative mechanism: providing the source code and application programming interface (API) of a database, enabling researchers to set up local versions without investing heavily in the development of the resource and allowing for confidential information to remain secure.'),
(76076, 2011, 'bio', 'SurvJamda: an R package to predict patients survival and risk assessment using joint analysis of microarray gene expression data', 'Yasrebi, Haleh', 'Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology (EPFL), School of Life Sciences (SV) and Swiss Institute of Bioinformatics, EPFL SV ISREC, Station 15, 1015 Lausanne, Switzerland. hyasrebi@yahoo.com', 'Switzerland', 0, 'Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Prognosis|Risk Assessment|Software|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btr103', '21367873', 'SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets.'),
(76077, 2011, 'bio', 'Mixtures of common t-factor analyzers for clustering high-dimensional microarray data', 'Baek, Jangsun|McLachlan, Geoffrey J', 'Department of Statistics, Chonnam National University, Gwangju, South Korea. jbaek@jnu.ac.kr|Department of Statistics, Chonnam National University, Gwangju, South Korea. jbaek@jnu.ac.kr', '-|-', 54, 'Algorithms|Cluster Analysis|Factor Analysis, Statistical|Gene Expression Profiling|Humans|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btr112', '21372081', 'Mixtures of factor analyzers enable model-based clustering to be undertaken for high-dimensional microarray data, where the number of observations n is small relative to the number of genes p. Moreover, when the number of clusters is not small, for example, where there are several different types of cancer, there may be the need to reduce further the number of parameters in the specification of the component-covariance matrices. A further reduction can be achieved by using mixtures of factor analyzers with common component-factor loadings (MCFA), which is a more parsimonious model. However, this approach is sensitive to both non-normality and outliers, which are commonly observed in microarray experiments. This sensitivity of the MCFA approach is due to its being based on a mixture model in which the multivariate normal family of distributions is assumed for the component-error and factor distributions.'),
(76078, 2011, 'bio', 'ncIDP-assign: a SPARKY extension for the effective NMR assignment of intrinsically disordered proteins', 'Tamiola, Kamil|Mulder, Frans A A', 'Department of Biophysical Chemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands. k.tamiola@rug.nl|Department of Biophysical Chemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands. k.tamiola@rug.nl', 'Netherlands|Netherlands', 0, 'Algorithms|Cell Adhesion Molecules, Neuronal|Humans|Magnetic Resonance Imaging|Membrane Proteins|Nerve Tissue Proteins|Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btr054', '21372082', 'We describe here the ncIDP-assign extension for the popular NMR assignment program SPARKY, which aids in the sequence-specific resonance assignment of intrinsically disordered proteins (IDPs). The assignment plugin greatly facilitates the effective matching of a set of connected resonances to the correct position in the sequence by making use of IDP random coil chemical shifts.'),
(76079, 2011, 'bio', 'A probabilistic model of nuclear import of proteins', 'Mehdi, Ahmed M|Sehgal, Muhammad Shoaib B|Kobe, Bostjan|Bailey, Timothy L|Bodxc3xa9n, Mikael', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Active Transport, Cell Nucleus|Amino Acid Sequence|Animals|Bayes Theorem|Cell Nucleus|Mice|Models, Biological|Nuclear Localization Signals|Nuclear Proteins|Protein Interaction Mapping|Saccharomyces cerevisiae|Support Vector Machine|alpha Karyopherins', 'Journal Article', '10.1093/bioinformatics/btr121', '21372083', 'Nucleo-cytoplasmic trafficking of proteins is a core regulatory process that sustains the integrity of the nuclear space of eukaryotic cells via an interplay between numerous factors. Despite progress on experimentally characterizing a number of nuclear localization signals, their presence alone remains an unreliable indicator of actual translocation.'),
(76080, 2011, 'bio', 'SC²ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data', 'Olex, Amy L|Fetrow, Jacquelyn S', 'Department of Computer Science and Department of Physics, Wake Forest University, Winston-Salem, NC 27109, USA. olexal@wfu.edu|Department of Computer Science and Department of Physics, Wake Forest University, Winston-Salem, NC 27109, USA. olexal@wfu.edu', 'United States|United States', 8, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btr115', '21372084', 'Standard and Consensus Clustering Analysis Tool for Microarray Data (SC²ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format.'),
(76081, 2011, 'bio', 'Comparative visualization of genetic and physical maps with Strudel', 'Bayer, Micha|Milne, Iain|Stephen, Gordon|Shaw, Paul|Cardle, Linda|Wright, Frank|Marshall, David', 'Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk|Genetics Programme, Scottish Crop Research Institute and Biomathematics and Statistics Scotland, Invergowrie, Dundee, UK. micha.bayer@scri.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 25, 'Computational Biology|Computer Graphics|Genome|Genomics|Physical Chromosome Mapping|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btr111', '21372085', 'Data visualization can play a key role in comparative genomics, for example, underpinning the investigation of conserved synteny patterns. Strudel is a desktop application that allows users to easily compare both genetic and physical maps interactively and efficiently. It can handle large datasets from several genomes simultaneously, and allows all-by-all comparisons between these.'),
(76082, 2011, 'bio', 'Identifying discriminative classification-based motifs in biological sequences', 'Vens, Celine|Rosso, Marie-Noëlle|Danchin, Etienne G J', 'Katholieke Universiteit Leuven, Department of Computer Science, Celestijnenlaan 200A, Leuven, Belgium. celine.vens@cs.kuleuven.be|Katholieke Universiteit Leuven, Department of Computer Science, Celestijnenlaan 200A, Leuven, Belgium. celine.vens@cs.kuleuven.be|Katholieke Universiteit Leuven, Department of Computer Science, Celestijnenlaan 200A, Leuven, Belgium. celine.vens@cs.kuleuven.be', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Animals|Computational Biology|Conserved Sequence|Discriminant Analysis|Helminth Proteins|Molecular Sequence Data|Proteome|Sequence Alignment|Tylenchoidea', 'Journal Article', '10.1093/bioinformatics/btr110', '21372086', 'Identification of conserved motifs in biological sequences is crucial to unveil common shared functions. Many tools exist for motif identification, including some that allow degenerate positions with multiple possible nucleotides or amino acids. Most efficient methods available today search conserved motifs in a set of sequences, but do not check for their specificity regarding to a set of negative sequences.'),
(76083, 2011, 'bio', 'GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies', 'Yung, Ling Sing|Yang, Can|Wan, Xiang|Yu, Weichuan', 'Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. timyung@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. timyung@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. timyung@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. timyung@ust.hk', 'China|China|China|China', 95, 'Algorithms|Case-Control Studies|Computational Biology|Diabetes Mellitus, Type 2|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btr114', '21372087', 'Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions.'),
(76084, 2011, 'bio', 'Agos--a universal web tool for GW Argonaute-binding domain prediction', 'Zielezinski, Andrzej|Karlowski, Wojciech M', 'Laboratory of Computational Genomics, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland.|Laboratory of Computational Genomics, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland.', 'Poland|Poland', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Arabidopsis|Arabidopsis Proteins|Argonaute Proteins|Binding Sites|Internet|Molecular Sequence Data|Protein Binding|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btr128', '21385787', 'AGO(Argonaute)-binding domains, composed of repeated motifs, in which only binary combinations of tryptophan and glycine are conserved, bind AGO proteins and are essential during RNAi-mediated gene silencing. The amino acid sequence of this domain is extremely divergent and therefore very difficult to detect. Commonly used bioinformatic tools fail to identify tryptophan-glycine and/or glycine-tryptophan motifs (WG/GW) domains and currently there is no publicly available software which can detect these weakly conserved, but functional AGO-binding segments.'),
(76085, 2011, 'bio', 'Baking a mass-spectrometry data PIE with McMC and simulated annealing: predicting protein post-translational modifications from integrated top-down and bottom-up data', 'Jefferys, Stuart R|Giddings, Morgan C', 'University of Chapel Hill, Chapel Hill, NC, USA.|University of Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States', 3, 'Algorithms|Bacterial Proteins|Escherichia coli|Markov Chains|Mass Spectrometry|Monte Carlo Method|Protein Isoforms|Protein Processing, Post-Translational|Proteins|Proteomics|Ribosomal Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr027', '21389073', 'Post-translational modifications are vital to the function of proteins, but are hard to study, especially since several modified isoforms of a protein may be present simultaneously. Mass spectrometers are a great tool for investigating modified proteins, but the data they provide is often incomplete, ambiguous and difficult to interpret. Combining data from multiple experimental techniques-especially bottom-up and top-down mass spectrometry-provides complementary information. When integrated with background knowledge this allows a human expert to interpret what modifications are present and where on a protein they are located. However, the process is arduous and for high-throughput applications needs to be automated.'),
(76086, 2011, 'bio', 'birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics', 'Chew, Teong Han|Joyce-Tan, Kwee Hong|Akma, Farizuwana|Shamsir, Mohd Shahir', 'Department of Biological Sciences, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia. teonghan@gmail.com|Department of Biological Sciences, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia. teonghan@gmail.com|Department of Biological Sciences, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia. teonghan@gmail.com|Department of Biological Sciences, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia. teonghan@gmail.com', 'Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Cluster Analysis|Computational Biology|Computing Methodologies|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btr109', '21398666', 'birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.'),
(76087, 2011, 'bio', 'GeCo++: a C++ library for genomic features computation and annotation in the presence of variants', 'Cereda, Matteo|Sironi, Manuela|Cavalleri, Matteo|Pozzoli, Uberto', 'Bioinformatics Lab, Scientific Institute I.R.C.C.S. E. Medea, Via Don L. Monza, Bosisio Parini (LC), Italy.|Bioinformatics Lab, Scientific Institute I.R.C.C.S. E. Medea, Via Don L. Monza, Bosisio Parini (LC), Italy.|Bioinformatics Lab, Scientific Institute I.R.C.C.S. E. Medea, Via Don L. Monza, Bosisio Parini (LC), Italy.|Bioinformatics Lab, Scientific Institute I.R.C.C.S. E. Medea, Via Don L. Monza, Bosisio Parini (LC), Italy.', 'Italy|Italy|Italy|Italy', 20, 'Algorithms|Base Sequence|Gene Library|Genomics|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr123', '21398667', 'We propose a C++ class library developed to the purpose of making the implementation of sequence analysis algorithms easier and faster when genomic annotations and variations need to be considered. The library provides a class hierarchy to seamlessly bind together annotations of genomic elements to sequences and to algorithm results, it allows to evaluate the effect of mutations/variations in terms of both element position shifts and of algorithm results, limiting recalculation to the minimum. Particular care has been posed to keep memory and time overhead into acceptable limits.'),
(76088, 2011, 'bio', 'sc-PDB: a database for identifying variations and multiplicity of druggable binding sites in proteins', 'Meslamani, Jamel|Rognan, Didier|Kellenberger, Esther', 'Structural Chemogenomics Group, Laboratory of Therapeutic Innovation, UMR7200 CNRS/University of Strasbourg, Faculté de Pharmacie, Illkirch, France.|Structural Chemogenomics Group, Laboratory of Therapeutic Innovation, UMR7200 CNRS/University of Strasbourg, Faculté de Pharmacie, Illkirch, France.|Structural Chemogenomics Group, Laboratory of Therapeutic Innovation, UMR7200 CNRS/University of Strasbourg, Faculté de Pharmacie, Illkirch, France.', 'France|France|France', 0, 'Binding Sites|Databases, Protein|Drug Design|Ligands|Proteins', 'Journal Article', '10.1093/bioinformatics/btr120', '21398668', 'The sc-PDB database is an annotated archive of druggable binding sites extracted from the Protein Data Bank. It contains all-atoms coordinates for 8166 protein-ligand complexes, chosen for their geometrical and physico-chemical properties. The sc-PDB provides a functional annotation for proteins, a chemical description for ligands and the detailed intermolecular interactions for complexes. The sc-PDB now includes a hierarchical classification of all the binding sites within a functional class.'),
(76089, 2011, 'bio', 'UniCarb-DB: a database resource for glycomic discovery', 'Hayes, Catherine A|Karlsson, Niclas G|Struwe, Weston B|Lisacek, Frederique|Rudd, Pauline M|Packer, Nicolle H|Campbell, Matthew P', 'Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.|Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 84, 'Chromatography, Liquid|Databases, Factual|Glycomics|Glycosylation|Polysaccharides|Protein Processing, Post-Translational|Proteins|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btr137', '21398669', 'Glycosylation is one of the most important post-translational modifications of proteins, known to be involved in pathogen recognition, innate immune response and protection of epithelial membranes. However, when compared to the tools and databases available for the processing of high-throughput proteomic data, the glycomic domain is severely lacking. While tools to assist the analysis of mass spectrometry (MS) and HPLC are continuously improving, there are few resources available to support liquid chromatography (LC)-MS/MS techniques for glycan structure profiling. Here, we present a platform for presenting oligosaccharide structures and fragment data characterized by LC-MS/MS strategies. The database is annotated with high-quality datasets and is designed to extend and reinforce those standards and ontologies developed by existing glycomics databases.'),
(76090, 2011, 'bio', 'Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection', 'Zheng, Cheng|Zhang, Shucha|Ragg, Susanne|Raftery, Daniel|Vitek, Olga', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu', 'United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Humans|Magnetic Resonance Spectroscopy|Male|Metabolomics|Models, Statistical', 'Evaluation Studies', '10.1093/bioinformatics/btr118', '21398670', 'Nuclear magnetic resonance (NMR) spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, accurate interpretation of the spectra in terms of identities and abundances of metabolites can be challenging, in particular in crowded regions with heavy peak overlap. Although a number of computational approaches for this task have recently been proposed, they are not entirely satisfactory in either accuracy or extent of automation.'),
(76091, 2011, 'bio', 'High-throughput feature counting and measurement of roots', 'Naeem, Asad|French, Andrew P|Wells, Darren M|Pridmore, Tony P', 'Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham, UK.|Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham, UK.|Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham, UK.|Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Algorithms|Image Processing, Computer-Assisted|Models, Biological|Plant Roots|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr126', '21398671', 'The original RootTrace tool has proved successful in measuring primary root lengths across time series image data. Biologists have shown interest in using the tool to address further problems, namely counting lateral roots to use as parameters in screening studies, and measuring highly curved roots. To address this, the software has been extended to count emerged lateral roots, and the tracking model extended so that strongly curved and agravitropic roots can be now be recovered. Here, we describe the novel image analysis algorithms and user interface implemented within the RootTrace framework to handle such situations and evaluate the results.'),
(76092, 2011, 'bio', 'Histogram-based DNA analysis for the visualization of chromosome, genome and species information', 'Costa, António M|Machado, José T|Quelhas, Maria D', 'Department of Informatics Engineering, Department of Electrotechnical Engineering, ISEP, Rua Dr Bernardino Almeida 431, Porto, Portugal. acc@isep.ipp.pt|Department of Informatics Engineering, Department of Electrotechnical Engineering, ISEP, Rua Dr Bernardino Almeida 431, Porto, Portugal. acc@isep.ipp.pt|Department of Informatics Engineering, Department of Electrotechnical Engineering, ISEP, Rua Dr Bernardino Almeida 431, Porto, Portugal. acc@isep.ipp.pt', 'Portugal|Portugal|Portugal', 21, 'Animals|Base Sequence|Cell Nucleus|Chromosomes|Computational Biology|Genome, Mitochondrial|Genomics|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btr131', '21398672', 'We describe a novel approach to explore DNA nucleotide sequence data, aiming to produce high-level categorical and structural information about the underlying chromosomes, genomes and species.'),
(76093, 2011, 'bio', 'PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies', 'Huang, Jie|Johnson, Andrew D|ODonnell, Christopher J', 'National Heart, Lung, and Blood Institutes Framingham Heart Study, Framingham, MA 01702, USA.|National Heart, Lung, and Blood Institutes Framingham Heart Study, Framingham, MA 01702, USA.|National Heart, Lung, and Blood Institutes Framingham Heart Study, Framingham, MA 01702, USA.', 'United States|United States|United States', 0, 'Computer Simulation|Genetic Pleiotropy|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Models, Statistical|Multivariate Analysis|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btr116', '21398673', 'The concept of pleiotropy was proposed a century ago, though up to now there have been insufficient efforts to design robust statistics and software aimed at visualizing and evaluating pleiotropy at a regional level. The Pleiotropic Region Identification Method (PRIMe) was developed to evaluate potentially pleiotropic loci based upon data from multiple genome-wide association studies (GWAS).'),
(76094, 2011, 'bio', 'TEQC: an R package for quality control in target capture experiments', 'Hummel, Manuela|Bonnin, Sarah|Lowy, Ernesto|Roma, Guglielmo', 'Core Facilities, Centre for Genomic Regulation, UPF, Barcelona, Spain. manuela.hummel@crg.es|Core Facilities, Centre for Genomic Regulation, UPF, Barcelona, Spain. manuela.hummel@crg.es|Core Facilities, Centre for Genomic Regulation, UPF, Barcelona, Spain. manuela.hummel@crg.es|Core Facilities, Centre for Genomic Regulation, UPF, Barcelona, Spain. manuela.hummel@crg.es', 'Spain|Spain|Spain|Spain', 21, 'Computational Biology|DNA Probes|Nucleic Acid Hybridization|Polymerase Chain Reaction|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr122', '21398674', 'TEQC is an R/Bioconductor package for quality assessment of target enrichment experiments. Quality measures comprise specificity and sensitivity of the capture, enrichment, per-target read coverage and its relation to hybridization probe characteristics, coverage uniformity and reproducibility, and read duplicate analysis. Several diagnostic plots allow visual inspection of the data quality.'),
(76095, 2011, 'bio', 'fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios', 'Excoffier, Laurent|Foll, Matthieu', 'Institute of Ecology and Evolution, University of Berne, Berne, Switzerland. laurent.excoffier@iee.unibe.ch|Institute of Ecology and Evolution, University of Berne, Berne, Switzerland. laurent.excoffier@iee.unibe.ch', 'Switzerland|Switzerland', 0, 'Bayes Theorem|Biological Evolution|Computer Simulation|Genetic Markers|Genomics|Markov Chains|Models, Genetic|Recombination, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btr124', '21398675', 'Genetic studies focus on increasingly larger genomic regions of both extant and ancient DNA, and there is a need for simulation software to match these technological advances. We present here a new coalescent-based simulation program fastsimcoal, which is able to quickly simulate a variety of genetic markers scattered over very long genomic regions with arbitrary recombination patterns under complex evolutionary scenarios.'),
(76096, 2011, 'bio', 'On the organization of bioinformatics core services in biology-based research institutes', 'Kallioniemi, Olli|Wessels, Lodewyk|Valencia, Alfonso', 'Institute of Ecology and Evolution, University of Berne, Berne, Switzerland. laurent.excoffier@iee.unibe.ch|Institute of Ecology and Evolution, University of Berne, Berne, Switzerland. laurent.excoffier@iee.unibe.ch|Institute of Ecology and Evolution, University of Berne, Berne, Switzerland. laurent.excoffier@iee.unibe.ch', 'Switzerland|Switzerland|Switzerland', 8, 'Academies and Institutes|Biology|Biomedical Research|Computational Biology|Workforce', 'Editorial', '10.1093/bioinformatics/btr125', '21402560', '-'),
(76097, 2011, 'bio', 'ogaraK: a population genetics simulator for malaria', 'Antao, Tiago|Hastings, Ian M', 'Department of Molecular and Biochemical Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK. tra@popgen.eu|Department of Molecular and Biochemical Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK. tra@popgen.eu', 'United Kingdom|United Kingdom', 0, 'Computer Simulation|Drug Resistance|Genes, Protozoan|Genetics, Population|Malaria, Falciparum|Plasmodium falciparum|Software', 'Journal Article', '10.1093/bioinformatics/btr139', '21414984', 'The evolution of resistance in Plasmodium falciparum malaria against most available treatments is a major global health threat. Population genetics approaches are commonly used to model the spread of drug resistance. Due to uncommon features in malaria biology, existing forward-time population genetics simulators cannot suitably model Plasmodium falciparum malaria.'),
(76098, 2011, 'bio', 'A wholly defined Agilent microarray spike-in dataset', 'Zhu, Qianqian|Miecznikowski, Jeffrey C|Halfon, Marc S', 'Department of Biochemistry, Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214, USA. qzhu@buffalo.edu|Department of Biochemistry, Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214, USA. qzhu@buffalo.edu|Department of Biochemistry, Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214, USA. qzhu@buffalo.edu', 'United States|United States|United States', 6, 'Computational Biology|DNA, Complementary|Oligonucleotide Array Sequence Analysis|ROC Curve', 'Comparative Study', '10.1093/bioinformatics/btr135', '21414985', 'Spike-in datasets provide a valuable resource for assessing and comparing among competing microarray analysis strategies. Our previous wholly defined spike-in datasets, the Golden and Platinum Spikes, have provided insights for the analysis of Affymetrix GeneChips. However, a similar dataset, in which all cRNA identities and relative levels are known prospectively, has not been available for two-color platforms.'),
(76099, 2011, 'bio', 'MrBayes on a graphics processing unit', 'Zhou, Jianfu|Liu, Xiaoguang|Stones, Douglas S|Xie, Qiang|Wang, Gang', 'Nankai-Baidu Joint Laboratory, College of Information Technical Science, Nankai University, Tianjin, China.|Nankai-Baidu Joint Laboratory, College of Information Technical Science, Nankai University, Tianjin, China.|Nankai-Baidu Joint Laboratory, College of Information Technical Science, Nankai University, Tianjin, China.|Nankai-Baidu Joint Laboratory, College of Information Technical Science, Nankai University, Tianjin, China.|Nankai-Baidu Joint Laboratory, College of Information Technical Science, Nankai University, Tianjin, China.', 'China|China|China|China|China', 0, 'Algorithms|Base Sequence|Bayes Theorem|Computational Biology|Markov Chains|Monte Carlo Method|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btr140', '21414986', 'Bayesian phylogenetic inference can be used to propose a tree of life for a collection of species whose DNA sequences are known. While there are many packages available that implement Bayesian phylogenetic inference, such as the popular MrBayes, running these programs poses significant computational challenges. Parallelized versions of the Metropolis coupled Markov chain Monte Carlo (MC³) algorithm in MrBayes have been presented that can run on various platforms, such as a graphics processing unit (GPU). The GPU has been used as a cost-effective means for computational research in many fields. However, until now, some limitations have prevented the GPU from being used to run MrBayes MC³ effectively.'),
(76100, 2011, 'bio', 'COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method', 'Ma, Haisu|Schadt, Eric E|Kaplan, Lee M|Zhao, Hongyu', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|HapMap Project|Humans', 'Comparative Study', '10.1093/bioinformatics/btr136', '21414987', 'The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extracting these sub-networks, very few existing approaches simultaneously consider both the differential expression of individual genes and the differential correlation of gene pairs, losing potentially valuable information in the data.'),
(76101, 2011, 'bio', 'Compounds In Literature (CIL): screening for compounds and relatives in PubMed', 'Grüning, Björn A|Senger, Christian|Erxleben, Anika|Flemming, Stephan|Günther, Stefan', 'Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, Freiburg, Germany.|Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, Freiburg, Germany.|Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, Freiburg, Germany.|Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, Freiburg, Germany.|Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, Freiburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Databases, Factual|Internet|Proteins|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr130', '21414988', 'Searching for certain compounds in literature can be an elaborate task, with many compounds having several different synonyms. Often, only the structure is known but not its name. Furthermore, rarely investigated compounds may not be described in the available literature at all. In such cases, preceding searches for described similar compounds facilitate literature mining. Highlighted names of proteins in selected texts may further accelerate the time-consuming process of literary research. Compounds In Literature (CIL) provides a web interface to automatically find names, structures, and similar structures in over 28 million compounds of PubChem and more than 18 million citations provided by the PubMed service. CILs pre-calculated database contains more than 56 million parent compound-abstract relations. Found compounds, relatives and abstracts are related to proteins in a concise heat map-like overview. Compounds and proteins are highlighted in their respective abstracts, and are provided with links to PubChem and UniProt.'),
(76102, 2011, 'bio', 'Structure-based de novo prediction of zinc-binding sites in proteins of unknown function', 'Zhao, Wei|Xu, Meng|Liang, Zhi|Ding, Bo|Niu, Liwen|Liu, Haiyan|Teng, Maikun', 'Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.', 'China|China|China|China|China|China|China', 27, 'Binding Sites|Ligands|Protein Binding|Protein Structure, Tertiary|Proteins|Sensitivity and Specificity|Zinc', 'Journal Article', '10.1093/bioinformatics/btr133', '21414989', 'Zinc-binding proteins are the most abundant metallo-proteins in Protein Data Bank (PDB). Accurate prediction of zinc-binding sites in proteins of unknown function may provide important clues for the inference of protein function. As zinc binding is often associated with characteristic 3D arrangements of zinc ligand residues, its prediction may benefit from using not only the sequence information but also the structure information of proteins.'),
(76103, 2011, 'bio', 'AStream: an R package for annotating LC/MS metabolomic data', 'Alonso, Arnald|Julià, Antonio|Beltran, Antoni|Vinaixa, Maria|Díaz, Marta|Ibañez, Lourdes|Correig, Xavier|Marsal, Sara', 'Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 38, 'Carbon Isotopes|Chromatography, Liquid|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btr138', '21414990', 'AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results.'),
(76104, 2011, 'bio', 'Customizable views on semantically integrated networks for systems biology', 'Weile, Jochen|Pocock, Matthew|Cockell, Simon J|Lord, Phillip|Dewar, James M|Holstein, Eva-Maria|Wilkinson, Darren|Lydall, David|Hallinan, Jennifer|Wipat, Anil', 'School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.|School of Computing Science, Faculty of Science Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Genetic|Information Storage and Retrieval|Internet|Saccharomyces cerevisiae|Systems Biology|Telomere', 'Journal Article', '10.1093/bioinformatics/btr134', '21414991', 'The rise of high-throughput technologies in the post-genomic era has led to the production of large amounts of biological data. Many of these datasets are freely available on the Internet. Making optimal use of these data is a significant challenge for bioinformaticians. Various strategies for integrating data have been proposed to address this challenge. One of the most promising approaches is the development of semantically rich integrated datasets. Although well suited to computational manipulation, such integrated datasets are typically too large and complex for easy visualization and interactive exploration.'),
(76105, 2011, 'bio', 'Integrative network alignment reveals large regions of global network similarity in yeast and human', 'Kuchaiev, Oleksii|Przulj, Natasa', 'Department of Computer Science, University of California Irvine, Irvine, CA 92617, USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92617, USA.', 'United States|United States', 163, 'Algorithms|Artificial Intelligence|Computational Biology|Humans|Molecular Sequence Annotation|Proteins|Saccharomyces cerevisiae|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btr127', '21414992', 'High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology.'),
(76106, 2011, 'bio', 'Improving the efficiency of multidimensional scaling in the analysis of high-dimensional data using singular value decomposition', 'Bécavin, Christophe|Tchitchek, Nicolas|Mintsa-Eya, Colette|Lesne, Annick|Benecke, Arndt', 'Institut des Hautes Études Scientifiques, Bures sur Yvette, France.|Institut des Hautes Études Scientifiques, Bures sur Yvette, France.|Institut des Hautes Études Scientifiques, Bures sur Yvette, France.|Institut des Hautes Études Scientifiques, Bures sur Yvette, France.|Institut des Hautes Études Scientifiques, Bures sur Yvette, France.', 'France|France|France|France|France', 30, 'Algorithms|Cytokines|Gene Expression Profiling|Humans|Malaria|Molecular Dynamics Simulation|Multivariate Analysis|Principal Component Analysis', 'Journal Article', '10.1093/bioinformatics/btr143', '21421551', 'Multidimensional scaling (MDS) is a well-known multivariate statistical analysis method used for dimensionality reduction and visualization of similarities and dissimilarities in multidimensional data. The advantage of MDS with respect to singular value decomposition (SVD) based methods such as principal component analysis is its superior fidelity in representing the distance between different instances specially for high-dimensional geometric objects. Here, we investigate the importance of the choice of initial conditions for MDS, and show that SVD is the best choice to initiate MDS. Furthermore, we demonstrate that the use of the first principal components of SVD to initiate the MDS algorithm is more efficient than an iteration through all the principal components. Adding stochasticity to the molecular dynamics simulations typically used for MDS of large datasets, contrary to previous suggestions, likewise does not increase accuracy. Finally, we introduce a k nearest neighbor method to analyze the local structure of the geometric objects and use it to control the quality of the dimensionality reduction.'),
(76107, 2011, 'bio', 'SplamiR--prediction of spliced miRNAs in plants', 'Thieme, Christoph J|Gramzow, Lydia|Lobbes, Dajana|Theissen, Günter', 'Department of Genetics, Friedrich Schiller University Jena, Philosophenweg 12, Jena, Germany.|Department of Genetics, Friedrich Schiller University Jena, Philosophenweg 12, Jena, Germany.|Department of Genetics, Friedrich Schiller University Jena, Philosophenweg 12, Jena, Germany.|Department of Genetics, Friedrich Schiller University Jena, Philosophenweg 12, Jena, Germany.', 'Germany|Germany|Germany|Germany', 9, 'Algorithms|Base Sequence|Databases, Nucleic Acid|Expressed Sequence Tags|Genome, Plant|Genomics|MicroRNAs|Molecular Sequence Data|Oryza|Plants|RNA Splicing|RNA, Messenger|RNA, Plant|Sequence Analysis, RNA|Software|Zea mays', 'Journal Article', '10.1093/bioinformatics/btr132', '21421552', 'MicroRNAs (miRNAs) are important regulators of biological processes in plants and animals. Recently, miRNA genes have been discovered, whose primary transcripts are spliced and which cannot be predicted directly from genomic sequence. Hence, more sophisticated programs for the detection of spliced miRNAs are required.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76108, 2011, 'bio', 'GO-Module: functional synthesis and improved interpretation of Gene Ontology patterns', 'Yang, Xinan|Li, Jianrong|Lee, Younghee|Lussier, Yves A', 'Department of Medicine, Section of Genetic Medicine and Center for Biomedical Informatics, the University of Chicago, Chicago, IL, USA.|Department of Medicine, Section of Genetic Medicine and Center for Biomedical Informatics, the University of Chicago, Chicago, IL, USA.|Department of Medicine, Section of Genetic Medicine and Center for Biomedical Informatics, the University of Chicago, Chicago, IL, USA.|Department of Medicine, Section of Genetic Medicine and Center for Biomedical Informatics, the University of Chicago, Chicago, IL, USA.', 'United States|United States|United States|United States', 27, 'Genes|Internet|Software|Terminology as Topic|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btr142', '21421553', 'GO-Module is a web-accessible synthesis and visualization tool developed for end-user biologists to greatly simplify the interpretation of prioritized Gene Ontology (GO) terms. GO-Module radically reduces the complexity of raw GO results into compact biomodules in two distinct ways, by (i) constructing biomodules from significant GO terms based on hierarchical knowledge, and (ii) refining the GO terms in each biomodule to contain only true positive results. Altogether, the features (biomodules) of GO-Module outputs are better organized and on average four times smaller than the input GO terms list (P = 0.0005, n = 16).'),
(76109, 2011, 'bio', 'anota: Analysis of differential translation in genome-wide studies', 'Larsson, Ola|Sonenberg, Nahum|Nadon, Robert', 'Department of Biochemistry, McGill University, Montreal, Quebec, Canada. ola.larsson@ki.se|Department of Biochemistry, McGill University, Montreal, Quebec, Canada. ola.larsson@ki.se|Department of Biochemistry, McGill University, Montreal, Quebec, Canada. ola.larsson@ki.se', 'Canada|Canada|Canada', 49, 'Gene Expression Regulation|Genome, Human|Genome-Wide Association Study|Humans|Protein Biosynthesis|RNA, Messenger|Ribosomes|Software', 'Journal Article', '10.1093/bioinformatics/btr146', '21422072', 'Translational control of gene expression has emerged as a major mechanism that regulates many biological processes and shows dysregulation in human diseases including cancer. When studying differential translation, levels of both actively translating mRNAs and total cytosolic mRNAs are obtained where the latter is used to correct for a possible contribution of differential cytosolic mRNA levels to the observed differential levels of actively translated mRNAs. We have recently shown that analysis of partial variance (APV) corrects for cytosolic mRNA levels more effectively than the commonly applied log ratio approach. APV provides a high degree of specificity and sensitivity for detecting biologically meaningful translation changes, especially when combined with a variance shrinkage method for estimating random error. Here we describe the anota (analysis of translational activity) R-package which implements APV, allows scrutiny of associated statistical assumptions and provides biologically motivated filters for analysis of genome wide datasets. Although the package was developed for analysis of differential translation in polysome microarray or ribosome-profiling datasets, any high-dimensional data that result in paired controls, such as RNP immunoprecipitation-microarray (RIP-CHIP) datasets, can be successfully analyzed with anota.'),
(76110, 2011, 'bio', 'Prediction of microRNA targets in Caenorhabditis elegans using a self-organizing map', 'Heikkinen, Liisa|Kolehmainen, Mikko|Wong, Garry', 'Department of Biosciences, Department of Neurobiology, A.I.Virtanen Institute for Molecular Sciences, Biocenter Finland, Kuopio, Finland. liisa.heikkinen@uef.fi|Department of Biosciences, Department of Neurobiology, A.I.Virtanen Institute for Molecular Sciences, Biocenter Finland, Kuopio, Finland. liisa.heikkinen@uef.fi|Department of Biosciences, Department of Neurobiology, A.I.Virtanen Institute for Molecular Sciences, Biocenter Finland, Kuopio, Finland. liisa.heikkinen@uef.fi', 'Finland|Finland|Finland', 15, 'Algorithms|Animals|Artificial Intelligence|Binding Sites|Caenorhabditis elegans|Cluster Analysis|Computational Biology|MicroRNAs|RNA, Helminth|Software', 'Journal Article', '10.1093/bioinformatics/btr144', '21422073', 'MicroRNAs (miRNAs) are small non-coding RNAs that regulate transcriptional processes via binding to the target gene mRNA. In animals, this binding is imperfect, which makes the computational prediction of animal miRNA targets a challenging task. The accuracy of miRNA target prediction can be improved with the use of machine learning methods. Previous work has described methods using supervised learning, but they suffer from the lack of adequate training examples, a common problem in miRNA target identification, which often leads to deficient generalization ability.'),
(76111, 2011, 'bio', 'ExpEdit: a webserver to explore human RNA editing in RNA-Seq experiments', 'Picardi, Ernesto|DAntonio, Mattia|Carrabino, Danilo|Castrignanò, Tiziana|Pesole, Graziano', 'Dipartimento di Biochimica e Biologia Molecolare E. Quagliariello, Università di Bari, Bari, Italy.|Dipartimento di Biochimica e Biologia Molecolare E. Quagliariello, Università di Bari, Bari, Italy.|Dipartimento di Biochimica e Biologia Molecolare E. Quagliariello, Università di Bari, Bari, Italy.|Dipartimento di Biochimica e Biologia Molecolare E. Quagliariello, Università di Bari, Bari, Italy.|Dipartimento di Biochimica e Biologia Molecolare E. Quagliariello, Università di Bari, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy', 19, 'Computational Biology|Genomics|Humans|Internet|RNA Editing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr117', '21427194', 'ExpEdit is a web application for assessing RNA editing in human at known or user-specified sites supported by transcript data obtained by RNA-Seq experiments. Mapping data (in SAM/BAM format) or directly sequence reads [in FASTQ/short read archive (SRA) format] can be provided as input to carry out a comparative analysis against a large collection of known editing sites collected in DARNED database as well as other user-provided potentially edited positions. Results are shown as dynamic tables containing University of California, Santa Cruz (UCSC) links for a quick examination of the genomic context.'),
(76112, 2011, 'bio', 'Mass-balanced randomization of metabolic networks', 'Basler, Georg|Ebenhöh, Oliver|Selbig, Joachim|Nikoloski, Zoran', 'University of Potsdam, Institute for Biochemistry and Biology, Potsdam, Germany. basler@mpimp-golm.mpg.de|University of Potsdam, Institute for Biochemistry and Biology, Potsdam, Germany. basler@mpimp-golm.mpg.de|University of Potsdam, Institute for Biochemistry and Biology, Potsdam, Germany. basler@mpimp-golm.mpg.de|University of Potsdam, Institute for Biochemistry and Biology, Potsdam, Germany. basler@mpimp-golm.mpg.de', 'Germany|Germany|Germany|Germany', 16, 'Algorithms|Animals|Bacteria|Humans|Metabolic Networks and Pathways|Models, Biological|Plants|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr145', '21436128', 'Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem.'),
(76113, 2011, 'bio', 'Prediction of novel pre-microRNAs with high accuracy through boosting and SVM', 'Zhang, Yuanwei|Yang, Yifan|Zhang, Huan|Jiang, Xiaohua|Xu, Bo|Xue, Yu|Cao, Yunxia|Zhai, Qian|Zhai, Yong|Xu, Mingqing|Cooke, Howard J|Shi, Qinghua', 'Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.|Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 24, 'Animals|Artificial Intelligence|Base Sequence|Computational Biology|Expressed Sequence Tags|Female|Fetus|Humans|MicroRNAs|Ovary', 'Journal Article', '10.1093/bioinformatics/btr148', '21436129', 'High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species.'),
(76114, 2011, 'bio', 'PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs', 'Xuan, Ping|Guo, Maozu|Liu, Xiaoyan|Huang, Yangchao|Li, Wenbin|Huang, Yufei', 'Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, PR China.', 'China|China|China|China|China|China', 54, 'Artificial Intelligence|Base Sequence|Computational Biology|Inverted Repeat Sequences|MicroRNAs|Plants|RNA, Messenger|RNA, Plant', 'Journal Article', '10.1093/bioinformatics/btr153', '21441575', 'MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant roles as post-transcriptional regulators in animals and plants. While some existing methods use comparative genomic approaches to identify plant precursor miRNAs (pre-miRNAs), others are based on the complementarity characteristics between miRNAs and their target mRNAs sequences. However, they can only identify the homologous miRNAs or the limited complementary miRNAs. Furthermore, since the plant pre-miRNAs are quite different from the animal pre-miRNAs, all the ab initio methods for animals cannot be applied to plants. Therefore, it is essential to develop a method based on machine learning to classify real plant pre-miRNAs and pseudo genome hairpins.'),
(76115, 2011, 'bio', 'Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes', 'Jung, Klaus|Becker, Benjamin|Brunner, Edgar|Beissbarth, Tim', 'Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.|Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.|Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.|Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.', 'Germany|Germany|Germany|Germany', 14, 'Algorithms|Colorectal Neoplasms|Gene Expression Profiling|Genome-Wide Association Study|Humans|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1093/bioinformatics/btr152', '21441576', 'An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. In a simulation study, we compare the power and computation times of four different approaches for global testing. The applicability of one of these methods to gene expression data is demonstrated for the first time. In addition, we propose an algorithm for the detection of those genes which might be responsible for a group effect.'),
(76116, 2011, 'bio', 'The sufficient minimal set of miRNA seed types', 'Ellwanger, Daniel C|Büttner, Florian A|Mewes, Hans-Werner|Stümpflen, Volker', 'Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 74, 'Algorithms|Animals|Base Sequence|Eukaryotic Initiation Factors|Gene Expression Profiling|Humans|Mice|MicroRNAs|Oligonucleotide Array Sequence Analysis|Oligonucleotides|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btr149', '21441577', 'Pairing between the target sequence and the 6-8 nt long seed sequence of the miRNA presents the most important feature for miRNA target site prediction. Novel high-throughput technologies such as Argonaute HITS-CLIP afford meanwhile a detailed study of miRNA:mRNA duplices. These interaction maps enable a first discrimination between functional and non-functional target sites in a bulky fashion. Prediction algorithms apply different seed paradigms to identify miRNA target sites. Therefore, a quantitative assessment of miRNA target site prediction is of major interest.'),
(76117, 2011, 'bio', 'Faster exact maximum parsimony search with XMP', 'White, W Timothy J|Holland, Barbara R', 'Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. w.t.white@massey.ac.nz|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. w.t.white@massey.ac.nz', 'New Zealand|New Zealand', 0, 'Algorithms|Animals|Bayes Theorem|Humans|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btr147', '21444294', 'Despite trends towards maximum likelihood and Bayesian criteria, maximum parsimony (MP) remains an important criterion for evaluating phylogenetic trees. Because exact MP search is NP-complete, the computational effort needed to find provably optimal trees skyrockets with increasing numbers of taxa, limiting analyses to around 25-30 taxa. This is, in part, because currently available programs fail to take advantage of parallelism.'),
(76118, 2011, 'bio', 'The caBIG® Life Science Business Architecture Model', 'Boyd, Lauren Becnel|Hunicke-Smith, Scott P|Stafford, Grace A|Freund, Elaine T|Ehlman, Michele|Chandran, Uma|Dennis, Robert|Fernandez, Anna T|Goldstein, Stephen|Steffen, David|Tycko, Benjamin|Klemm, Juli D', 'Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu|Department of Medicine, Hematology/Oncology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. lbboyd@bcm.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biomedical Research|Computational Biology|Computer Systems|National Cancer Institute (U.S.)|Neoplasms|Software|United States|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btr141', '21450709', 'Business Architecture Models (BAMs) describe what a business does, who performs the activities, where and when activities are performed, how activities are accomplished and which data are present. The purpose of a BAM is to provide a common resource for understanding business functions and requirements and to guide software development. The cancer Biomedical Informatics Grid (caBIG®) Life Science BAM (LS BAM) provides a shared understanding of the vocabulary, goals and processes that are common in the business of LS research.'),
(76119, 2011, 'bio', 'hmChIP: a database and web server for exploring publicly available human and mouse ChIP-seq and ChIP-chip data', 'Chen, Li|Wu, George|Ji, Hongkai', 'Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.', 'United States|United States|United States', 34, 'Animals|Chromatin Immunoprecipitation|DNA|DNA-Binding Proteins|Databases, Genetic|Genome-Wide Association Study|Humans|Internet|Mice|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btr156', '21450710', 'hmChIP is a database of genome-wide chromatin immunoprecipitation (ChIP) data in human and mouse. Currently, the database contains 2016 samples from 492 ChIP-seq and ChIP-chip experiments, representing a total of 170 proteins and 11 069 914 protein-DNA interactions. A web server provides interface for database query. Protein-DNA binding intensities can be retrieved from individual samples for user-provided genomic regions. The retrieved intensities can be used to cluster samples and genomic regions to facilitate exploration of combinatorial patterns, cell-type dependencies, and cross-sample variability of protein-DNA interactions.'),
(76120, 2011, 'bio', 'pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery', 'Johannes, Marc|Fröhlich, Holger|Sültmann, Holger|Beissbarth, Tim', 'German Cancer Research Center, Cancer Genome Research, Heidelberg, Germany. m.johannes@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Heidelberg, Germany. m.johannes@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Heidelberg, Germany. m.johannes@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Heidelberg, Germany. m.johannes@dkfz-heidelberg.de', 'Germany|Germany|Germany|Germany', 16, 'Algorithms|Biomarkers|Gene Expression Profiling|Gene Regulatory Networks|Humans|Neoplasms|Prognosis', 'Journal Article', '10.1093/bioinformatics/btr157', '21450711', 'Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data.'),
(76121, 2011, 'bio', 'Exact and complete short-read alignment to microbial genomes using Graphics Processing Unit programming', 'Blom, Jochen|Jakobi, Tobias|Doppmeier, Daniel|Jaenicke, Sebastian|Kalinowski, Jörn|Stoye, Jens|Goesmann, Alexander', 'Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 64, 'Algorithms|Base Sequence|Computational Biology|Gene Expression Profiling|Genome, Bacterial|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr151', '21450712', 'The introduction of next-generation sequencing techniques and especially the high-throughput systems Solexa (Illumina Inc.) and SOLiD (ABI) made the mapping of short reads to reference sequences a standard application in modern bioinformatics. Short-read alignment is needed for reference based re-sequencing of complete genomes as well as for gene expression analysis based on transcriptome sequencing. Several approaches were developed during the last years allowing for a fast alignment of short sequences to a given template. Methods available to date use heuristic techniques to gain a speedup of the alignments, thereby missing possible alignment positions. Furthermore, most approaches return only one best hit for every query sequence, thus losing the potentially valuable information of alternative alignment positions with identical scores.'),
(76122, 2011, 'bio', 'MRSD: a web server for metabolic route search and design', 'Xia, Deguo|Zheng, Haoran|Liu, Zhiqiang|Li, Guisheng|Li, Jinlong|Hong, Jiong|Zhao, Kai', 'School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.|School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, PR China.', 'China|China|China|China|China|China|China', 0, 'Internet|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btr160', '21450713', 'We present a tool called MRSD (Metabolic Route Search and Design) to search and design routes based on the weighted compound transform diagraph. The search submodule returns routes between a source and product compound within seconds in the network of one or multiple organisms based on data from KEGG. The design submodule designs a route from an appointed compound in an interactive mode. The two complementary functions, Metabolic Route Search and Design, can be broadly used in biosynthesis, bio-pharmaceuticals and the other related fields.'),
(76123, 2011, 'bio', 'Inference of transcriptional regulatory network by bootstrapping patterns', 'Wang, Hei-Chia|Chen, Yi-Hsiu|Kao, Hung-Yu|Tsai, Shaw-Jenq', 'Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.|Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.|Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.|Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.', '-|-|-|-', 0, 'Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr155', '21450714', 'Transcriptional regulatory networks, which consist of linkages between transcription factors (TF) and target genes (TGene), control the expression of a genome and play important roles in all aspects of an organisms life cycle. Accurate prediction of transcriptional regulatory networks is critical in providing useful information for biologists to determine what to do next. Currently, there is a substantial amount of fragmented gene regulation information described in the medical literature. However, current related text analysis methods designed to identify protein-protein interactions are not entirely suitable for finding transcriptional regulatory networks.'),
(76124, 2011, 'bio', 'High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans', 'Cosgun, Erdal|Limdi, Nita A|Duarte, Christine W', 'Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.', 'United States|United States|United States', 37, 'African Americans|Anticoagulants|Artificial Intelligence|Dose-Response Relationship, Drug|Genetic Variation|Genome-Wide Association Study|Humans|Models, Biological|Pharmacogenetics|Polymorphism, Single Nucleotide|Warfarin', 'Journal Article', '10.1093/bioinformatics/btr159', '21450715', 'With complex traits and diseases having potential genetic contributions of thousands of genetic factors, and with current genotyping arrays consisting of millions of single nucleotide polymorphisms (SNPs), powerful high-dimensional statistical techniques are needed to comprehensively model the genetic variance. Machine learning techniques have many advantages including lack of parametric assumptions, and high power and flexibility.'),
(76125, 2011, 'bio', 'A novel network-based method for measuring the functional relationship between gene sets', 'Wang, Qianghu|Sun, Jie|Zhou, Meng|Yang, Haixiu|Li, Yan|Li, Xiang|Lv, Sali|Li, Xia|Li, Yixue', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.', 'China|China|China|China|China|China|China|China|China', 17, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Humans|Multiprotein Complexes|Protein Interaction Mapping|Signal Transduction|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr154', '21450716', 'In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms.'),
(76126, 2011, 'bio', 'A qualitative continuous model of cellular auxin and brassinosteroid signaling and their crosstalk', 'Sankar, Martial|Osmont, Karen S|Rolcik, Jakub|Gujas, Bojan|Tarkowska, Danuse|Strnad, Miroslav|Xenarios, Ioannis|Hardtke, Christian S', 'Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch|Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland. martial.sankar@unil.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 24, 'Arabidopsis|Cell Growth Processes|Indoleacetic Acids|Models, Biological|Plant Growth Regulators|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btr158', '21450717', 'Hormone pathway interactions are crucial in shaping plant development, such as synergism between the auxin and brassinosteroid pathways in cell elongation. Both hormone pathways have been characterized in detail, revealing several feedback loops. The complexity of this network, combined with a shortage of kinetic data, renders its quantitative analysis virtually impossible at present.'),
(76127, 2011, 'bio', 'Rigid substructure search', 'Shirvanyants, David|Alexandrova, Anastassia N|Dokholyan, Nikolay V', 'Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, NC 27599-7260, USA.|Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, NC 27599-7260, USA.|Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, NC 27599-7260, USA.', 'United States|United States|United States', 15, 'Algorithms|Binding Sites|Databases, Protein|Drug Design|Internet|Protein Binding|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btr129', '21460026', 'Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications, including molecular docking, de novo drug design, structure identification and comparison of functional sites. Here we present Erebus, a web server that searches the entire Protein Data Bank for a given substructure defined by a set of atoms of interest, such as the binding scaffolds for small molecules. The identified substructure contains atoms having the same names, belonging to same amino acids and separated by the same distances (within a given tolerance) as the atoms of the query structure. The accuracy of a match is measured by the root-mean-square deviation or by the normal weight with a given variance. Tests show that our approach can reliably locate rigid binding scaffolds of drugs and metal ions.'),
(76128, 2011, 'bio', 'EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards', 'Hemani, Gibran|Theocharidis, Athanasios|Wei, Wenhua|Haley, Chris', 'Division of Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. gib.hemani@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. gib.hemani@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. gib.hemani@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. gib.hemani@roslin.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Graphics|Epistasis, Genetic|Genome-Wide Association Study|Phenotype|Polymorphism, Single Nucleotide|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr172', '21471009', 'Hundreds of genome-wide association studies have been performed over the last decade, but as single nucleotide polymorphism (SNP) chip density has increased so has the computational burden to search for epistasis [for n SNPs the computational time resource is O(n(n-1)/2)]. While the theoretical contribution of epistasis toward phenotypes of medical and economic importance is widely discussed, empirical evidence is conspicuously absent because its analysis is often computationally prohibitive. To facilitate resolution in this field, tools must be made available that can render the search for epistasis universally viable in terms of hardware availability, cost and computational time.'),
(76129, 2011, 'bio', 'Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies', 'Teschendorff, Andrew E|Zhuang, Joanna|Widschwendter, Martin', 'Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, London WC1E 6BT, UK. a.teschendorff@ucl.ac.uk|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, London WC1E 6BT, UK. a.teschendorff@ucl.ac.uk|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, London WC1E 6BT, UK. a.teschendorff@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 147, 'Algorithms|Breast Neoplasms|DNA Methylation|Female|Gene Expression Profiling|Humans|Male|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Evaluation Studies', '10.1093/bioinformatics/btr171', '21471010', 'A common difficulty in large-scale microarray studies is the presence of confounding factors, which may significantly skew estimates of statistical significance, cause unreliable feature selection and high false negative rates. To deal with these difficulties, an algorithmic framework known as Surrogate Variable Analysis (SVA) was recently proposed.'),
(76130, 2011, 'bio', 'Alignment-free detection of local similarity among viral and bacterial genomes', 'Domazet-Lošo, Mirjana|Haubold, Bernhard', 'Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Germany.', 'Germany|Germany', 0, 'Escherichia coli|Gene Transfer, Horizontal|Genome, Bacterial|Genome, Viral|Genomics|HIV-1|Humans|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btr176', '21471011', 'Bacterial and viral genomes are often affected by horizontal gene transfer observable as abrupt switching in local homology. In addition to the resulting mosaic genome structure, they frequently contain regions not found in close relatives, which may play a role in virulence mechanisms. Due to this connection to medical microbiology, there are numerous methods available to detect horizontal gene transfer. However, these are usually aimed at individual genes and viral genomes rather than the much larger bacterial genomes. Here, we propose an efficient alignment-free approach to describe the mosaic structure of viral and bacterial genomes, including their unique regions.'),
(76131, 2011, 'bio', 'ProDy: protein dynamics inferred from theory and experiments', 'Bakan, Ahmet|Meireles, Lidio M|Bahar, Ivet', 'Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. ahb12@pitt.edu|Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. ahb12@pitt.edu|Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. ahb12@pitt.edu', 'United States|United States|United States', 0, 'Computer Graphics|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr168', '21471012', 'We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods.'),
(76132, 2011, 'bio', 'Inferring Boolean network structure via correlation', 'Maucher, Markus|Kracher, Barbara|Kühl, Michael|Kestler, Hans A', 'Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.|Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.|Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.|Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Escherichia coli|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Models, Genetic|Saccharomycetales|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr166', '21471013', 'Accurate, context-specific regulation of gene expression is essential for all organisms. Accordingly, it is very important to understand the complex relations within cellular gene regulatory networks. A tool to describe and analyze the behavior of such networks are Boolean models. The reconstruction of a Boolean network from biological data requires identification of dependencies within the network. This task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. Thus, we developed a method that is especially suited for network structure reconstruction from large-scale data. In our approach, we took advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene, and this kind of regulatory behavior can be modeled using monotone functions.'),
(76133, 2011, 'bio', 'Correcting errors in short reads by multiple alignments', 'Salmela, Leena|Schröder, Jan', 'Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. leena.salmela@cs.helsinki.fi|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. leena.salmela@cs.helsinki.fi', 'Finland|Finland', 110, 'Algorithms|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr170', '21471014', 'Current sequencing technologies produce a large number of erroneous reads. The sequencing errors present a major challenge in utilizing the data in de novo sequencing projects as assemblers have difficulties in dealing with errors.'),
(76134, 2011, 'bio', 'DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models', 'Taslim, Cenny|Huang, Tim|Lin, Shili', 'Department of Molecular Virology, Immunology and Medical Genetics and Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.|Department of Molecular Virology, Immunology and Medical Genetics and Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.|Department of Molecular Virology, Immunology and Medical Genetics and Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.', 'United States|United States|United States', 19, 'Algorithms|Binding Sites|Cell Line, Tumor|Chromatin Immunoprecipitation|DNA-Binding Proteins|Humans|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr165', '21471015', 'Differential Identification using Mixtures Ensemble (DIME) is a package for identification of biologically significant differential binding sites between two conditions using ChIP-seq data. It considers a collection of finite mixture models combined with a false discovery rate (FDR) criterion to find statistically significant regions. This leads to a more reliable assessment of differential binding sites based on a statistical approach. In addition to ChIP-seq, DIME is also applicable to data from other high-throughput platforms.'),
(76135, 2011, 'bio', 'CMView: interactive contact map visualization and analysis', 'Vehlow, Corinna|Stehr, Henning|Winkelmann, Matthias|Duarte, José M|Petzold, Lars|Dinse, Juliane|Lappe, Michael', 'Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.|Department of Simulations and Graphics, O.v.G. University Magdeburg, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computer Graphics|Models, Molecular|Protein Conformation|Protein Structure, Secondary|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btr163', '21471016', 'Contact maps are a valuable visualization tool in structural biology. They are a convenient way to display proteins in two dimensions and to quickly identify structural features such as domain architecture, secondary structure and contact clusters. We developed a tool called CMView which integrates rich contact map analysis with 3D visualization using PyMol. Our tool provides functions for contact map calculation from structure, basic editing, visualization in contact map and 3D space and structural comparison with different built-in alignment methods. A unique feature is the interactive refinement of structural alignments based on user selected substructures.'),
(76136, 2011, 'bio', 'Comprehensive and relaxed search for oligonucleotide signatures in hierarchically clustered sequence datasets', 'Bader, Kai Christian|Grothoff, Christian|Meier, Harald', 'Services Department of Informatics, Technische Universität München, Boltzmannstrasse 3, 85748 Garching, Germany.|Services Department of Informatics, Technische Universität München, Boltzmannstrasse 3, 85748 Garching, Germany.|Services Department of Informatics, Technische Universität München, Boltzmannstrasse 3, 85748 Garching, Germany.', 'Germany|Germany|Germany', 12, 'Algorithms|Cluster Analysis|DNA Primers|Oligonucleotide Probes|Sensitivity and Specificity|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr161', '21471017', 'PCR, hybridization, DNA sequencing and other important methods in molecular diagnostics rely on both sequence-specific and sequence group-specific oligonucleotide primers and probes. Their design depends on the identification of oligonucleotide signatures in whole genome or marker gene sequences. Although genome and gene databases are generally available and regularly updated, collections of valuable signatures are rare. Even for single requests, the search for signatures becomes computationally expensive when working with large collections of target (and non-target) sequences. Moreover, with growing dataset sizes, the chance of finding exact group-matching signatures decreases, necessitating the application of relaxed search methods. The resultant substantial increase in complexity is exacerbated by the dearth of algorithms able to solve these problems efficiently.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76137, 2011, 'bio', 'Multi-platform segmentation for joint detection of copy number variants', 'Teo, Shu Mei|Pawitan, Yudi|Kumar, Vikrant|Thalamuthu, Anbupalam|Seielstad, Mark|Chia, Kee Seng|Salim, Agus', 'Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.|Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Aged|Algorithms|Breast Neoplasms|DNA Copy Number Variations|DNA, Neoplasm|Female|Genome-Wide Association Study|Genotype|Humans|Middle Aged|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btr162', '21471018', 'With the expansion of whole-genome studies, there is rapid evolution of genotyping platforms. This leads to practical issues such as upgrading of genotyping equipment which often results in research groups having data from different platforms for the same samples. While having more data can potentially yield more accurate copy-number estimates, combining such data is not straightforward as different platforms show different degrees of attenuation of the true copy-number or different noise characteristics and marker panels. Currently, there is still a relative lack of procedures for combining information from different platforms.'),
(76138, 2011, 'bio', 'Reasoning with bio-ontologies: using relational closure rules to enable practical querying', 'Blondé, Ward|Mironov, Vladimir|Venkatesan, Aravind|Antezana, Erick|De Baets, Bernard|Kuiper, Martin', 'Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be|Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be|Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be|Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be|Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be|Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium. ward.blonde@ugent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Knowledge Bases|Logic|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btr164', '21471019', 'Ontologies have become indispensable in the Life Sciences for managing large amounts of knowledge. The use of logics in ontologies ranges from sound modelling to practical querying of that knowledge, thus adding a considerable value. We conceive reasoning on bio-ontologies as a semi-automated process in three steps: (i) defining a logic-based representation language, (ii) building a consistent ontology using that language, and (iii) exploiting the ontology through querying.'),
(76139, 2011, 'bio', 'Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds', 'Mu, Fangping|Unkefer, Clifford J|Unkefer, Pat J|Hlavacek, William S', 'Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. fmu@lanl.gov|Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. fmu@lanl.gov|Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. fmu@lanl.gov|Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. fmu@lanl.gov', 'United States|United States|United States|United States', 27, 'Artificial Intelligence|Biocatalysis|Classification|Computational Biology|Databases, Factual|Enzymes|Metabolic Networks and Pathways|Metabolome|Metabolomics|Molecular Structure', 'Journal Article', '10.1093/bioinformatics/btr177', '21478194', 'Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.'),
(76140, 2011, 'bio', 'CyClus3D: a Cytoscape plugin for clustering network motifs in integrated networks', 'Audenaert, Pieter|Van Parys, Thomas|Brondel, Florian|Pickavet, Mario|Demeester, Piet|Van de Peer, Yves|Michoel, Tom', 'Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.|Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Cluster Analysis|Gene Regulatory Networks|Models, Biological|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btr182', '21478195', 'Network motifs in integrated molecular networks represent functional relationships between distinct data types. They aggregate to form dense topological structures corresponding to functional modules which cannot be detected by traditional graph clustering algorithms. We developed CyClus3D, a Cytoscape plugin for clustering composite three-node network motifs using a 3D spectral clustering algorithm.'),
(76141, 2011, 'bio', 'Performance assessment of copy number microarray platforms using a spike-in experiment', 'Halper-Stromberg, Eitan|Frelin, Laurence|Ruczinski, Ingo|Scharpf, Robert|Jie, Chunfa|Carvalho, Benilton|Hao, Haiping|Hetrick, Kurt|Jedlicka, Anne|Dziedzic, Amanda|Doheny, Kim|Scott, Alan F|Baylin, Steve|Pevsner, Jonathan|Spencer, Forrest|Irizarry, Rafael A', 'Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 20, 'Algorithms|DNA Copy Number Variations|Female|Humans|Male|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btr106', '21478196', 'Changes in the copy number of chromosomal DNA segments [copy number variants (CNVs)] have been implicated in human variation, heritable diseases and cancers. Microarray-based platforms are the current established technology of choice for studies reporting these discoveries and constitute the benchmark against which emergent sequence-based approaches will be evaluated. Research that depends on CNV analysis is rapidly increasing, and systematic platform assessments that distinguish strengths and weaknesses are needed to guide informed choice.'),
(76142, 2011, 'bio', 'A comprehensive protein-centric ID mapping service for molecular data integration', 'Huang, Hongzhan|McGarvey, Peter B|Suzek, Baris E|Mazumder, Raja|Zhang, Jian|Chen, Yongxing|Wu, Cathy H', 'Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA. huang@dbi.udel.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Protein|Internet|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr101', '21478197', 'Identifier (ID) mapping establishes links between various biological databases and is an essential first step for molecular data integration and functional annotation. ID mapping allows diverse molecular data on genes and proteins to be combined and mapped to functional pathways and ontologies. We have developed comprehensive protein-centric ID mapping services providing mappings for 90 IDs derived from databases on genes, proteins, pathways, diseases, structures, protein families, protein interaction, literature, ontologies, etc. The services are widely used and have been regularly updated since 2006.'),
(76143, 2011, 'bio', 'Comrad: detection of expressed rearrangements by integrated analysis of RNA-Seq and low coverage genome sequence data', 'McPherson, Andrew|Wu, Chunxiao|Hajirasouliha, Iman|Hormozdiari, Fereydoun|Hach, Faraz|Lapuk, Anna|Volik, Stanislav|Shah, Sohrab|Collins, Colin|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. andrew.mcpherson@gmail.com', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 27, 'Algorithms|Cell Line, Tumor|Chromosome Breakpoints|Chromosome Mapping|Gene Expression Profiling|Gene Fusion|Genomics|Humans|Mutant Chimeric Proteins|RNA Splicing|RNA, Messenger|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btr184', '21478487', 'Comrad is a novel algorithmic framework for the integrated analysis of RNA-Seq and whole genome shotgun sequencing (WGSS) data for the purposes of discovering genomic rearrangements and aberrant transcripts. The Comrad framework leverages the advantages of both RNA-Seq and WGSS data, providing accurate classification of rearrangements as expressed or not expressed and accurate classification of the genomic or non-genomic origin of aberrant transcripts. A major benefit of Comrad is its ability to accurately identify aberrant transcripts and associated rearrangements using low coverage genome data. As a result, a Comrad analysis can be performed at a cost comparable to that of two RNA-Seq experiments, significantly lower than an analysis requiring high coverage genome data.'),
(76144, 2011, 'bio', 'Reliability-oriented bioinformatic networks visualization', 'Aladaxc4x9f, Ahmet Emre|Erten, Cesim|Sxc3xb6zdinler, Melih', 'Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083.|Department of Computer Engineering, Kadir Has University, Cibali, Istanbul 34083.', '-|-|-', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btr178', '21478488', 'We present our protein-protein interaction (PPI) network visualization system RobinViz (reliability-oriented bioinformatic networks visualization). Clustering the PPI network based on gene ontology (GO) annotations or biclustered gene expression data, providing a clustered visualization model based on a central/peripheral duality, computing layouts with algorithms specialized for interaction reliabilities represented as weights, completely automated data acquisition, processing are notable features of the system.'),
(76145, 2011, 'bio', 'MEME-ChIP: motif analysis of large DNA datasets', 'Machanick, Philip|Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia.', 'Australia|Australia', 706, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|DNA|Genomics|Internet|Protein Binding|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr189', '21486936', 'Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets.'),
(76146, 2011, 'bio', 'Cobweb: a Java applet for network exploration and visualisation', 'von Eichborn, Joachim|Bourne, Philip E|Preissner, Robert', 'Structural Bioinformatics Group, Institute for Physiology, Charité - Universitätsmedizin Berlin, Lindenberger Weg 80, 13125 Berlin, Germany. joachim.eichborn@charite.de|Structural Bioinformatics Group, Institute for Physiology, Charité - Universitätsmedizin Berlin, Lindenberger Weg 80, 13125 Berlin, Germany. joachim.eichborn@charite.de|Structural Bioinformatics Group, Institute for Physiology, Charité - Universitätsmedizin Berlin, Lindenberger Weg 80, 13125 Berlin, Germany. joachim.eichborn@charite.de', 'Germany|Germany|Germany', 0, 'Computer Graphics|Databases, Factual|Internet|Protein Interaction Mapping|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr195', '21486937', 'Cobweb is a Java applet for real-time network visualization, its strength lies in enabling the interactive exploration of networks. Therefore, it allows new nodes to be interactively added to a network by querying a database on a server. The network constantly rearranges to provide the most meaningful topological view.'),
(76147, 2011, 'bio', 'An optimal peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using mixture similarity measure', 'Kim, Seongho|Fang, Aiqin|Wang, Bing|Jeong, Jaesik|Zhang, Xiang', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA. s0kim023@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA. s0kim023@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA. s0kim023@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA. s0kim023@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA. s0kim023@louisville.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Gas Chromatography-Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btr188', '21493650', 'Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions.'),
(76148, 2011, 'bio', 'Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV drug research centre', 'Spjuth, Ola|Eklund, Martin|Lapins, Maris|Junaid, Muhammad|Wikberg, Jarl E S', 'Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 3, 'Anti-HIV Agents|Drug Design|Drug Resistance, Viral|HIV|HIV Protease|HIV Protease Inhibitors|HIV Reverse Transcriptase|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1093/bioinformatics/btr192', '21493651', 'The HIV Drug Research Centre (HIVDRC) has established Web services for prediction of drug susceptibility for HIV proteases and reverse transcriptases. The services are based on two proteochemometric models which accepts a protease or reverse transcriptase sequence in amino acid form, and outputs the predicted drug susceptibility values. The predictions are based on a comprehensive analysis where all the relevant inhibitors are included, resulting in models with excellent predictive capabilities.'),
(76149, 2011, 'bio', 'BamTools: a C++ API and toolkit for analyzing and managing BAM files', 'Barnett, Derek W|Garrison, Erik K|Quinlan, Aaron R|Strömberg, Michael P|Marth, Gabor T', 'Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. barnetde@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. barnetde@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. barnetde@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. barnetde@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. barnetde@bc.edu', 'United States|United States|United States|United States|United States', 286, 'Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr174', '21493652', 'Analysis of genomic sequencing data requires efficient, easy-to-use access to alignment results and flexible data management tools (e.g. filtering, merging, sorting, etc.). However, the enormous amount of data produced by current sequencing technologies is typically stored in compressed, binary formats that are not easily handled by the text-based parsers commonly used in bioinformatics research.'),
(76150, 2011, 'bio', 'A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio', 'Leung, Henry C M|Yiu, S M|Yang, Bin|Peng, Yu|Wang, Yi|Liu, Zhihua|Chen, Jingchi|Qin, Junjie|Li, Ruiqiang|Chin, Francis Y L', 'Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, 'Algorithms|Cluster Analysis|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr186', '21493653', 'With the rapid development of next-generation sequencing techniques, metagenomics, also known as environmental genomics, has emerged as an exciting research area that enables us to analyze the microbial environment in which we live. An important step for metagenomic data analysis is the identification and taxonomic characterization of DNA fragments (reads or contigs) resulting from sequencing a sample of mixed species. This step is referred to as binning. Binning algorithms that are based on sequence similarity and sequence composition markers rely heavily on the reference genomes of known microorganisms or phylogenetic markers. Due to the limited availability of reference genomes and the bias and low availability of markers, these algorithms may not be applicable in all cases. Unsupervised binning algorithms which can handle fragments from unknown species provide an alternative approach. However, existing unsupervised binning algorithms only work on datasets either with balanced species abundance ratios or rather different abundance ratios, but not both.'),
(76151, 2011, 'bio', 'Proteins without 3D structure: definition, detection and beyond', 'Orosz, Ferenc|Ovádi, Judit', 'Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, Karolina út 29, Budapest, H-1113 Hungary. orosz@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, Karolina út 29, Budapest, H-1113 Hungary. orosz@enzim.hu', 'Hungary|Hungary', 26, 'Humans|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btr175', '21493654', 'Predictions, and experiments to a lesser extent, following the decoding of the human genome showed that a significant fraction of gene products do not have well-defined 3D structures. While the presence of structured domains traditionally suggested function, it was not clear what the absence of structure implied. These and many other findings initiated the extensive theoretical and experimental research into these types of proteins, commonly known as intrinsically disordered proteins (IDPs). Crucial to understanding IDPs is the evaluation of structural predictors based on different principles and trained on various datasets, which is currently the subject of active research. The view is emerging that structural disorder can be considered as a separate structural category and not simply as absence of secondary and/or tertiary structure. IDPs perform essential functions and their improper functioning is responsible for human diseases such as neurodegenerative disorders.'),
(76152, 2011, 'bio', 'RuleBender: a visual interface for rule-based modeling', 'Xu, Wen|Smith, Adam M|Faeder, James R|Marai, G Elisabeta', 'Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA.', 'United States|United States|United States|United States', 0, 'Biochemical Phenomena|Computer Graphics|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr197', '21493655', 'Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling intracellular biochemistry. Current interfaces for RBM are predominantly text-based and command-line driven. Better visual tools are needed to make RBM accessible to a broad range of users, to make specification of models less error prone and to improve workflows. We present RULEBENDER, an open-source visual interface that facilitates interactive debugging, simulation and analysis of RBMs.'),
(76153, 2011, 'bio', 'Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications', 'Krueger, Felix|Andrews, Simon R', 'Bioinformatics Group, The Babraham Institute, CB22 3AT, Cambridge, UK. felix.krueger@bbsrc.ac.uk|Bioinformatics Group, The Babraham Institute, CB22 3AT, Cambridge, UK. felix.krueger@bbsrc.ac.uk', 'United Kingdom|United Kingdom', 1438, 'Cytosine|DNA|DNA Methylation|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btr167', '21493656', 'A combination of bisulfite treatment of DNA and high-throughput sequencing (BS-Seq) can capture a snapshot of a cells epigenomic state by revealing its genome-wide cytosine methylation at single base resolution. Bismark is a flexible tool for the time-efficient analysis of BS-Seq data which performs both read mapping and methylation calling in a single convenient step. Its output discriminates between cytosines in CpG, CHG and CHH context and enables bench scientists to visualize and interpret their methylation data soon after the sequencing run is completed.'),
(76154, 2011, 'bio', 'Sciencenet--towards a global search and share engine for all scientific knowledge', 'Lütjohann, Dominic S|Shah, Asmi H|Christen, Michael P|Richter, Florian|Knese, Karsten|Liebel, Urban', 'Institute of Organic Chemistry, Karlsruhe, Germany.|Institute of Organic Chemistry, Karlsruhe, Germany.|Institute of Organic Chemistry, Karlsruhe, Germany.|Institute of Organic Chemistry, Karlsruhe, Germany.|Institute of Organic Chemistry, Karlsruhe, Germany.|Institute of Organic Chemistry, Karlsruhe, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Biological Science Disciplines|Internet|Search Engine|Software', 'Journal Article', '10.1093/bioinformatics/btr181', '21493657', 'Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist. We have developed a prototype distributed scientific search engine technology, Sciencenet, which facilitates rapid searching over this large data space. By bringing the search engine to the data, we do not require server farms. This platform also allows users to contribute to the search index and publish their large-scale data to support e-Science. Furthermore, a community-driven method guarantees that only scientific content is crawled and presented. Our peer-to-peer approach is sufficiently scalable for the science web without performance or capacity tradeoff.'),
(76155, 2011, 'bio', 'Figure summarizer browser extensions for PubMed Central', 'Agarwal, Shashank|Yu, Hong', 'Medical Informatics, College of Health Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201-0413, USA. agarwal@uwm.edu|Medical Informatics, College of Health Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201-0413, USA. agarwal@uwm.edu', 'United States|United States', 0, 'Algorithms|Data Mining|Internet|PubMed|Software', 'Journal Article', '10.1093/bioinformatics/btr194', '21493658', 'Figures in biomedical articles present visual evidence for research facts and help readers understand the article better. However, when figures are taken out of context, it is difficult to understand their content. We developed a summarization algorithm to summarize the content of figures and used it in our figure search engine (http://figuresearch.askhermes.org/). In this article, we report on the development of web browser extensions for Mozilla Firefox, Google Chrome and Apple Safari to display summaries for figures in PubMed Central and NCBI Images.'),
(76156, 2011, 'bio', 'SMETHILLIUM: spatial normalization method for Illumina infinium HumanMethylation BeadChip', 'Sabbah, Camille|Mazo, Gildas|Paccard, Caroline|Reyal, Fabien|Hupé, Philippe', 'Institut Curie, 26 rue dUlm, F-75248, Paris, France.|Institut Curie, 26 rue dUlm, F-75248, Paris, France.|Institut Curie, 26 rue dUlm, F-75248, Paris, France.|Institut Curie, 26 rue dUlm, F-75248, Paris, France.|Institut Curie, 26 rue dUlm, F-75248, Paris, France.', 'France|France|France|France|France', 8, 'DNA Methylation|Epigenesis, Genetic|Humans|Molecular Probes|Oligonucleotide Array Sequence Analysis|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr187', '21493659', 'DNA methylation is a major epigenetic modification in human cells. Illumina HumanMethylation27 BeadChip makes it possible to quantify the methylation state of 27 578 loci spanning 14 495 genes. We developed a non-parametric normalization method to correct the spatial background noise in order to improve the signal-to-noise ratio. The prediction performance of the proposed method was assessed on three fully methylated samples and three fully unmethylated DNA samples. We demonstrate that the spatial normalization outperforms BeadStudio to predict the methylation state of a given locus.'),
(76157, 2011, 'bio', 'RING: networking interacting residues, evolutionary information and energetics in protein structures', 'Martin, Alberto J M|Vidotto, Michele|Boscariol, Filippo|Di Domenico, Tomàs|Walsh, Ian|Tosatto, Silvio C E', 'Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 77, 'Databases, Protein|Glutathione Peroxidase|Humans|Imaging, Three-Dimensional|Internet|Protein Conformation|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr191', '21493660', 'Residue interaction networks (RINs) have been used in the literature to describe the protein 3D structure as a graph where nodes represent residues and edges physico-chemical interactions, e.g. hydrogen bonds or van-der-Waals contacts. Topological network parameters can be calculated over RINs and have been correlated with various aspects of protein structure and function. Here we present a novel web server, RING, to construct physico-chemically valid RINs interactively from PDB files for subsequent visualization in the Cytoscape platform. The additional structure-based parameters secondary structure, solvent accessibility and experimental uncertainty can be combined with information regarding residue conservation, mutual information and residue-based energy scoring functions. Different visualization styles are provided to facilitate visualization and standard plugins can be used to calculate topological parameters in Cytoscape. A sample use case analyzing the active site of glutathione peroxidase is presented.'),
(76158, 2011, 'bio', 'Rapid membrane protein topology prediction', 'Hennerdal, Aron|Elofsson, Arne', 'Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Center for Biomembrane Research, Swedish e-science Research Center, Stockholm University, Stockholm, Sweden.|Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Center for Biomembrane Research, Swedish e-science Research Center, Stockholm University, Stockholm, Sweden.', 'Sweden|Sweden', 0, 'Algorithms|Internet|Markov Chains|Membrane Proteins|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btr119', '21493661', 'State-of-the-art methods for topology of xcexb1-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments.'),
(76159, 2011, 'bio', 'OTUbase: an R infrastructure package for operational taxonomic unit data', 'Beck, Daniel|Settles, Matt|Foster, James A', 'Department of Biological Sciences, University of Idaho, Moscow, ID 83843, USA.|Department of Biological Sciences, University of Idaho, Moscow, ID 83843, USA.|Department of Biological Sciences, University of Idaho, Moscow, ID 83843, USA.', 'United States|United States|United States', 0, 'Classification|Cluster Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr196', '21498398', 'OTUbase is an R package designed to facilitate the analysis of operational taxonomic unit (OTU) data and sequence classification (taxonomic) data. Currently there are programs that will cluster sequence data into OTUs and/or classify sequence data into known taxonomies. However, there is a need for software that can take the summarized output of these programs and organize it into easily accessed and manipulated formats. OTUbase provides this structure and organization within R, to allow researchers to easily manipulate the data with the rich library of R packages currently available for additional analysis.'),
(76160, 2011, 'bio', 'Normalizing bead-based microRNA expression data: a measurement error model-based approach', 'Wang, Bin|Wang, Xiao-Feng|Xi, Yaguang', 'Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA. bwang@jaguar1.usouthal.edu|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA. bwang@jaguar1.usouthal.edu|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA. bwang@jaguar1.usouthal.edu', 'United States|United States|United States', 0, 'Humans|Likelihood Functions|MicroRNAs|Models, Biological|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction', 'Journal Article', '10.1093/bioinformatics/btr180', '21498399', 'Compared with complementary DNA (cDNA) or messenger RNA (mRNA) microarray data, microRNA (miRNA) microarray data are harder to normalize due to the facts that the total number of miRNAs is small, and that the majority of miRNAs usually have low expression levels. In bead-based microarrays, the hybridization is completed in several pools. As a result, the number of miRNAs tested in each pool is even smaller, which poses extra difficulty to intrasample normalization and ultimately affects the quality of the final profiles assembled from various pools. In this article, we consider a measurement error model-based method for bead-based microarray intrasample normalization.'),
(76161, 2011, 'bio', 'BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data', 'Yu, Guoqiang|Zhang, Bai|Bova, G Steven|Xu, Jianfeng|Shih, Ie-Ming|Wang, Yue', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|DNA Copy Number Variations|DNA, Neoplasm|Female|Genomics|Humans|Male|Mutation|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Polymorphism, Single Nucleotide|Prostatic Neoplasms|Sequence Deletion|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr183', '21498400', 'Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples.'),
(76162, 2011, 'bio', 'datPAV--an online processing, analysis and visualization tool for exploratory investigation of experimental data', 'Biswas, Ambarish|Rao, Raghuraj|Umashankar, Shivshankar|Mynampati, Kalyan C|Reuben, Sheela|Parab, Gauri|Swarup, Sanjay', 'Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.|Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 3, 'Computer Graphics|Metabolomics|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1093/bioinformatics/btr179', '21498401', 'Data processing, analysis and visualization (datPAV) is an exploratory tool that allows experimentalist to quickly assess the general characteristics of the data. This platform-independent software is designed as a generic tool to process and visualize data matrices. This tool explores organization of the data, detect errors and support basic statistical analyses. Processed data can be reused whereby different step-by-step data processing/analysis workflows can be created to carry out detailed investigation. The visualization option provides publication-ready graphics. Applications of this tool are demonstrated at the web site for three cases of metabolomics, environmental and hydrodynamic data analysis.'),
(76163, 2011, 'bio', 'MAISTAS: a tool for automatic structural evaluation of alternative splicing products', 'Floris, Matteo|Raimondo, Domenico|Leoni, Guido|Orsini, Massimiliano|Marcatili, Paolo|Tramontano, Anna', 'CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.|CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.|CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.|CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.|CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.|CRS4-Bioinformatics Laboratory, c/o Sardegna Ricerche Scientific Park, Pula, 09010 Cagliari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Alternative Splicing|Genome, Human|Humans|Models, Molecular|Protein Isoforms|Software|Structural Homology, Protein|Voltage-Dependent Anion Channels', 'Journal Article', '10.1093/bioinformatics/btr198', '21498402', 'Analysis of the human genome revealed that the amount of transcribed sequence is an order of magnitude greater than the number of predicted and well-characterized genes. A sizeable fraction of these transcripts is related to alternatively spliced forms of known protein coding genes. Inspection of the alternatively spliced transcripts identified in the pilot phase of the ENCODE project has clearly shown that often their structure might substantially differ from that of other isoforms of the same gene, and therefore that they might perform unrelated functions, or that they might even not correspond to a functional protein. Identifying these cases is obviously relevant for the functional assignment of gene products and for the interpretation of the effect of variations in the corresponding proteins.'),
(76164, 2011, 'bio', 'PathScan: a tool for discerning mutational significance in groups of putative cancer genes', 'Wendl, Michael C|Wallis, John W|Lin, Ling|Kandoth, Cyriac|Mardis, Elaine R|Wilson, Richard K|Ding, Li', 'The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu|The Genome Institute, Washington University, St Louis, MO 63108, USA. mwendl@wustl.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Adenocarcinoma|Adenocarcinoma of Lung|Genes, Neoplasm|Genomics|Humans|Lung Neoplasms|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btr193', '21498403', 'The expansion of cancer genome sequencing continues to stimulate development of analytical tools for inferring relationships between somatic changes and tumor development. Pathway associations are especially consequential, but existing algorithms are demonstrably inadequate.'),
(76165, 2011, 'bio', 'Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions', 'He, Dan|Hormozdiari, Farhad|Furlotte, Nicholas|Eskin, Eleazar', 'Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Chromosome Mapping|DNA|DNA Copy Number Variations|Genomics|Mice|Repetitive Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btr169', '21505028', 'Structural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads.'),
(76166, 2011, 'bio', 'Signaling gateway molecule pages--a data model perspective', 'Dinasarapu, Ashok Reddy|Saunders, Brian|Ozerlat, Iley|Azam, Kenan|Subramaniam, Shankar', 'Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States', 20, 'Databases, Protein|Internet|Intracellular Signaling Peptides and Proteins|Protein Processing, Post-Translational|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btr190', '21505029', 'The Signaling Gateway Molecule Pages (SGMP) database provides highly structured data on proteins which exist in different functional states participating in signal transduction pathways. A molecule page starts with astate of a native protein, without any modification and/or interactions. New states are formed with every post-translational modification or interaction with one or more proteins, small molecules or class molecules and with each change in cellular location. State transitions are caused by a combination of one or more modifications, interactions and translocations which then might be associated with one or more biological processes. In a characterized biological state, a molecule can function as one of several entities or their combinations, including channel, receptor, enzyme, transcription factor and transporter. We have also exported SGMP data to the Biological Pathway Exchange (BioPAX) and Systems Biology Markup Language (SBML) as well as in our custom XML.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76167, 2011, 'bio', 'KalignP: improved multiple sequence alignments using position specific gap penalties in Kalign2', 'Shu, Nanjiang|Elofsson, Arne', 'Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Center for Biomembrane Research, Swedish e-science Research Center, Stockholm University, 106 91 Stockholm, Sweden.|Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Center for Biomembrane Research, Swedish e-science Research Center, Stockholm University, 106 91 Stockholm, Sweden.', 'Sweden|Sweden', 5, 'Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btr235', '21505030', 'Kalign2 is one of the fastest and most accurate methods for multiple alignments. However, in contrast to other methods Kalign2 does not allow externally supplied position specific gap penalties. Here, we present a modification to Kalign2, KalignP, so that it accepts such penalties. Further, we show that KalignP using position specific gap penalties obtained from predicted secondary structures makes steady improvement over Kalign2 when tested on Balibase 3.0 as well as on a dataset derived from Pfam-A seed alignments.'),
(76168, 2011, 'bio', 'VIZ-GRAIL: visualizing functional connections across disease loci', 'Raychaudhuri, Soumya', 'Divisions of Genetics and Rheumatology, Brigham and Womens Hospital, Boston, MA 02115, USA. soumya@broadinstitute.org', 'United States', 12, 'Algorithms|Computer Graphics|Disease|Genes|Genetic Loci|Software', 'Journal Article', '10.1093/bioinformatics/btr185', '21505031', 'As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci. Previously, we defined a statistical strategy, Gene Relationships Across Implicated Loci (GRAIL), to identify whether pair-wise gene relationships defined using PubMed text similarity are enriched across loci. Here, we have implemented VIZ-GRAIL, a software tool to display those relationships and to depict the underlying biological patterns.'),
(76169, 2011, 'bio', 'Mcheza: a workbench to detect selection using dominant markers', 'Antao, Tiago|Beaumont, Mark A', 'Molecular and Biochemical Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK. tra@popgen.eu|Molecular and Biochemical Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK. tra@popgen.eu', 'United Kingdom|United Kingdom', 72, 'Amplified Fragment Length Polymorphism Analysis|Genetic Markers|Genome|Oligonucleotide Array Sequence Analysis|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btr253', '21505032', 'Dominant markers (DArTs and AFLPs) are commonly used for genetic analysis in the fields of evolutionary genetics, ecology and conservation of genetic resources. The recent prominence of these markers has coincided with renewed interest in detecting the effects of local selection and adaptation at the level of the genome.'),
(76170, 2011, 'bio', 'Rgtsp: a generalized top scoring pairs package for class prediction', 'Popovici, Vlad|Budinská, Eva|Delorenzi, Mauro', 'Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch', 'Switzerland|Switzerland|Switzerland', 5, 'Classification|Gene Expression Profiling|Receptors, Estrogen|Software', 'Journal Article', '10.1093/bioinformatics/btr233', '21505033', 'A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance.'),
(76171, 2011, 'bio', 'Systems biology model repository for macrophage pathway simulation', 'Nagasaki, Masao|Saito, Ayumu|Fujita, André|Tremmel, Georg|Ueno, Kazuko|Ikeda, Emi|Jeong, Euna|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. masao@hgc.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Computer Simulation|Knowledge Bases|Lipopolysaccharides|Macrophage Activation|Macrophages|Models, Immunological|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr173', '21505034', 'The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis.'),
(76172, 2011, 'bio', 'FR-HIT, a very fast program to recruit metagenomic reads to homologous reference genomes', 'Niu, Beifang|Zhu, Zhengwei|Fu, Limin|Wu, Sitao|Li, Weizhong', 'Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA.|Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA.|Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA.|Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA.|Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Genome|Metagenomics|Reference Standards|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr252', '21505035', 'Fragment recruitment, a process of aligning sequencing reads to reference genomes, is a crucial step in metagenomic data analysis. The available sequence alignment programs are either slow or insufficient for recruiting metagenomic reads. We implemented an efficient algorithm, FR-HIT, for fragment recruitment. We applied FR-HIT and several other tools including BLASTN, MegaBLAST, BLAT, LAST, SSAHA2, SOAP2, BWA and BWA-SW to recruit four metagenomic datasets from different type of sequencers. On average, FR-HIT and BLASTN recruited significantly more reads than other programs, while FR-HIT is about two orders of magnitude faster than BLASTN. FR-HIT is slower than the fastest SOAP2, BWA and BWA-SW, but it recruited 1-5 times more reads.'),
(76173, 2011, 'bio', 'A reference dataset for the analyses of membrane protein secondary structures and transmembrane residues using circular dichroism spectroscopy', 'Abdul-Gader, Ali|Miles, Andrew John|Wallace, B A', 'Department of Crystallography, Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK.', 'United Kingdom|United Kingdom|United Kingdom', 80, 'Circular Dichroism|Databases, Protein|Membrane Proteins|Protein Structure, Secondary|Synchrotrons', 'Journal Article', '10.1093/bioinformatics/btr234', '21505036', 'Empirical analyses of protein secondary structures based on circular dichroism (CD) and synchrotron radiation circular dichroism (SRCD) spectroscopic data rely on the availability of reference datasets comprised of spectra of relevant proteins, whose crystal structures have been determined. Datasets comprised of only soluble proteins have not proven suitable for analysing the spectra of membrane proteins.'),
(76174, 2011, 'bio', 'A graphical interface for the FoldX forcefield', 'Van Durme, Joost|Delgado, Javier|Stricher, Francois|Serrano, Luis|Schymkowitz, Joost|Rousseau, Frederic', 'VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.|VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.|VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.|VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.|VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.|VIB Switch Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 173, 'Computer Graphics|Mutagenesis|Protein Conformation|Protein Folding|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr254', '21505037', 'A graphical user interface for the FoldX protein design program has been developed as a plugin for the YASARA molecular graphics suite. The most prominent FoldX commands such as free energy difference upon mutagenesis and interaction energy calculations can now be run entirely via a windowed menu system and the results are immediately shown on screen.'),
(76175, 2011, 'bio', 'CytoModeler: a tool for bridging large-scale network analysis and dynamic quantitative modeling', 'Xia, Tian|Van Hemert, John|Dickerson, Julie A', 'Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA. netscape@iastate.edu|Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA. netscape@iastate.edu|Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA. netscape@iastate.edu', 'United States|United States|United States', 0, 'Computer Simulation|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr150', '21511714', 'CytoModeler is an open-source Java application based on the Cytoscape platform. It integrates large-scale network analysis and quantitative modeling by combining omics analysis on the Cytoscape platform, access to deterministic and stochastic simulators, and static and dynamic network context visualizations of simulation results.'),
(76176, 2011, 'bio', 'PONDEROSA, an automated 3D-NOESY peak picking program, enables automated protein structure determination', 'Lee, Woonghee|Kim, Jin Hae|Westler, William M|Markley, John L', 'National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu|National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI 53706, USA. whlee@nmrfam.wisc.edu', 'United States|United States|United States|United States', 0, 'Escherichia coli Proteins|Humans|Iron-Sulfur Proteins|Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Software|Ubiquitin', 'Journal Article', '10.1093/bioinformatics/btr200', '21511715', 'PONDEROSA (Peak-picking Of Noe Data Enabled by Restriction of Shift Assignments) accepts input information consisting of a protein sequence, backbone and sidechain NMR resonance assignments, and 3D-NOESY ((13)C-edited and/or (15)N-edited) spectra, and returns assignments of NOESY crosspeaks, distance and angle constraints, and a reliable NMR structure represented by a family of conformers. PONDEROSA incorporates and integrates external software packages (TALOS+, STRIDE and CYANA) to carry out different steps in the structure determination. PONDEROSA implements internal functions that identify and validate NOESY peak assignments and assess the quality of the calculated three-dimensional structure of the protein. The robustness of the analysis results from PONDEROSAs hierarchical processing steps that involve iterative interaction among the internal and external modules. PONDEROSA supports a variety of input formats: SPARKY assignment table (.shifts) and spectrum file formats (.ucsf), XEASY proton file format (.prot), and NMR-STAR format (.star). To demonstrate the utility of PONDEROSA, we used the package to determine 3D structures of two proteins: human ubiquitin and Escherichia coli iron-sulfur scaffold protein variant IscU(D39A). The automatically generated structural constraints and ensembles of conformers were as good as or better than those determined previously by much less automated means.'),
(76177, 2011, 'bio', 'Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models', 'Seifert, Michael|Strickert, Marc|Schliep, Alexander|Grosse, Ivo', 'Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. seifert@ipk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. seifert@ipk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. seifert@ipk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. seifert@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany', 13, 'Breast Neoplasms|Chromosome Mapping|Female|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Markov Chains|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btr199', '21511716', 'Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.'),
(76178, 2011, 'bio', 'A detailed investigation of accessibilities around target sites of siRNAs and miRNAs', 'Kiryu, Hisanori|Terai, Goro|Imamura, Osamu|Yoneyama, Hiroyuki|Suzuki, Kenji|Asai, Kiyoshi', 'Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp|Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp|Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp|Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp|Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp|Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 30, 'Animals|Fibroblasts|Gene Expression Regulation|HeLa Cells|Humans|Mice|MicroRNAs|Nucleic Acid Conformation|RNA, Messenger|RNA, Small Interfering|RNA-Induced Silencing Complex|Rats|Sequence Analysis, RNA|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btr276', '21531769', 'The importance of RNA sequence analysis has been increasing since the discovery of various types of non-coding RNAs transcribed in animal cells. Conventional RNA sequence analyses have mainly focused on structured regions, which are stabilized by the stacking energies acting on adjacent base pairs. On the other hand, recent findings regarding the mechanisms of small interfering RNAs (siRNAs) and transcription regulation by microRNAs (miRNAs) indicate the importance of analyzing accessible regions where no base pairs exist. So far, relatively few studies have investigated the nature of such regions.'),
(76179, 2011, 'bio', 'parmigene--a parallel R package for mutual information estimation and gene network reconstruction', 'Sales, Gabriele|Romualdi, Chiara', 'Department of Statistical Sciences, University of Padova, 35121 Padova, Italy.|Department of Statistical Sciences, University of Padova, 35121 Padova, Italy.', 'Italy|Italy', 53, 'Algorithms|B-Lymphocytes|Gene Expression Profiling|Gene Regulatory Networks|Genes, myc|Humans|Image Processing, Computer-Assisted|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btr274', '21531770', 'Inferring large transcriptional networks using mutual information has been shown to be effective in several experimental setup. Unfortunately, this approach has two main drawbacks: (i) several mutual information estimators are prone to biases and (ii) available software still has large computational costs when processing thousand of genes.'),
(76180, 2011, 'bio', 'DREME: motif discovery in transcription factor ChIP-seq data', 'Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia. t.bailey@uq.edu.au', 'Australia', 503, 'Algorithms|Animals|Cell Line|Chromatin Immunoprecipitation|Erythrocytes|Humans|Mice|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr261', '21543442', 'Transcription factor (TF) ChIP-seq datasets have particular characteristics that provide unique challenges and opportunities for motif discovery. Most existing motif discovery algorithms do not scale well to such large datasets, or fail to report many motifs associated with cofactors of the ChIP-ed TF.'),
(76181, 2011, 'bio', 'Improved similarity scores for comparing motifs', 'Tanaka, Emi|Bailey, Timothy|Grant, Charles E|Noble, William Stafford|Keich, Uri', 'School of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Bayes Theorem|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr257', '21543443', 'A question that often comes up after applying a motif finder to a set of co-regulated DNA sequences is whether the reported putative motif is similar to any known motif. While several tools have been designed for this task, Habib et al. pointed out that the scores that are commonly used for measuring similarity between motifs do not distinguish between a good alignment of two informative columns (say, all-A) and one of two uninformative columns. This observation explains why tools such as Tomtom occasionally return an alignment of uninformative columns which is clearly spurious. To address this problem, Habib et al. suggested a new score [Bayesian Likelihood 2-Component (BLiC)] which uses a Bayesian information criterion to penalize matches that are also similar to the background distribution.'),
(76182, 2011, 'bio', 'Multiple-rule bias in the comparison of classification rules', 'Yousefi, Mohammadmahdi R|Hua, Jianping|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.', 'United States|United States|United States', 0, 'Classification|Computational Biology|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btr262', '21546390', 'There is growing discussion in the bioinformatics community concerning overoptimism of reported results. Two approaches contributing to overoptimism in classification are (i) the reporting of results on datasets for which a proposed classification rule performs well and (ii) the comparison of multiple classification rules on a single dataset that purports to show the advantage of a certain rule.'),
(76183, 2011, 'bio', 'Grid computing for improving conformational sampling in NMR structure calculation', 'Mareuil, Fabien|Blanchet, Christophe|Malliavin, Thérèse E|Nilges, Michael', 'Unité de Bioinformatique Structurale, CNRS URA 2185, Institut Pasteur 25-28 rue du Dr Roux, F-75724 Paris Cedex 15, France.|Unité de Bioinformatique Structurale, CNRS URA 2185, Institut Pasteur 25-28 rue du Dr Roux, F-75724 Paris Cedex 15, France.|Unité de Bioinformatique Structurale, CNRS URA 2185, Institut Pasteur 25-28 rue du Dr Roux, F-75724 Paris Cedex 15, France.|Unité de Bioinformatique Structurale, CNRS URA 2185, Institut Pasteur 25-28 rue du Dr Roux, F-75724 Paris Cedex 15, France.', 'France|France|France|France', 2, 'Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btr255', '21546391', 'Methods for automatic nuclear magnetic resonance (NMR) structure determination need to face a high level of ambiguity encountered in NMR spectra recorded by solid-state NMR and by solution NMR of partially unfolded proteins, leading to time-consuming calculations. The software package Ambiguous Restraints for Iterative Assignment (ARIA) allows for straightforward parallelization of the calculation, as the conformers can be generated in parallel on many nodes.'),
(76184, 2011, 'bio', 'Boulder ALignment Editor (ALE): a web-based RNA alignment tool', 'Stombaugh, Jesse|Widmann, Jeremy|McDonald, Daniel|Knight, Rob', 'Department of Chemistry and Biochemistry, University of Colorado, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado, CO, USA.', 'United States|United States|United States|United States', 0, 'Base Pairing|Internet|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr258', '21546392', 'The explosion of interest in non-coding RNAs, together with improvements in RNA X-ray crystallography, has led to a rapid increase in RNA structures at atomic resolution from 847 in 2005 to 1900 in 2010. The success of whole-genome sequencing has led to an explosive growth of unaligned homologous sequences. Consequently, there is a compelling and urgent need for user-friendly tools for producing structure-informed RNA alignments. Most alignment software considers the primary sequence alone, some specialized alignment software can also include Watson-Crick base pairs, but none adequately addresses the needs introduced by the rapid influx of both sequence and structural data. Therefore, we have developed the Boulder ALignment Editor (ALE), which is a web-based RNA alignment editor, designed for editing and assessing alignments using structural information. Some features of BoulderALE include the annotation and evaluation of an alignment based on isostericity of Watson-Crick and non-Watson-Crick base pairs, along with the collapsing (horizontally and vertically) of the alignment, while maintaining the ability to edit the alignment.'),
(76185, 2011, 'bio', 'Molecular signatures database (MSigDB) 30', 'Liberzon, Arthur|Subramanian, Aravind|Pinchback, Reid|Thorvaldsdóttir, Helga|Tamayo, Pablo|Mesirov, Jill P', 'Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Genomics|Internet|Molecular Sequence Annotation', 'Journal Article', '10.1093/bioinformatics/btr260', '21546393', 'Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets.'),
(76186, 2011, 'bio', 'ReMark: an automatic program for clustering orthologs flexibly combining a Recursive and a Markov clustering algorithms', 'Kim, Kangseok|Kim, Wonil|Kim, Sunshin', 'Department of Knowledge Information Security, Ajou University, Suwon, Korea.|Department of Knowledge Information Security, Ajou University, Suwon, Korea.|Department of Knowledge Information Security, Ajou University, Suwon, Korea.', '-|-|-', 10, 'Algorithms|Cluster Analysis|Genome|Markov Chains|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr259', '21546394', 'ReMark is a fully automatic tool for clustering orthologs by combining a Recursive and a Markov clustering (MCL) algorithms. The ReMark detects and recursively clusters ortholog pairs through reciprocal BLAST best hits between multiple genomes running software program (RecursiveClustering.java) in the first step. Then, it employs MCL algorithm to compute the clusters (score matrices generated from the previous step) and refines the clusters by adjusting an inflation factor running software program (MarkovClustering.java). This method has two key features. One utilizes, to get more reliable results, the diagonal scores in the matrix of the initial ortholog clusters. Another clusters orthologs flexibly through being controlled naturally by MCL with a selected inflation factor. Users can therefore select the fitting state of orthologous protein clusters by regulating the inflation factor according to their research interests.'),
(76187, 2011, 'bio', 'FusionHunter: identifying fusion transcripts in cancer using paired-end RNA-seq', 'Li, Yang|Chien, Jeremy|Smith, David I|Ma, Jian', 'Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States', 0, 'Gene Fusion|Genes, Neoplasm|Humans|Neoplasms|RNA, Messenger|RNA, Neoplasm|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr265', '21546395', 'Fusion transcripts can be created as a result of genome rearrangement in cancer. Some of them play important roles in carcinogenesis, and can serve as diagnostic and therapeutic targets. With more and more cancer genomes being sequenced by next-generation sequencing technologies, we believe an efficient tool for reliably identifying fusion transcripts will be desirable for many groups.'),
(76188, 2011, 'bio', 'famCNV: copy number variant association for quantitative traits in families', 'Eleftherohorinou, Hariklia|Andersson-Assarsson, Johanna C|Walters, Robin G|El-Sayed Moustafa, Julia S|Coin, Lachlan|Jacobson, Peter|Carlsson, Lena M S|Blakemore, Alexandra I F|Froguel, Philippe|Walley, Andrew J|Falchi, Mario', 'Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'DNA Copy Number Variations|Disease|Family|Gene Frequency|Genome-Wide Association Study|Humans|Phenotype|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btr264', '21546396', 'A program package to enable genome-wide association of copy number variants (CNVs) with quantitative phenotypes in families of arbitrary size and complexity. Intensity signals that act as proxies for the number of copies are modeled in a variance component framework and association with traits is assessed through formal likelihood testing.'),
(76189, 2011, 'bio', 'APOLLO: a quality assessment service for single and multiple protein models', 'Wang, Zheng|Eickholt, Jesse|Cheng, Jianlin', 'Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 58, 'Artificial Intelligence|Models, Molecular|Protein Conformation|Proteins|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btr268', '21546397', 'We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.'),
(76190, 2011, 'bio', 'PLIO: an ontology for formal description of protein-ligand interactions', 'Ivchenko, Olga|Younesi, Erfan|Shahid, Mohammad|Wolf, Antje|Müller, Bernd|Hofmann-Apitius, Martin', 'Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Data Mining|HIV Protease|Ligands|Natural Language Processing|Protein Binding|Proteins|PubMed|Purinergic P1 Receptor Antagonists|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btr256', '21546398', 'Biomedical ontologies have proved to be valuable tools for data analysis and data interoperability. Protein-ligand interactions are key players in drug discovery and development, however, existing public ontologies that describe the knowledge space of biomolecular interactions do not cover all aspects relevant to pharmaceutical modelling and simulation.'),
(76191, 2011, 'bio', 'Clonality: an R package for testing clonal relatedness of two tumors from the same patient based on their genomic profiles', 'Ostrovnaya, Irina|Seshan, Venkatraman E|Olshen, Adam B|Begg, Colin B', 'Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. ostrovni@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. ostrovni@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. ostrovni@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. ostrovni@mskcc.org', 'United States|United States|United States|United States', 0, 'Clone Cells|Genomics|Humans|Loss of Heterozygosity|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btr267', '21546399', 'If a cancer patient develops multiple tumors, it is sometimes impossible to determine whether these tumors are independent or clonal based solely on pathological characteristics. Investigators have studied how to improve this diagnostic challenge by comparing the presence of loss of heterozygosity (LOH) at selected genetic locations of tumor samples, or by comparing genomewide copy number array profiles. We have previously developed statistical methodology to compare such genomic profiles for an evidence of clonality. We assembled the software for these tests in a new R package called Clonality. For LOH profiles, the package contains significance tests. The analysis of copy number profiles includes a likelihood ratio statistic and reference distribution, as well as an option to produce various plots that summarize the results.'),
(76192, 2011, 'bio', 'Mixture models for analysis of the taxonomic composition of metagenomes', 'Meinicke, Peter|Asshauer, Kathrin Petra|Lingner, Thomas', 'Department of Bioinformatics, Institute for Microbiology and Genetics, Georg-August University, Göttingen, Germany.|Department of Bioinformatics, Institute for Microbiology and Genetics, Georg-August University, Göttingen, Germany.|Department of Bioinformatics, Institute for Microbiology and Genetics, Georg-August University, Göttingen, Germany.', 'Germany|Germany|Germany', 47, 'Algorithms|Metagenome|Metagenomics|Models, Genetic|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr266', '21546400', 'Inferring the taxonomic profile of a microbial community from a large collection of anonymous DNA sequencing reads is a challenging task in metagenomics. Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmentary sequences to phylogenetic categories, the accuracy of results largely depends on fragment length. This dependence complicates comparative analysis of data originating from different sequencing platforms or resulting from different preprocessing pipelines.'),
(76193, 2011, 'bio', 'BINOCh: binding inference from nucleosome occupancy changes', 'Meyer, Clifford A|He, Housheng H|Brown, Myles|Liu, X Shirley', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA. cliff@jimmy.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA. cliff@jimmy.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA. cliff@jimmy.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA. cliff@jimmy.harvard.edu', 'United States|United States|United States|United States', 0, 'Cell Line, Tumor|Dihydrotestosterone|Histones|Humans|Nucleosomes|Oligonucleotide Array Sequence Analysis|Receptors, Androgen|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr279', '21551136', 'Transcription factor binding events are frequently associated with a pattern of nucleosome occupancy changes in which nucleosomes flanking the binding site increase in occupancy, while those in the vicinity of the binding site itself are displaced. Genome-wide information on enhancer proximal nucleosome occupancy can be readily acquired using ChIP-seq targeting enhancer-related histone modifications such as H3K4me2. Here, we present a software package, BINOCh that allows biologists to use such data to infer the identity of key transcription factors that regulate the response of a cell to a stimulus or determine a program of differentiation.'),
(76194, 2011, 'bio', 'A cautionary note for retrocopy identification: DNA-based duplication of intron-containing genes significantly contributes to the origination of single exon genes', 'Zhang, Yong E|Vibranovski, Maria D|Krinsky, Benjamin H|Long, Manyuan', 'Department of Ecology and Evolution, The University of Chicago, 1101 E 57th Street, Chicago, IL 60637, USA. zhangy@uchicago.edu|Department of Ecology and Evolution, The University of Chicago, 1101 E 57th Street, Chicago, IL 60637, USA. zhangy@uchicago.edu|Department of Ecology and Evolution, The University of Chicago, 1101 E 57th Street, Chicago, IL 60637, USA. zhangy@uchicago.edu|Department of Ecology and Evolution, The University of Chicago, 1101 E 57th Street, Chicago, IL 60637, USA. zhangy@uchicago.edu', 'United States|United States|United States|United States', 12, 'Animals|Drosophila melanogaster|Evolution, Molecular|Exons|Gene Duplication|Humans|Introns|Male|Retroelements|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr280', '21551137', 'Retrocopies are important genes in the genomes of almost all higher eukaryotes. However, the annotation of such genes is a non-trivial task. Intronless genes have often been considered to be retroposed copies of intron-containing paralogs. Such categorization relies on the implicit premise that alignable regions of the duplicates should be long enough to cover exon-exon junctions of the intron-containing genes, and thus intron loss events can be inferred. Here, we examined the alternative possibility that intronless genes could be generated by partial DNA-based duplication of intron-containing genes in the fruitfly genome.'),
(76195, 2011, 'bio', 'Identification of prokaryotic small proteins using a comparative genomic approach', 'Samayoa, Josue|Yildiz, Fitnat H|Karplus, Kevin', 'Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA. jsamayoa@jhu.edu|Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA. jsamayoa@jhu.edu|Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA. jsamayoa@jhu.edu', 'United States|United States|United States', 0, 'Base Sequence|Codon|Enterobacteriaceae|Escherichia coli|Escherichia coli K12|Escherichia coli Proteins|Genomics|Open Reading Frames|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/btr275', '21551138', 'Accurate prediction of genes encoding small proteins (on the order of 50 amino acids or less) remains an elusive open problem in bioinformatics. Some of the best methods for gene prediction use either sequence composition analysis or sequence similarity to a known protein coding sequence. These methods often fail for small proteins, however, either due to a lack of experimentally verified small protein coding genes or due to the limited statistical significance of statistics on small sequences. Our approach is based upon the hypothesis that true small proteins will be under selective pressure for encoding the particular amino acid sequence, for ease of translation by the ribosome and for structural stability. This stability can be achieved either independently or as part of a larger protein complex. Given this assumption, it follows that small proteins should display conserved local protein structure properties much like larger proteins. Our method incorporates neural-net predictions for three local structure alphabets within a comparative genomic approach using a genomic alignment of 22 closely related bacteria genomes to generate predictions for whether or not a given open reading frame (ORF) encodes for a small protein.'),
(76196, 2011, 'bio', 'MPEA--metabolite pathway enrichment analysis', 'Kankainen, Matti|Gopalacharyulu, Peddinti|Holm, Liisa|Oresic, Matej', 'VTT Technical Research Centre of Finland, Espooi, Finland. matti.kankainen@helsinki.fi|VTT Technical Research Centre of Finland, Espooi, Finland. matti.kankainen@helsinki.fi|VTT Technical Research Centre of Finland, Espooi, Finland. matti.kankainen@helsinki.fi|VTT Technical Research Centre of Finland, Espooi, Finland. matti.kankainen@helsinki.fi', 'Finland|Finland|Finland|Finland', 58, 'Adipose Tissue|Body Weight|Gas Chromatography-Mass Spectrometry|Gene Expression Profiling|Humans|Metabolic Networks and Pathways|Metabolome', 'Journal Article', '10.1093/bioinformatics/btr278', '21551139', 'We present metabolite pathway enrichment analysis (MPEA) for the visualization and biological interpretation of metabolite data at the system level. Our tool follows the concept of gene set enrichment analysis (GSEA) and tests whether metabolites involved in some predefined pathway occur towards the top (or bottom) of a ranked query compound list. In particular, MPEA is designed to handle many-to-many relationships that may occur between the query compounds and metabolite annotations. For a demonstration, we analysed metabolite profiles of 14 twin pairs with differing body weights. MPEA found significant pathways from data that had no significant individual query compounds, its results were congruent with those discovered from transcriptomics data and it detected more pathways than the competing metabolic pathway method did.'),
(76197, 2011, 'bio', 'Application of the Bayesian MMSE estimator for classification error to gene expression microarray data', 'Dalton, Lori A|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. ldalton@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. ldalton@tamu.edu', 'United States|United States', 0, 'Bayes Theorem|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Humans|Normal Distribution|Oligonucleotide Array Sequence Analysis|Probability', 'Journal Article', '10.1093/bioinformatics/btr272', '21551140', 'With the development of high-throughput genomic and proteomic technologies, coupled with the inherent difficulties in obtaining large samples, biomedicine faces difficult small-sample classification issues, in particular, error estimation. Most popular error estimation methods are motivated by intuition rather than mathematical inference. A recently proposed error estimator based on Bayesian minimum mean square error estimation places error estimation in an optimal filtering framework. In this work, we examine the application of this error estimator to gene expression microarray data, including the suitability of the Gaussian model with normal-inverse-Wishart priors and how to find prior probabilities.'),
(76198, 2011, 'bio', 'The strength of chemical linkage as a criterion for pruning metabolic graphs', 'Zhou, Wanding|Nakhleh, Luay', 'Department of Bioengineering, Rice University, Houston, TX, USA. wz4@rice.edu|Department of Bioengineering, Rice University, Houston, TX, USA. wz4@rice.edu', 'United States|United States', 2, 'Escherichia coli|Escherichia coli Proteins|Metabolic Networks and Pathways|Metabolome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btr271', '21551141', 'A metabolic graph represents the connectivity patterns of a metabolic system, and provides a powerful framework within which the organization of metabolic reactions can be analyzed and elucidated. A common practice is to prune (i.e. remove nodes and edges) the metabolic graph prior to any analysis in order to eliminate confounding signals from the representation. Currently, this pruning process is carried out in an ad hoc fashion, resulting in discrepancies and ambiguities across studies.'),
(76199, 2011, 'bio', 'TVNViewer: an interactive visualization tool for exploring networks that change over time or space', 'Curtis, Ross E|Yuen, Amos|Song, Le|Goyal, Anuj|Xing, Eric P', 'Joint CMU-Pitt PhD Program in Computational Biology, Lane Center for Computational Biology, School of Computer Science, Language Technologies Institute and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Joint CMU-Pitt PhD Program in Computational Biology, Lane Center for Computational Biology, School of Computer Science, Language Technologies Institute and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Joint CMU-Pitt PhD Program in Computational Biology, Lane Center for Computational Biology, School of Computer Science, Language Technologies Institute and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Joint CMU-Pitt PhD Program in Computational Biology, Lane Center for Computational Biology, School of Computer Science, Language Technologies Institute and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Joint CMU-Pitt PhD Program in Computational Biology, Lane Center for Computational Biology, School of Computer Science, Language Technologies Institute and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Humans|Internet|Metabolic Networks and Pathways|Plants|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr273', '21551142', 'The relationship between genes and proteins is a dynamic relationship that changes across time and differs in different cells. The study of these differences can reveal various insights into biological processes and disease progression, especially with the aid of proper tools for network visualization. Toward this purpose, we have developed TVNViewer, a novel visualization tool, which is specifically designed to aid in the exploration and analysis of dynamic networks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76200, 2011, 'bio', 'A multiple network learning approach to capture system-wide condition-specific responses', 'Roy, Sushmita|Werner-Washburne, Margaret|Lane, Terran', 'Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. sroy@broadinstitute.org|Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. sroy@broadinstitute.org|Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. sroy@broadinstitute.org', 'Mexico|Mexico|Mexico', 6, 'Artificial Intelligence|Computational Biology|Computer Simulation|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btr270', '21551143', 'Condition-specific networks capture system-wide behavior under varying conditions such as environmental stresses, cell types or tissues. These networks frequently comprise parts that are unique to each condition, and parts that are shared among related conditions. Existing approaches for learning condition-specific networks typically identify either only differences or only similarities across conditions. Most of these approaches first learn networks per condition independently, and then identify similarities and differences in a post-learning step. Such approaches do not exploit the shared information across conditions during network learning.'),
(76201, 2011, 'bio', 'Pathway analysis of high-throughput biological data within a Bayesian network framework', 'Isci, Senol|Ozturk, Cengizhan|Jones, Jon|Otu, Hasan H', 'Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey.|Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey.', 'Turkey|Turkey|Turkey|Turkey', 0, 'Algorithms|Bayes Theorem|Carcinoma, Renal Cell|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Screening Assays|Humans|Kidney Neoplasms|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btr269', '21551144', 'Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network.'),
(76202, 2011, 'bio', 'Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences', 'Lee, Tzong-Yi|Lin, Zong-Qing|Hsieh, Sheng-Jen|Bretaña, Neil Arvin|Lu, Cheng-Tsung', 'Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yzu.edu.tw', '-|-|-|-|-', 0, 'Amino Acid Motifs|Cluster Analysis|Humans|Hydrophobic and Hydrophilic Interactions|Phosphorylation|Plant Proteins|Plants|Protein Kinases|Protein Processing, Post-Translational|Protein Sorting Signals|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btr291', '21551145', 'Bioinformatics research often requires conservative analyses of a group of sequences associated with a specific biological function (e.g. transcription factor binding sites, micro RNA target sites or protein post-translational modification sites). Due to the difficulty in exploring conserved motifs on a large-scale sequence data involved with various signals, a new method, MDDLogo, is developed. MDDLogo applies maximal dependence decomposition (MDD) to cluster a group of aligned signal sequences into subgroups containing statistically significant motifs. In order to extract motifs that contain a conserved biochemical property of amino acids in protein sequences, the set of 20 amino acids is further categorized according to their physicochemical properties, e.g. hydrophobicity, charge or molecular size. MDDLogo has been demonstrated to accurately identify the kinase-specific substrate motifs in 1221 human phosphorylation sites associated with seven well-known kinase families from Phospho.ELM. Moreover, in a set of plant phosphorylation data-lacking kinase information, MDDLogo has been applied to help in the investigation of substrate motifs of potential kinases and in the improvement of the identification of plant phosphorylation sites with various substrate specificities. In this study, MDDLogo is comparable with another well-known motif discover tool, Motif-X.'),
(76203, 2011, 'bio', 'Sim4db and Leaff: utilities for fast batch spliced alignment and sequence indexing', 'Walenz, Brian|Florea, Liliana', 'The J. Craig Venter Institute, Rockville, MD 20850, USA.|The J. Craig Venter Institute, Rockville, MD 20850, USA.', 'United States|United States', 0, 'Animals|Base Sequence|Biological Evolution|DNA, Complementary|Genome|Humans|RNA Splicing|Sequence Alignment|Software|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btr285', '21551146', 'The large number of genomes that will be sequenced will need to be annotated with genes and other functional features. Aligning gene sequences from a related species to the target genome is an economical and highly reliable method to identify genes, unfortunately, existing tools have been lacking in sensitivity and speed. A program we reported, sim4cc, was shown to be highly accurate but is limited to comparing one cDNA with one genomic sequence. We present here an optimization of the tool, implemented in the packages sim4db and leaff. The new tool performs batch alignments of cDNA and genomic sequences in a fraction of the time required by its predecessor, and thus is very well suited for genome-wide analyses.'),
(76204, 2011, 'bio', 'The role of indirect connections in gene networks in predicting function', 'Gillis, Jesse|Pavlidis, Paul', 'Centre for High-Throughput Biology and Department of Psychiatry, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada.|Centre for High-Throughput Biology and Department of Psychiatry, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada.', 'Canada|Canada', 38, 'Algorithms|Animals|Gene Expression|Gene Regulatory Networks|Humans|Mice|Proteins|Yeasts', 'Journal Article', '10.1093/bioinformatics/btr288', '21551147', 'Gene networks have been used widely in gene function prediction algorithms, many based on complex extensions of the guilt by association principle. We sought to provide a unified explanation for the performance of gene function prediction algorithms in exploiting network structure and thereby simplify future analysis.'),
(76205, 2011, 'bio', 'Genome-wide DNA sequence polymorphisms facilitate nucleosome positioning in yeast', 'Dai, Zhiming|Dai, Xianhua|Xiang, Qian', 'Department of Electronic, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China. zhimdai@gmail.com|Department of Electronic, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China. zhimdai@gmail.com|Department of Electronic, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China. zhimdai@gmail.com', 'China|China|China', 0, 'Chromatin Assembly and Disassembly|Genome-Wide Association Study|Nucleosomes|Polymorphism, Genetic|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btr290', '21551148', 'The intrinsic DNA sequence is an important determinant of nucleosome positioning. Some DNA sequence patterns can facilitate nucleosome formation, while others can inhibit nucleosome formation. Nucleosome positioning influences the overall rate of sequence evolution. However, its impacts on specific patterns of sequence evolution are still poorly understood.'),
(76206, 2011, 'bio', 'An MCMC algorithm for detecting short adjacent repeats shared by multiple sequences', 'Li, Qiwei|Fan, Xiaodan|Liang, Tong|Li, Shuo-Yen R', 'Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, 'Algorithms|Base Sequence|Bayes Theorem|Computational Biology|Markov Chains|Models, Statistical|Monte Carlo Method|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btr287', '21551149', 'Repeats detection problems are traditionally formulated as string matching or signal processing problems. They cannot readily handle gaps between repeat units and are incapable of detecting repeat patterns shared by multiple sequences. This study detects short adjacent repeats with interunit insertions from multiple sequences. For biological sequences, such studies can shed light on molecular structure, biological function and evolution.'),
(76207, 2011, 'bio', 'Creating views on integrated multidomain data', 'Rohn, Hendrik|Klukas, Christian|Schreiber, Falk', 'Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. rohn@lpk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. rohn@lpk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. rohn@lpk-gatersleben.de', 'Germany|Germany|Germany', 0, 'Hordeum|Information Storage and Retrieval|Seeds|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr282', '21551150', 'Modern data acquisition methods in biology allow the procurement of different types of data in increasing quantity, facilitating a comprehensive view of biological systems. As data are usually gathered and interpreted by separate domain scientists, it is hard to grasp multidomain properties and structures. Consequently, there is a need for the integration of biological data from different sources and of different types in one application, providing various visualization approaches.'),
(76208, 2011, 'bio', 'Integrative gene network construction for predicting a set of complementary prostate cancer genes', 'Ahn, Jaegyoon|Yoon, Youngmi|Park, Chihyun|Shin, Eunji|Park, Sanghyun', 'Department of Computer Science, Yonsei University, Seoul, South Korea.|Department of Computer Science, Yonsei University, Seoul, South Korea.|Department of Computer Science, Yonsei University, Seoul, South Korea.|Department of Computer Science, Yonsei University, Seoul, South Korea.|Department of Computer Science, Yonsei University, Seoul, South Korea.', '-|-|-|-|-', 0, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Male|Neoplasms|Oligonucleotide Array Sequence Analysis|Prognosis|Prostatic Neoplasms|Proteins', 'Journal Article', '10.1093/bioinformatics/btr283', '21551151', 'Diagnosis and prognosis of cancer and understanding oncogenesis within the context of biological pathways is one of the most important research areas in bioinformatics. Recently, there have been several attempts to integrate interactome and transcriptome data to identify subnetworks that provide limited interpretations of known and candidate cancer genes, as well as increase classification accuracy. However, these studies provide little information about the detailed roles of identified cancer genes.'),
(76209, 2011, 'bio', 'Model building and intelligent acquisition with application to protein subcellular location classification', 'Jackson, C|Glory-Afshar, E|Murphy, R F|Kovacevic, J', 'Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 9, 'Algorithms|Cells|Cellular Structures|Likelihood Functions|Models, Biological|Photobleaching|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr286', '21558154', 'We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired.'),
(76210, 2011, 'bio', 'Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method', 'Jia, Gengjie|Stephanopoulos, Gregory N|Gunawan, Rudiyanto', 'Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 117576.|Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 117576.|Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 117576.', 'Singapore|Singapore|Singapore', 0, 'Kinetics|Lactococcus lactis|Metabolic Networks and Pathways|Metabolome|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btr293', '21558155', 'Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints.'),
(76211, 2011, 'bio', 'Genome annotation test with validation on transcription start site and ChIP-Seq for Pol-II binding data', 'Bedo, Justin|Kowalczyk, Adam', 'National ICT Australia, Victoria Research Laboratories, The University of Melbourne, VIC 3010, Australia. justin.bedo@nicta.com.au|National ICT Australia, Victoria Research Laboratories, The University of Melbourne, VIC 3010, Australia. justin.bedo@nicta.com.au', 'Australia|Australia', 0, 'Algorithms|Artificial Intelligence|Binding Sites|Chromatin Immunoprecipitation|DNA Polymerase II|Genome, Human|Genomics|Humans|Molecular Sequence Annotation|Promoter Regions, Genetic|STAT1 Transcription Factor|Sequence Analysis, DNA|Software|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btr263', '21558156', 'Many ChIP-Seq experiments are aimed at developing gold standards for determining the locations of various genomic features such as transcription start or transcription factor binding sites on the whole genome. Many such pioneering experiments lack rigorous testing methods and adequate gold standard annotations to compare against as they themselves are the most reliable source of empirical data available. To overcome this problem, we propose a self-consistency test whereby a dataset is tested against itself. It relies on a supervised machine learning style protocol for in silico annotation of a genome and accuracy estimation to guarantee, at least, self-consistency.'),
(76212, 2011, 'bio', 'Model-based gene set analysis for Bioconductor', 'Bauer, Sebastian|Robinson, Peter N|Gagneur, Julien', 'Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.', 'Germany|Germany|Germany', 26, 'Algorithms|Gene Expression Profiling|Models, Genetic|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btr296', '21561920', 'Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fishers exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach.'),
(76213, 2011, 'bio', 'Modular model of TNFalpha cytotoxicity', 'Chignola, Roberto|Vyshemirsky, Vladislav|Farina, Marcello|Del Fabbro, Alessio|Milotti, Edoardo', 'Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.|Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.|Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.|Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.|Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.', 'Italy|Italy|Italy|Italy|Italy', 4, 'Bayes Theorem|Cell Line, Tumor|Cell Survival|Humans|Models, Biological|Protein Binding|Receptors, Tumor Necrosis Factor|Signal Transduction|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1093/bioinformatics/btr297', '21561921', 'Tumour Necrosis Factor alpha (TNF) initiates a complex series of biochemical events in the cell upon binding to its type R1 receptor (TNF-R1). Recent experimental work has unravelled the molecular regulation of the signalling complexes that lead either to cell survival or death. Survival signals are activated by direct binding of TNF to TNF-R1 at the cell membrane whereas apoptotic signals by endocytosed TNF/TNF-R1 complexes. Here we describe a reduced, effective model with few free parameters, where we group some intricate mechanisms into effective modules, that successfully describes this complex set of actions. We study the parameter space to show that the model is structurally stable and robust over a broad range of parameter values.'),
(76214, 2011, 'bio', 'HiTRACE: high-throughput robust analysis for capillary electrophoresis', 'Yoon, Sungroh|Kim, Jinkyu|Hum, Justine|Kim, Hanjoo|Park, Seunghyun|Kladwang, Wipapat|Das, Rhiju', 'School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea. sryoon@korea.ac.kr', '-|-|-|-|-|-|-', 0, 'Algorithms|Electrophoresis, Capillary|Nucleic Acid Conformation|Nucleic Acids|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btr277', '21561922', 'Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment.'),
(76215, 2011, 'bio', 'SCORER 20: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences', 'Armstrong, Craig T|Vincent, Thomas L|Green, Peter J|Woolfson, Derek N', 'School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.|School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.|School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.|School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Algorithms|Amino Acid Motifs|Internet|Protein Multimerization|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btr299', '21576179', 'The coiled coil is a ubiquitous xcexb1-helical protein structure domain that directs and facilitates protein-protein interactions in a wide variety of biological processes. At the protein-sequence level, coiled coils are quite straightforward and readily recognized via the conspicuous heptad repeats of hydrophobic and polar residues. However, structurally they are more complicated, existing in a range of oligomer states and topologies. Here, we address the issue of predicting coiled-coil oligomeric state from protein sequence.'),
(76216, 2011, 'bio', 'Classification with correlated features: unreliability of feature ranking and solutions', 'Tolosi, Laura|Lengauer, Thomas', 'Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany.', 'Germany|Germany', 124, 'Breast Neoplasms|Cluster Analysis|Comparative Genomic Hybridization|Female|Genomics|Humans|Logistic Models|Models, Biological|Models, Molecular|Neoplasms|Solutions|Statistics as Topic|Urinary Bladder Neoplasms', 'Journal Article', '10.1093/bioinformatics/btr300', '21576180', 'Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover, features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated. Classical model selection methods such as penalized logistic regression or random forest become unstable in the presence of high feature correlations. Sophisticated penalties such as group Lasso or fused Lasso can force the models to assign similar weights to correlated features and thus improve model stability and interpretability. In this article, we show that the measures of feature relevance corresponding to the above-mentioned methods are biased such that the weights of the features belonging to groups of correlated features decrease as the sizes of the groups increase, which leads to incorrect model interpretation and misleading feature ranking.'),
(76217, 2011, 'bio', 'Sensitive and fast mapping of di-base encoded reads', 'Hormozdiari, Farhad|Hach, Faraz|Sahinalp, S Cenk|Eichler, Evan E|Alkan, Can', 'Department of Genome Sciences, Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195-5065, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Chromosomes, Human, Pair 1|Genetic Variation|Genome|High-Throughput Nucleotide Sequencing|Humans|Nucleotides|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btr303', '21586516', 'Discovering variation among high-throughput sequenced genomes relies on efficient and effective mapping of sequence reads. The speed, sensitivity and accuracy of read mapping are crucial to determining the full spectrum of single nucleotide variants (SNVs) as well as structural variants (SVs) in the donor genomes analyzed.'),
(76218, 2011, 'bio', 'Hidden conformations in protein structures', 'Ashkenazy, Haim|Unger, Ron|Kliger, Yossef', 'Compugen LTD, Tel Aviv 69512, Israel.|Compugen LTD, Tel Aviv 69512, Israel.|Compugen LTD, Tel Aviv 69512, Israel.', 'Israel|Israel|Israel', 7, 'Algorithms|Calmodulin|Computational Biology|Forecasting|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btr292', '21586517', 'Prediction of interactions between protein residues (contact map prediction) can facilitate various aspects of 3D structure modeling. However, the accuracy of ab initio contact prediction is still limited. As structural genomics initiatives move ahead, solved structures of homologous proteins can be used as multiple templates to improve contact prediction of the major conformation of an unsolved target protein. Furthermore, multiple templates may provide a wider view of the proteins conformational space. However, successful usage of multiple structural templates is not straightforward, due to their variable relevance to the target protein, and because of data redundancy issues.'),
(76219, 2011, 'bio', 'Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy', 'Hou, Tingjun|Li, Youyong|Wang, Wei', 'Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, PR China. tingjunhou@hotmail.com|Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, PR China. tingjunhou@hotmail.com|Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, PR China. tingjunhou@hotmail.com', 'China|China|China', 55, 'A Kinase Anchor Proteins|Amino Acid Sequence|Cyclic AMP-Dependent Protein Kinases|Humans|Molecular Sequence Data|Peptides|Phosphorylation|Protein Binding|Protein Subunits|Proteomics', 'Journal Article', '10.1093/bioinformatics/btr294', '21586518', 'Favorable interaction between the regulatory subunit of the cAMP-dependent protein kinase (PKA) and a peptide in A-kinase anchoring proteins (AKAPs) is critical for translocating PKA to the subcellular sites where the enzyme phosphorylates its substrates. It is very hard to identify AKAPs peptides binding to PKA due to the high sequence diversity of AKAPs.'),
(76220, 2011, 'bio', '3FunMap: full-sib family functional mapping of dynamic traits', 'Tong, Chunfa|Wang, Zhong|Zhang, Bo|Shi, Jisen|Wu, Rongling', 'Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China.|Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China.|Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China.|Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China.|Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China.', 'China|China|China|China|China', 0, 'Chromosome Mapping|Computer Simulation|Genetic Markers|Models, Genetic|Populus|Quantitative Trait Loci|Siblings|Software', 'Journal Article', '10.1093/bioinformatics/btr306', '21586519', 'Functional mapping that embeds the developmental mechanisms of complex traits shows great power to study the dynamic pattern of genetic effects triggered by individual quantitative trait loci (QTLs). A full-sib family, produced by crossing two heterozygous parents, is characteristic of uncertainties about cross-type at a locus and linkage phase between different loci. Integrating functional mapping into a full-sib family requires a model selection procedure capable of addressing these uncertainties. 3FunMap, written in VC++ 6.0, provides a flexible and extensible platform to perform full-sib functional mapping of dynamic traits. Functions in the package encompass linkage phase determination, marker map construction and the pattern identification of QTL segregation, dynamic tests of QTL effects, permutation tests and numerical simulation. We demonstrate the features of 3FunMap through real data analysis and computer simulation.'),
(76221, 2011, 'bio', 'Genome-wide association studies pipeline (GWASpi): a desktop application for genome-wide SNP analysis and management', 'Muñiz-Fernandez, Fernando|Carreño-Torres, Angel|Morcillo-Suarez, Carlos|Navarro, Arcadi', 'Institut de Biología Evolutiva, Universitat Pompeu Fabra, Biomedical Research Park (PRBB), Barcelona, Spain.|Institut de Biología Evolutiva, Universitat Pompeu Fabra, Biomedical Research Park (PRBB), Barcelona, Spain.|Institut de Biología Evolutiva, Universitat Pompeu Fabra, Biomedical Research Park (PRBB), Barcelona, Spain.|Institut de Biología Evolutiva, Universitat Pompeu Fabra, Biomedical Research Park (PRBB), Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 9, 'Databases, Genetic|Genome-Wide Association Study|Humans|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btr301', '21586520', 'Genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) arrays are the most widely used approach to detect loci associated to human traits. Due to the complexity of the methods and software packages available, each with its particular format requiring intricate management workflows, the analysis of GWAS usually confronts scientists with steep learning curves. Indeed, the wide variety of tools makes the parsing and manipulation of data the most time consuming and error prone part of a study. To help resolve these issues, we present GWASpi, a user-friendly, multiplatform, desktop-able application for the management and analysis of GWAS data, with a novel approach on database technologies to leverage the most out of commonly available desktop hardware. GWASpi aims to be a start-to-finish GWAS management application, from raw data to results, containing the most common analysis tools. As a result, GWASpi is easy to use and reduces in up to two orders of magnitude the time needed to perform the fundamental steps of a GWAS.'),
(76222, 2011, 'bio', 'FusionMap: detecting fusion genes from next-generation sequencing data at base-pair resolution', 'Ge, Huanying|Liu, Kejun|Juan, Todd|Fang, Fang|Newman, Matthew|Hoeck, Wolfgang', 'Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com|Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com|Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com|Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com|Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com|Research and Development Informatics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA. ge@amgen.com', 'United States|United States|United States|United States|United States|United States', 152, 'Base Pairing|Base Sequence|Computational Biology|Gene Fusion|Genome|Oncogene Proteins, Fusion|Sensitivity and Specificity|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr310', '21593131', 'Next generation sequencing technology generates high-throughput data, which allows us to detect fusion genes at both transcript and genomic levels. To detect fusion genes, the current bioinformatics tools heavily rely on paired-end approaches and overlook the importance of reads that span fusion junctions. Thus there is a need to develop an efficient aligner to detect fusion events by accurate mapping of these junction-spanning single reads, particularly when the read gets longer with the improvement in sequencing technology.'),
(76223, 2011, 'bio', 'Java bioinformatics analysis web services for multiple sequence alignment--JABAWS:MSA', 'Troshin, Peter V|Procter, James B|Barton, Geoffrey J', 'Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.|Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.|Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Internet|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr304', '21593132', 'JABAWS is a web services framework that simplifies the deployment of web services for bioinformatics. JABAWS:MSA provides services for five multiple sequence alignment (MSA) methods (Probcons, T-coffee, Muscle, Mafft and ClustalW), and is the system employed by the Jalview multiple sequence analysis workbench since version 2.6. A fully functional, easy to set up server is provided as a Virtual Appliance (VA), which can be run on most operating systems that support a virtualization environment such as VMware or Oracle VirtualBox. JABAWS is also distributed as a Web Application aRchive (WAR) and can be configured to run on a single computer and/or a cluster managed by Grid Engine, LSF or other queuing systems that support DRMAA. JABAWS:MSA provides clients full access to each applications parameters, allows administrators to specify named parameter preset combinations and execution limits for each application through simple configuration files. The JABAWS command-line client allows integration of JABAWS services into conventional scripts.'),
(76224, 2011, 'bio', 'An ensemble biclustering approach for querying gene expression compendia with experimental lists', 'De Smet, Riet|Marchal, Kathleen', 'Department of Plant Systems Biology, VIB, Ghent University, Technologiepark 927, Ghent, Belgium.|Department of Plant Systems Biology, VIB, Ghent University, Technologiepark 927, Ghent, Belgium.', 'Belgium|Belgium', 0, 'Algorithms|Cluster Analysis|Escherichia coli|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btr307', '21593133', 'Query-based biclustering techniques allow interrogating a gene expression compendium with a given gene or gene list. They do so by searching for genes in the compendium that have a profile close to the average expression profile of the genes in this query-list. As it can often not be guaranteed that the genes in a long query-list will all be mutually coexpressed, it is advisable to use each gene separately as a query. This approach, however, leaves the user with a tedious post-processing of partially redundant biclustering results. The fact that for each query-gene multiple parameter settings need to be tested in order to detect the most optimal bicluster size adds to the redundancy problem.'),
(76225, 2011, 'bio', 'Fast accessibility-based prediction of RNA-RNA interactions', 'Tafer, Hakim|Amman, Fabian|Eggenhofer, Florian|Stadler, Peter F|Hofacker, Ivo L', 'Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. htafer@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. htafer@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. htafer@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. htafer@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. htafer@bioinf.uni-leipzig.de', 'Germany|Germany|Germany|Germany|Germany', 20, 'Algorithms|Base Sequence|Genome|Models, Molecular|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr281', '21593134', 'Currently, the best RNA-RNA interaction prediction tools are based on approaches that consider both the inter- and intramolecular interactions of hybridizing RNAs. While accurate, these methods are too slow and memory-hungry to be employed in genome-wide RNA target scans. Alternative methods neglecting intramolecular structures are fast enough for genome-wide applications, but are too inaccurate to be of much practical use.'),
(76226, 2011, 'bio', 'Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking', 'Zsila, Ferenc|Bikadi, Zsolt|Malik, David|Hari, Peter|Pechan, Imre|Berces, Attila|Hazai, Eszter', 'Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.|Department of Molecular Pharmacology, Institute of Biomolecular Chemistry, Chemical Research Center, H-1025 Budapest, Pusztaszeri út 59-67., Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 81, 'Artificial Intelligence|Binding Sites|Crystallography, X-Ray|Humans|Models, Molecular|Pharmaceutical Preparations|Protein Binding|Serum Albumin', 'Journal Article', '10.1093/bioinformatics/btr284', '21593135', 'Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.'),
(76227, 2011, 'bio', 'GenPlay, a multipurpose genome analyzer and browser', 'Lajugie, Julien|Bouhassira, Eric E', 'Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA.', 'United States|United States', 0, 'Base Sequence|Gene Expression|Genome|Humans|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr309', '21596789', 'Rapidly decreasing sequencing cost due to the emergence and improvement of massively parallel sequencing technologies has resulted in a dramatic increase in the quantity of data that needs to be analyzed. Therefore, software tools to process, visualize, analyze and integrate data produced on multiple platforms and using multiple methods are needed.'),
(76228, 2011, 'bio', 'Bioinformatics challenges for personalized medicine', 'Fernald, Guy Haskin|Capriotti, Emidio|Daneshjou, Roxana|Karczewski, Konrad J|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.|Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.|Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.|Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.|Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States', 157, 'Computational Biology|Genome-Wide Association Study|Human Genome Project|Humans|Polymorphism, Single Nucleotide|Precision Medicine', 'Journal Article', '10.1093/bioinformatics/btr295', '21596790', 'Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.'),
(76229, 2011, 'bio', 'rBioNet: A COBRA toolbox extension for reconstructing high-quality biochemical networks', 'Thorleifsson, Stefan Gretar|Thiele, Ines', 'Center for Systems Biology, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.', 'Iceland|Iceland', 0, 'Chromosome Mapping|Databases as Topic|Genome|Metabolic Networks and Pathways|Quality Control|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr308', '21596791', 'Genome-scale metabolic networks are widely used in systems biology. However, to date no freely available tool exists that ensures quality control during the reconstruction process.'),
(76230, 2011, 'bio', 'linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type', 'Kalinka, Alex T|Tomancak, Pavel', 'Max Planck Institute for Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany. kalinka@mpi-cbg.de|Max Planck Institute for Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany. kalinka@mpi-cbg.de', 'Germany|Germany', 0, 'Algorithms|Drosophila Proteins|Gene Expression Profiling|Gene Regulatory Networks|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btr311', '21596792', 'An essential element when analysing the structure, function, and dynamics of biological networks is the identification of communities of related nodes. An algorithm proposed recently enhances this process by clustering the links between nodes, rather than the nodes themselves, thereby allowing each node to belong to multiple overlapping or nested communities. The R package linkcomm implements this algorithm and extends it in several aspects: (i) the clustering algorithm handles networks that are weighted, directed, or both weighted and directed, (ii) several visualization methods are implemented that facilitate the representation of the link communities and their relationships, (iii) a suite of functions are included for the downstream analysis of the link communities including novel community-based measures of node centrality, (iv) the main algorithm is written in C++ and designed to handle networks of any size, and (v) several clustering methods are available for networks that can be handled in memory, and the number of communities can be adjusted by the user.'),
(76231, 2011, 'bio', 'Positional integratomic approach in identification of genomic candidate regions for Parkinsons disease', 'Maver, Ales|Peterlin, Borut', 'Department of Obstetrics and Gynecology, Institute of Medical Genetics, University Medical Centre Ljubljana, 3, Šlajmerjeva Street, Ljubljana 1000, Slovenia. ales.maver@gmail.com|Department of Obstetrics and Gynecology, Institute of Medical Genetics, University Medical Centre Ljubljana, 3, Šlajmerjeva Street, Ljubljana 1000, Slovenia. ales.maver@gmail.com', 'Slovenia|Slovenia', 8, 'Databases, Genetic|Gene Expression Profiling|Genetic Association Studies|Genetic Predisposition to Disease|Genetic Testing|High-Throughput Screening Assays|Humans|Parkinson Disease|Proteomics', 'Journal Article', '10.1093/bioinformatics/btr313', '21596793', 'Recent abundance of data from studies employing high-throughput technologies to reveal alterations in human disease on genomic, transcriptomic, proteomic and other levels, offer the possibility to integrate this information into a comprehensive picture of molecular events occurring in human disease. Diversity of data originating from these studies presents a methodological obstacle in the integration process, also due to difficulties in choosing the optimal unified denominator that would allow inclusion of variables from various types of studies. We present a novel approach for integration of such multi-origin data based on positions of genetic alterations occurring in human diseases. Parkinsons disease (PD) was chosen as a model for evaluation of our methodology.'),
(76232, 2011, 'bio', 'TimeTree2: species divergence times on the iPhone', 'Kumar, Sudhir|Hedges, S Blair', 'Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA.|Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA.', 'United States|United States', 0, 'Cell Phone|Evolution, Molecular|Internet|Knowledge Bases|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr315', '21622662', 'Scientists, educators and the general public often need to know times of divergence between species. But they rarely can locate that information because it is buried in the scientific literature, usually in a format that is inaccessible to text search engines. We have developed a public knowledgebase that enables data-driven access to the collection of peer-reviewed publications in molecular evolution and phylogenetics that have reported estimates of time of divergence between species. Users can query the TimeTree resource by providing two names of organisms (common or scientific) that can correspond to species or groups of species. The current TimeTree web resource (TimeTree2) contains timetrees reported from molecular clock analyses in 910 published studies and 17 341 species that span the diversity of life. TimeTree2 interprets complex and hierarchical data from these studies for each user query, which can be launched using an iPhone application, in addition to the website. Published time estimates are now readily accessible to the scientific community, K-12 and college educators, and the general public, without requiring knowledge of evolutionary nomenclature.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76233, 2011, 'bio', 'Computational discovery of human coding and non-coding transcripts with conserved splice sites', 'Rose, Dominic|Hiller, Michael|Schutt, Katharina|Hackermüller, Jörg|Backofen, Rolf|Stadler, Peter F', 'Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany. dominic@bioinf.uni-leipzig.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 20, 'Animals|Base Sequence|Computers, Molecular|Databases, Nucleic Acid|Exons|Gene Expression Profiling|Genomics|Humans|Molecular Sequence Data|Protein Isoforms|RNA Splice Sites|RNA Splicing|RNA, Messenger|RNA, Untranslated|Sequence Analysis, RNA|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btr314', '21622663', 'Long non-coding RNAs (lncRNAs) resemble protein-coding mRNAs but do not encode proteins. Most lncRNAs are under lower sequence constraints than protein-coding genes and lack conserved secondary structures, making it hard to predict them computationally.'),
(76234, 2011, 'bio', 'RightField: embedding ontology annotation in spreadsheets', 'Wolstencroft, Katy|Owen, Stuart|Horridge, Matthew|Krebs, Olga|Mueller, Wolfgang|Snoep, Jacky L|du Preez, Franco|Goble, Carole', 'School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk|School of Computer Science, Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. kwolstencroft@cs.man.ac.uk', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Abstracting and Indexing as Topic|Classification|Information Management|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr312', '21622664', 'In the Life Sciences, guidelines, checklists and ontologies describing what metadata is required for the interpretation and reuse of experimental data are emerging. Data producers, however, may have little experience in the use of such standards and require tools to support this form of data annotation.'),
(76235, 2011, 'bio', 'SVA: software for annotating and visualizing sequenced human genomes', 'Ge, Dongliang|Ruzzo, Elizabeth K|Shianna, Kevin V|He, Min|Pelak, Kimberly|Heinzen, Erin L|Need, Anna C|Cirulli, Elizabeth T|Maia, Jessica M|Dickson, Samuel P|Zhu, Mingfu|Singh, Abanish|Allen, Andrew S|Goldstein, David B', 'Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu|Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. d.ge@duke.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 52, 'Audiovisual Aids|Base Sequence|Genome, Human|Genomic Structural Variation|Humans|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr317', '21624899', 'Here we present Sequence Variant Analyzer (SVA), a software tool that assigns a predicted biological function to variants identified in next-generation sequencing studies and provides a browser to visualize the variants in their genomic contexts. SVA also provides for flexible interaction with software implementing variant association tests allowing users to consider both the bioinformatic annotation of identified variants and the strength of their associations with studied traits. We illustrate the annotation features of SVA using two simple examples of sequenced genomes that harbor Mendelian mutations.'),
(76236, 2011, 'bio', 'Coverage tradeoffs and power estimation in the design of whole-genome sequencing experiments for detecting association', 'Shen, Yufeng|Song, Ruijie|Peer, Itsik', 'Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu', 'United States|United States|United States', 5, 'Case-Control Studies|Chromosome Mapping|Genome|Genome-Wide Association Study|Genomic Structural Variation|Humans|Internet|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btr305', '21636589', 'Whole-genome sequencing (WGS) allows direct interrogation of previously undetected uncommon or rare variants, which potentially contribute to the missing heritability of human disease. However, cost of sequencing large numbers of samples limits its application in case-control association studies. Here, we describe theoretical and empirical design considerations for such sequencing studies, aimed at maximizing the power of detecting association under the constraint of study-wide cost.'),
(76237, 2011, 'bio', 'Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction', 'Zhang, Zengming|Li, Yu|Lin, Biaoyang|Schroeder, Michael|Huang, Bingding', 'Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China.|Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China.|Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China.|Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China.|Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China.', 'China|China|China|China|China', 0, 'Algorithms|Binding Sites|Computational Biology|Drug Design|Internet|Ligands|Models, Molecular|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btr331', '21636590', 'Protein-ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITE(cs/c), PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result.'),
(76238, 2011, 'bio', 'PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs)', 'Luna, Augustin|Sunshine, Margot L|van Iersel, Martijn P|Aladjem, Mirit I|Kohn, Kurt W', 'Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. margot@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. margot@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. margot@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. margot@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. margot@discover.nci.nih.gov', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Metabolic Networks and Pathways|Molecular Sequence Annotation|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr336', '21636591', 'A plugin for the Java-based PathVisio pathway editor has been developed to help users draw diagrams of bioregulatory networks according to the Molecular Interaction Map (MIM) notation. Together with the core PathVisio application, this plugin presents a simple to use and cross-platform application for the construction of complex MIM diagrams with the ability to annotate diagram elements with comments, literature references and links to external databases. This tool extends the capabilities of the PathVisio pathway editor by providing both MIM-specific glyphs and support for a MIM-specific markup language file format for exchange with other MIM-compatible tools and diagram validation.'),
(76239, 2011, 'bio', 'The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections', 'Lagorce, David|Maupetit, Julien|Baell, Jonathan|Sperandio, Olivier|Tufféry, Pierre|Miteva, Maria A|Galons, Hervé|Villoutreix, Bruno O', 'MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr|MTi, Ressource Parisienne en Bioinformatique Structurale, Institut National de Santé et de Recherche Médicale (INSERM), UMR-S 973 - Paris Diderot University, 75205 Paris, Cedex 13, France. david.lagorce@inserm.fr', 'France|France|France|France|France|France|France|France', 0, 'Computer Simulation|Electronics|Internet|Online Systems|Pharmaceutical Preparations|Small Molecule Libraries|Software', 'Journal Article', '10.1093/bioinformatics/btr333', '21636592', 'The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters.'),
(76240, 2011, 'bio', 'A memory-efficient data structure representing exact-match overlap graphs with application for next-generation DNA assembly', 'Dinh, Hieu|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA. hdinh@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA. hdinh@engr.uconn.edu', 'United States|United States', 0, 'Algorithms|Computer Storage Devices|DNA|Information Systems|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr321', '21636593', 'Exact-match overlap graphs have been broadly used in the context of DNA assembly and the shortest super string problem where the number of strings n ranges from thousands to billions. The length xe2x84x93 of the strings is from 25 to 1000, depending on the DNA sequencing technologies. However, many DNA assemblers using overlap graphs suffer from the need for too much time and space in constructing the graphs. It is nearly impossible for these DNA assemblers to handle the huge amount of data produced by the next-generation sequencing technologies where the number n of strings could be several billions. If the overlap graph is explicitly stored, it would require xcexa9(n(2)) memory, which could be prohibitive in practice when n is greater than a hundred million. In this article, we propose a novel data structure using which the overlap graph can be compactly stored. This data structure requires only linear time to construct and and linear memory to store.'),
(76241, 2011, 'bio', 'LinkinPath: from sequence to interconnected pathway', 'Ingsriswang, Supawadee|Yokwai, Sunai|Wichadakul, Duangdao', 'Information Systems Laboratory, Bioresources Technology Unit, National Center for Genetic Engineering and Biotechnology, Klong Luang, Pathumthani 12120, Thailand. supawadee@biotec.or.th|Information Systems Laboratory, Bioresources Technology Unit, National Center for Genetic Engineering and Biotechnology, Klong Luang, Pathumthani 12120, Thailand. supawadee@biotec.or.th|Information Systems Laboratory, Bioresources Technology Unit, National Center for Genetic Engineering and Biotechnology, Klong Luang, Pathumthani 12120, Thailand. supawadee@biotec.or.th', 'Thailand|Thailand|Thailand', 0, 'Chromosome Mapping|Gene Regulatory Networks|Genes|Internet|Metabolic Networks and Pathways|Protein Interaction Mapping|Proteins|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btr328', '21636594', 'LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps, (ii) compare functional compositions within and among the datasets using TreeMaps, (iii) analyze the statistically enriched pathways across the datasets, (iv) build the pathway-to-pathway networks for each dataset, (v) explore potential interaction/reaction paths between pathways, and (vi) identify common pathway-to-pathway networks across the datasets.'),
(76242, 2011, 'bio', 'Aligning short reads to reference alignments and trees', 'Berger, Simon A|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-69118 Heidelberg, Germany. simon.berger@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, D-69118 Heidelberg, Germany. simon.berger@h-its.org', 'Germany|Germany', 0, 'Algorithms|Base Sequence|Likelihood Functions|Phylogeny|Reference Standards|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/btr320', '21636595', 'Likelihood-based methods for placing short read sequences from metagenomic samples into reference phylogenies have been recently introduced. At present, it is unclear how to align those reads with respect to the reference alignment that was deployed to infer the reference phylogeny. Moreover, the adaptability of such alignment methods with respect to the underlying reference alignment strategies/philosophies has not been explored. It has also not been assessed if the reference phylogeny can be deployed in conjunction with the reference alignment to improve alignment accuracy in this context.'),
(76243, 2011, 'bio', 'Comparative studies of de novo assembly tools for next-generation sequencing technologies', 'Lin, Yong|Li, Jian|Shen, Hui|Zhang, Lei|Papasian, Christopher J|Deng, Hong-Wen', 'Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.|Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.|Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.|Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.|Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.|Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China.', 'China|China|China|China|China|China', 90, 'Base Composition|Base Sequence|Computational Biology|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btr319', '21636596', 'Several new de novo assembly tools have been developed recently to assemble short sequencing reads generated by next-generation sequencing platforms. However, the performance of these tools under various conditions has not been fully investigated, and sufficient information is not currently available for informed decisions to be made regarding the tool that would be most likely to produce the best performance under a specific set of conditions.'),
(76244, 2011, 'bio', 'Robust biclustering by sparse singular value decomposition incorporating stability selection', 'Sill, Martin|Kaiser, Sebastian|Benner, Axel|Kopp-Schneider, Annette', 'Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, DKFZ, 69120 Heidelberg, Germany. m.sill@dkfz.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression Profiling|Humans|Lung Neoplasms|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1093/bioinformatics/btr322', '21636597', 'Over the past decade, several biclustering approaches have been published in the field of gene expression data analysis. Despite of huge diversity regarding the mathematical concepts of the different biclustering methods, many of them can be related to the singular value decomposition (SVD). Recently, a sparse SVD approach (SSVD) has been proposed to reveal biclusters in gene expression data. In this article, we propose to incorporate stability selection to improve this method. Stability selection is a subsampling-based variable selection that allows to control Type I error rates. The here proposed S4VD algorithm incorporates this subsampling approach to find stable biclusters, and to estimate the selection probabilities of genes and samples to belong to the biclusters.'),
(76245, 2011, 'bio', 'FACIL: Fast and Accurate Genetic Code Inference and Logo', 'Dutilh, Bas E|Jurgelenaite, Rasa|Szklarczyk, Radek|van Hijum, Sacha A F T|Harhangi, Harry R|Schmid, Markus|de Wild, Bart|Françoijs, Kees-Jan|Stunnenberg, Hendrik G|Strous, Marc|Jetten, Mike S M|Op den Camp, Huub J M|Huynen, Martijn A', 'Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, Germany. dutilh@cmbi.ru.nl', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 21, 'Base Sequence|DNA|Databases, Nucleic Acid|Foraminifera|Genetic Code|Internet|Mitochondria|Proteins|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr316', '21653513', 'The intensification of DNA sequencing will increasingly unveil uncharacterized species with potential alternative genetic codes. A total of 0.65% of the DNA sequences currently in Genbank encode their proteins with a variant genetic code, and these exceptions occur in many unrelated taxa.'),
(76246, 2011, 'bio', 'Cdc45: the missing RecJ ortholog in eukaryotes?', 'Sanchez-Pulido, Luis|Ponting, Chris P', 'Department of Physiology, Anatomy and Genetics, MRC Functional Genomics Unit, University of Oxford, Oxford OX1 3QX, UK. luis.sanchezpulido@dpag.ox.ac.uk|Department of Physiology, Anatomy and Genetics, MRC Functional Genomics Unit, University of Oxford, Oxford OX1 3QX, UK. luis.sanchezpulido@dpag.ox.ac.uk', 'United Kingdom|United Kingdom', 0, 'Bacterial Proteins|Cell Cycle Proteins|Chromosomes|DNA Replication|Eukaryota|Evolution, Molecular|Exodeoxyribonucleases|Molecular Structure|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btr332', '21653514', 'DNA replication is one of the most ancient of cellular processes and functional similarities among its molecular machinery are apparent across all cellular life. Cdc45 is one of the essential components of the eukaryotic replication fork and is required for the initiation and elongation of DNA replication, but its molecular function is currently unknown. In order to trace its evolutionary history and to identify functional domains, we embarked on a computational sequence analysis of the Cdc45 protein family. Our findings reveal eukaryotic Cdc45 and prokaryotic RecJ to possess a common ancestry and Cdc45 to contain a catalytic site within a predicted exonuclease domain. The likely orthology between Cdc45 and RecJ reveals new lines of enquiry into DNA replication mechanisms in eukaryotes.'),
(76247, 2011, 'bio', 'Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties', 'Chen, Shu-An|Ou, Yu-Yen|Lee, Tzong-Yi|Gromiha, M Michael', 'Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.', '-|-|-|-', 55, 'Amino Acids|Databases, Protein|Membrane Transport Proteins|Position-Specific Scoring Matrices|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btr340', '21653515', 'Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Currently, our knowledge about the functions of transporters is limited due to the paucity of their 3D structures. Hence, computational techniques are necessary to annotate the functions of transporters. In this work, we focused on an important functional aspect of transporters, namely annotation of targets for transport proteins. We have systematically analyzed four major classes of transporters with different transporter targets: (i) electron, (ii) protein/mRNA, (iii) ion and (iv) others, using amino acid properties. We have developed a radial basis function network-based method for predicting transport targets with amino acid properties and position specific scoring matrix profiles. Our method showed a 10-fold cross-validation accuracy of 90.1, 80.1, 70.3 and 82.3% for electron transporters, protein/mRNA transporters, ion transporters and others, respectively, in a dataset of 543 transporters. We have also evaluated the performance of the method with an independent dataset of 108 proteins and we obtained similar accuracy. We suggest that our method could be an effective tool for functional annotation of transport proteins.'),
(76248, 2011, 'bio', 'HAPGEN2: simulation of multiple disease SNPs', 'Su, Zhan|Marchini, Jonathan|Donnelly, Peter', 'Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK. zhan@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK. zhan@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK. zhan@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Disease|Genome-Wide Association Study|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btr341', '21653516', 'Performing experiments with simulated data is an inexpensive approach to evaluating competing experimental designs and analysis methods in genome-wide association studies. Simulation based on resampling known haplotypes is fast and efficient and can produce samples with patterns of linkage disequilibrium (LD), which mimic those in real data. However, the inability of current methods to simulate multiple nearby disease SNPs on the same chromosome can limit their application.'),
(76249, 2011, 'bio', 'genBlastG: using BLAST searches to build homologous gene models', 'She, Rong|Chu, Jeffrey Shih-Chieh|Uyar, Bora|Wang, Jun|Wang, Ke|Chen, Nansheng', 'School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.|School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.|School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.|School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.|School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.|School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 0A1.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Animals|Arabidopsis|Caenorhabditis elegans|Computational Biology|Genomics|Models, Genetic|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btr342', '21653517', 'BLAST users frequently expect to obtain homologous genes with certain similarity to their query genes. But what they get from BLAST searches are often collections of local alignments called high-scoring segment pairs (HSPs). On the other hand, most homology-based gene finders have been built using computation-intensive algorithms, without taking full advantage of BLAST searches that have been perfected over the last decades.'),
(76250, 2011, 'bio', 'Mathematical modeling of 16S ribosomal DNA amplification reveals optimal conditions for the interrogation of complex microbial communities with phylogenetic microarrays', 'Paliy, Oleg|Foy, Brent D', 'Department of Biochemistry and Molecular Biology, Wright State University, Dayton, OH 45435, USA. oleg.paliy@wright.edu|Department of Biochemistry and Molecular Biology, Wright State University, Dayton, OH 45435, USA. oleg.paliy@wright.edu', 'United States|United States', 10, 'Bacteria|Computer Simulation|DNA Primers|DNA, Bacterial|DNA, Ribosomal|Metagenome|Models, Theoretical|Nucleic Acid Amplification Techniques|Oligonucleotide Array Sequence Analysis|Phylogeny|Polymerase Chain Reaction|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bioinformatics/btr326', '21653518', 'Many current studies of complex microbial communities rely on the isolation of community genomic DNA, amplification of 16S ribosomal RNA genes (rDNA) and subsequent examination of community structure through interrogation of the amplified 16S rDNA pool by high-throughput sequencing, phylogenetic microarrays or quantitative PCR.'),
(76251, 2011, 'bio', 'MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects', 'Westra, Harm-Jan|Jansen, Ritsert C|Fehrmann, Rudolf S N|te Meerman, Gerard J|van Heel, David|Wijmenga, Cisca|Franke, Lude', 'Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 41, 'Algorithms|Gene Expression Profiling|Genome-Wide Association Study|Genomics|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sensitivity and Specificity|Specimen Handling', 'Journal Article', '10.1093/bioinformatics/btr323', '21653519', 'Sample mix-ups can arise during sample collection, handling, genotyping or data management. It is unclear how often sample mix-ups occur in genome-wide studies, as there currently are no post hoc methods that can identify these mix-ups in unrelated samples. We have therefore developed an algorithm (MixupMapper) that can both detect and correct sample mix-ups in genome-wide studies that study gene expression levels.'),
(76252, 2011, 'bio', 'A probabilistic method for the detection and genotyping of small indels from population-scale sequence data', 'Bansal, Vikas|Libiger, Ondrej', 'Scripps Genomic Medicine, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA. vbansal@scripps.edu|Scripps Genomic Medicine, Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA. vbansal@scripps.edu', 'United States|United States', 13, 'Base Sequence|Genetics, Population|Genome, Human|Genotype|Humans|INDEL Mutation|Likelihood Functions|Models, Genetic|Pilot Projects|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btr344', '21653520', 'High-throughput sequencing technologies have made population-scale studies of human genetic variation possible. Accurate and comprehensive detection of DNA sequence variants is crucial for the success of these studies. Small insertions and deletions represent the second most frequent class of variation in the human genome after single nucleotide polymorphisms (SNPs). Although several alignment tools for the gapped alignment of sequence reads to a reference genome are available, computational methods for discriminating indels from sequencing errors and genotyping indels directly from sequence reads are needed.'),
(76253, 2011, 'bio', 'nucleR: a package for non-parametric nucleosome positioning', 'Flores, Oscar|Orozco, Modesto', 'IRB-BSC Joint Research Program on Computational Biology, Institute of Research in Biomedicine, Baldiri i Reixac 10, Barcelona 08028, Spain.|IRB-BSC Joint Research Program on Computational Biology, Institute of Research in Biomedicine, Baldiri i Reixac 10, Barcelona 08028, Spain.', 'Spain|Spain', 46, 'Algorithms|Fourier Analysis|Genomics|Nucleosomes|Software', 'Journal Article', '10.1093/bioinformatics/btr345', '21653521', 'nucleR is an R/Bioconductor package for a flexible and fast recognition of nucleosome positioning from next generation sequencing and tiling arrays experiments. The software is integrated with standard high-throughput genomics R packages and allows for in situ visualization as well as to export results to common genome browser formats.'),
(76254, 2011, 'bio', 'The variant call format and VCFtools', 'Danecek, Petr|Auton, Adam|Abecasis, Goncalo|Albers, Cornelis A|Banks, Eric|DePristo, Mark A|Handsaker, Robert E|Lunter, Gerton|Marth, Gabor T|Sherry, Stephen T|McVean, Gilean|Durbin, Richard|1000 Genomes Project Analysis Group', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.|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|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3503, 'Alleles|Genetic Variation|Genome, Human|Genomics|Genotype|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btr330', '21653522', 'The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.'),
(76255, 2011, 'bio', 'The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways', 'Beltrame, Luca|Calura, Enrica|Popovici, Razvan R|Rizzetto, Lisa|Guedez, Damariz Rivero|Donato, Michele|Romualdi, Chiara|Draghici, Sorin|Cavalieri, Duccio', 'Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.|Department of Pharmacology M.Aiazzi Mancini, University of Firenze, 50139 Firenze, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 17, 'Computational Biology|Computer Graphics|Programming Languages|Signal Transduction|Software', 'Comparative Study', '10.1093/bioinformatics/btr339', '21653523', 'Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions.'),
(76256, 2011, 'bio', 'mapDamage: testing for damage patterns in ancient DNA sequences', 'Ginolhac, Aurelien|Rasmussen, Morten|Gilbert, M Thomas P|Willerslev, Eske|Orlando, Ludovic', 'Centre for Geogenetics, Natural History Museum of Denmark, Copenhagen University, 1350 København K, Denmark. aginolhac@snm.ku.dk|Centre for Geogenetics, Natural History Museum of Denmark, Copenhagen University, 1350 København K, Denmark. aginolhac@snm.ku.dk|Centre for Geogenetics, Natural History Museum of Denmark, Copenhagen University, 1350 København K, Denmark. aginolhac@snm.ku.dk|Centre for Geogenetics, Natural History Museum of Denmark, Copenhagen University, 1350 København K, Denmark. aginolhac@snm.ku.dk|Centre for Geogenetics, Natural History Museum of Denmark, Copenhagen University, 1350 København K, Denmark. aginolhac@snm.ku.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 141, 'Base Sequence|Computational Biology|DNA Contamination|DNA Damage|DNA Restriction Enzymes|Genome, Human|Humans|Paleontology|Reference Standards|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr347', '21659319', 'Ancient DNA extracts consist of a mixture of contaminant DNA molecules, most often originating from environmental microbes, and endogenous fragments exhibiting substantial levels of DNA damage. The latter introduce specific nucleotide misincorporations and DNA fragmentation signatures in sequencing reads that could be advantageously used to argue for sequence validity. mapDamage is a Perl script that computes nucleotide misincorporation and fragmentation patterns using next-generation sequencing reads mapped against a reference genome. The Perl script outputs are further automatically processed in embedded R script in order to detect typical patterns of genuine ancient DNA sequences.'),
(76257, 2011, 'bio', 'The LabelHash server and tools for substructure-based functional annotation', 'Moll, Mark|Bryant, Drew H|Kavraki, Lydia E', 'Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu|Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu|Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu', 'United States|United States|United States', 5, 'Algorithms|Amino Acid Motifs|Computational Biology|Databases, Protein|Internet|Molecular Sequence Annotation|Proteins|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr343', '21659320', 'The LabelHash server and tools are designed for large-scale substructure comparison. The main use is to predict the function of unknown proteins. Given a set of (putative) functional residues, LabelHash finds all occurrences of matching substructures in the entire Protein Data Bank, along with a statistical significance estimate and known functional annotations for each match. The results can be downloaded for further analysis in any molecular viewer. For Chimera, there is a plugin to facilitate this process.'),
(76258, 2011, 'bio', 'RNASAlign: RNA structural alignment system', 'Wong, Thomas K F|Wan, Kwok-Lung|Hsu, Bay-Yuan|Cheung, Brenda W Y|Hon, Wing-Kai|Lam, Tak-Wah|Yiu, Siu-Ming', 'Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 5, 'Algorithms|Internet|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr338', '21659321', 'Structural alignment of RNA is found to be a useful computational technique for idenitfying non-coding RNAs (ncRNAs). However, existing tools do not handle structures with pseudoknots. Although algorithms exist that can handle structural alignment for different types of pseudoknots, no software tools are available and users have to determine the type of pseudoknots to select the appropriate algoirthm to use which limits the usage of structural alignment in identifying novel ncRNAs.'),
(76259, 2011, 'bio', 'Parent-specific copy number in paired tumor-normal studies using circular binary segmentation', 'Olshen, Adam B|Bengtsson, Henrik|Neuvial, Pierre|Spellman, Paul T|Olshen, Richard A|Seshan, Venkatraman E', 'Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu|Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. olshena@biostat.ucsf.edu', 'United States|United States|United States|United States|United States|United States', 62, 'Algorithms|Alleles|Computer Simulation|Gene Dosage|Gene Frequency|Glioblastoma|Humans|Loss of Heterozygosity|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr329', '21666266', 'High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods.'),
(76260, 2011, 'bio', 'Xwalk: computing and visualizing distances in cross-linking experiments', 'Kahraman, Abdullah|Malmström, Lars|Aebersold, Ruedi', 'Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH Zurich), CH-8093 Zurich, Switzerland. abdullah@imsb.biol.ethz.ch|Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH Zurich), CH-8093 Zurich, Switzerland. abdullah@imsb.biol.ethz.ch|Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH Zurich), CH-8093 Zurich, Switzerland. abdullah@imsb.biol.ethz.ch', 'Switzerland|Switzerland|Switzerland', 83, 'Algorithms|Amino Acid Sequence|Amino Acids|Computational Biology|Internet|Mass Spectrometry|Programming Languages|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr348', '21666267', 'Chemical cross-linking of proteins or protein complexes and the mass spectrometry-based localization of the cross-linked amino acids in peptide sequences is a powerful method for generating distance restraints on the substrates topology.'),
(76261, 2011, 'bio', 'MGV: a generic graph viewer for comparative omics data', 'Symons, Stephan|Nieselt, Kay', 'Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, 72076 Tübingen, Germany. symons@informatik.uni-tuebingen.de|Center for Bioinformatics Tübingen, Faculty of Science, University of Tübingen, 72076 Tübingen, Germany. symons@informatik.uni-tuebingen.de', 'Germany|Germany', 0, 'Cluster Analysis|Computer Graphics|Gene Expression Profiling|Metabolomics|Models, Biological|Proteomics|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr351', '21666268', 'High-throughput transcriptomics, proteomics and metabolomics methods have revolutionized our knowledge of biological systems. To gain knowledge from comparative omics studies, strong data integration and visualization features are required. Knowledge gained from these studies is often available in the form of graphs, and their visualization is especially useful in a wide range of systems biology topics, including pathway analysis, interaction networks or gene models. Especially, it is necessary to compare biological models with measured data. This allows the identification of new models and new insights into existing ones.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76262, 2011, 'bio', 'Pathogen detection using short-RNA deep sequencing subtraction and assembly', 'Isakov, Ofer|Modai, Shira|Shomron, Noam', 'Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel', 0, 'Cell Line|HIV|High-Throughput Nucleotide Sequencing|Humans|Mycoplasma|RNA, Bacterial|RNA, Viral|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btr349', '21666269', 'Early and accurate detection of human pathogen infection is critical for treatment and therapeutics. Here we describe pathogen identification using short RNA subtraction and assembly (SRSA), a detection method that overcomes the requirement of prior knowledge and culturing of pathogens, by using degraded small RNA and deep sequencing technology. We prove our approachs efficiency through identification of a combined viral and bacterial infection in human cells.'),
(76263, 2011, 'bio', 'Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates', 'Yang, Yuedong|Faraggi, Eshel|Zhao, Huiying|Zhou, Yaoqi', 'School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.', 'India|India|India|India', 0, 'Algorithms|Computational Biology|Models, Molecular|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btr350', '21666270', 'In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area.'),
(76264, 2011, 'bio', 'Question answering systems in biology and medicine--the time is now', 'Wren, Jonathan D', 'School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.', 'India', 8, 'Biology|Computers|Humans|Information Systems|Medicine|Software', 'Letter', '10.1093/bioinformatics/btr327', '21672971', '-'),
(76265, 2011, 'bio', 'Immune system simulation online', 'Rapin, Nicolas|Lund, Ole|Castiglione, Filippo', 'Biotech Research & Innovation Centre and Bioinformatics Centre, University of Copenhagen, DK-2200 Copenhagen N, Denmark.|Biotech Research & Innovation Centre and Bioinformatics Centre, University of Copenhagen, DK-2200 Copenhagen N, Denmark.|Biotech Research & Innovation Centre and Bioinformatics Centre, University of Copenhagen, DK-2200 Copenhagen N, Denmark.', 'Denmark|Denmark|Denmark', 13, 'Computer Simulation|Epitope Mapping|Humans|Immune System|Internet|Lymphocytes|Models, Biological|Online Systems|Position-Specific Scoring Matrices|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btr335', '21685045', 'The recognition of antigenic peptides is a major event of an immune response. In current mesoscopic-scale simulators of the immune system, this crucial step has been modeled in a very approximated way.'),
(76266, 2011, 'bio', 'Noise reduction in genome-wide perturbation screens using linear mixed-effect models', 'Yu, Danni|Danku, John|Baxter, Ivan|Kim, Sungjin|Vatamaniuk, Olena K|Salt, David E|Vitek, Olga', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Gene Knockout Techniques|Genomics|High-Throughput Screening Assays|Linear Models|Phenotype|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btr359', '21685046', 'High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.'),
(76267, 2011, 'bio', 'AMIGO, a toolbox for advanced model identification in systems biology using global optimization', 'Balsa-Canto, Eva|Banga, Julio R', 'Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain. ebalsa@iim.csic.es|Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain. ebalsa@iim.csic.es', 'Spain|Spain', 54, 'Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr370', '21685047', 'Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation. These parameters must be estimated by fitting the model to experimental data. This estimation problem is very challenging due to the non-linear character of the dynamics, the large number of parameters and the frequently poor information content of the experimental data (poor practical identifiability). The design of optimal (more informative) experiments is an associated problem of the highest interest.'),
(76268, 2011, 'bio', 'A tree-based approach for motif discovery and sequence classification', 'Yan, Rui|Boutros, Paul C|Jurisica, Igor', 'Department of Computer Science, University of Toronto, Toronto, Canada M5S 3G4. ruiyan@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, Canada M5S 3G4. ruiyan@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, Canada M5S 3G4. ruiyan@cs.toronto.edu', 'Canada|Canada|Canada', 4, 'Algorithms|Animals|Artificial Intelligence|Base Sequence|Computational Biology|DNA|Humans|Mice|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btr353', '21685048', 'Pattern discovery algorithms are widely used for the analysis of DNA and protein sequences. Most algorithms have been designed to find overrepresented motifs in sparse datasets of long sequences, and ignore most positional information. We introduce an algorithm optimized to exploit spatial information in sparse-but-populous datasets.'),
(76269, 2011, 'bio', 'Enhancing the rate of scaffold discovery with diversity-oriented prioritization', 'Swamidass, S Joshua|Calhoun, Bradley T|Bittker, Joshua A|Bodycombe, Nicole E|Clemons, Paul A', 'Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|High-Throughput Screening Assays', 'Journal Article', '10.1093/bioinformatics/btr369', '21685049', 'In high-throughput screens (HTS) of small molecules for activity in an in vitro assay, it is common to search for active scaffolds, with at least one example successfully confirmed as an active. The number of active scaffolds better reflects the success of the screen than the number of active molecules. Many existing algorithms for deciding which hits should be sent for confirmatory testing neglect this concern.'),
(76270, 2011, 'bio', 'Genomic data integration using guided clustering', 'Maneck, Matthias|Schrader, Alexandra|Kube, Dieter|Spang, Rainer', 'Institut für funktionelle Genomik, Universität Regensburg, Germany.|Institut für funktionelle Genomik, Universität Regensburg, Germany.|Institut für funktionelle Genomik, Universität Regensburg, Germany.|Institut für funktionelle Genomik, Universität Regensburg, Germany.', 'Germany|Germany|Germany|Germany', 7, 'Algorithms|Cell Line, Tumor|Chromatin Immunoprecipitation|Cluster Analysis|DNA-Binding Proteins|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Lymphoma, Large B-Cell, Diffuse|Oligonucleotide Array Sequence Analysis|Prognosis|Proto-Oncogene Proteins c-bcl-6|Toll-Like Receptors|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr363', '21685050', 'In biomedical research transcriptomic, proteomic or metabolomic profiles of patient samples are often combined with genomic profiles from experiments in cell lines or animal models. Integrating experimental data with patient data is still a challenging task due to the lack of tailored statistical tools.'),
(76271, 2011, 'bio', 'Methyl-Analyzer--whole genome DNA methylation profiling', 'Xin, Yurong|Ge, Yongchao|Haghighi, Fatemeh G', 'Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA.|Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA.|Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA.', 'United States|United States|United States', 0, 'CpG Islands|DNA Methylation|DNA Modification Methylases|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr356', '21685051', 'Methyl-Analyzer is a python package that analyzes genome-wide DNA methylation data produced by the Methyl-MAPS (methylation mapping analysis by paired-end sequencing) method. Methyl-MAPS is an enzymatic-based method that uses both methylation-sensitive and -dependent enzymes covering >80% of CpG dinucleotides within mammalian genomes. It combines enzymatic-based approaches with high-throughput next-generation sequencing technology to provide whole genome DNA methylation profiles. Methyl-Analyzer processes and integrates sequencing reads from methylated and unmethylated compartments and estimates CpG methylation probabilities at single base resolution.'),
(76272, 2011, 'bio', 'Integration of gene normalization stages and co-reference resolution using a Markov logic network', 'Dai, Hong-Jie|Chang, Yen-Ching|Tsai, Richard Tzong-Han|Hsu, Wen-Lian', 'Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw|Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw|Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw|Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw', '-|-|-|-', 9, 'Data Mining|Databases, Genetic|Genes|Humans|Markov Chains|Software', 'Journal Article', '10.1093/bioinformatics/btr358', '21685052', 'Gene normalization (GN) is the task of normalizing a textual gene mention to a unique gene database ID. Traditional top performing GN systems usually need to consider several constraints to make decisions in the normalization process, including filtering out false positives, or disambiguating an ambiguous gene mention, to improve system performance. However, these constraints are usually executed in several separate stages and cannot use each others input/output interactively. In this article, we propose a novel approach that employs a Markov logic network (MLN) to model the constraints used in the GN task. Firstly, we show how various constraints can be formulated and combined in an MLN. Secondly, we are the first to apply the two main concepts of co-reference resolution-discourse salience in centering theory and transitivity-to GN models. Furthermore, to make our results more relevant to developers of information extraction applications, we adopt the instance-based precision/recall/F-measure (PRF) in addition to the article-wide PRF to assess system performance.'),
(76273, 2011, 'bio', 'CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer', 'Wong, Wing Chung|Kim, Dewey|Carter, Hannah|Diekhans, Mark|Ryan, Michael C|Karchin, Rachel', 'Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States|United States', 65, 'Base Sequence|DNA Mutational Analysis|Databases, Factual|Humans|Mutation, Missense|Neoplasms|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btr357', '21685053', 'Thousands of cancer exomes are currently being sequenced, yielding millions of non-synonymous single nucleotide variants (SNVs) of possible relevance to disease etiology. Here, we provide a software toolkit to prioritize SNVs based on their predicted contribution to tumorigenesis. It includes a database of precomputed, predictive features covering all positions in the annotated human exome and can be used either stand-alone or as part of a larger variant discovery pipeline.'),
(76274, 2011, 'bio', 'Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks', 'Chan, Siu Hung Joshua|Ji, Ping', 'Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. joshua.chan@connect.polyu.hk|Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. joshua.chan@connect.polyu.hk', 'Hong Kong|Hong Kong', 17, 'Algorithms|Escherichia coli|Genome, Bacterial|Genomics|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btr367', '21685054', 'Elementary flux mode (EFM) is a fundamental concept as well as a useful tool in metabolic pathway analysis. One important role of EFMs is that every flux distribution can be decomposed into a set of EFMs and a number of methods to study flux distributions originated from it. Yet finding such decompositions requires the complete set of EFMs, which is intractable in genome-scale metabolic networks due to combinatorial explosion.'),
(76275, 2011, 'bio', 'Menu-driven cloud computing and resource sharing for R and Bioconductor', 'Bolouri, Hamid|Dulepet, Rajiv|Angerman, Michael', 'Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. hbolouri@fhcrc.org|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. hbolouri@fhcrc.org|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. hbolouri@fhcrc.org', 'United States|United States|United States', 0, 'Humans|Programming Languages|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btr364', '21685055', 'We report CRdata.org, a cloud-based, free, open-source web server for running analyses and sharing data and R scripts with others. In addition to using the free, public service, CRdata users can launch their own private Amazon Elastic Computing Cloud (EC2) nodes and store private data and scripts on Amazons Simple Storage Service (S3) with user-controlled access rights. All CRdata services are provided via point-and-click menus.'),
(76276, 2011, 'bio', 'KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels', 'Stead, L F|Wood, I C|Westhead, D R', 'Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.|Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.|Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Adult|Artificial Intelligence|Computational Biology|Humans|Polymorphism, Single Nucleotide|Potassium Channels, Voltage-Gated|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btr365', '21685056', 'Non-synonymous single nucleotide polymorphisms (nsSNPs) in voltage-gated potassium (Kv) channels cause diseases with potentially fatal consequences in seemingly healthy individuals. Identifying disease-causing genetic variation will aid presymptomatic diagnosis and treatment of such disorders. NsSNP-effect predictors are hypothesized to perform best when developed for specific gene families. We, thus, created KvSNP: a method that assigns a disease-causing probability to Kv-channel nsSNPs.'),
(76277, 2011, 'bio', 'This special issue comprises the papers accepted for presentationat the 19th Annual International Conference on Intelligent Systemsfor Molecular Biology, joint with the 10th European Conference onComputational Biology, an official conference of the Intern', 'Gaasterland, Terry|Vingron, Martin', 'Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.|Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.', 'United Kingdom|United Kingdom', 2, 'Computational Biology|Editorial Policies|Peer Review, Research', 'Editorial', '10.1093/bioinformatics/btr302', '21685057', '-'),
(76278, 2011, 'bio', 'A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling', 'Wang, Zhiyong|Xu, Jinbo', 'Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu|Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Models, Molecular|Models, Statistical|Nucleic Acid Conformation|Probability|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr232', '21685058', 'Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.'),
(76279, 2011, 'bio', 'Discovering and visualizing indirect associations between biomedical concepts', 'Tsuruoka, Yoshimasa|Miwa, Makoto|Hamamoto, Kaisei|Tsujii, Junichi|Ananiadou, Sophia', 'School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp|School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp|School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp|School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp|School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Artificial Intelligence|Data Mining|Internet|MEDLINE|Medical Informatics Applications|PubMed|United States', 'Journal Article', '10.1093/bioinformatics/btr214', '21685059', 'Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner.'),
(76280, 2011, 'bio', 'MeSH: a window into full text for document summarization', 'Bhattacharya, Sanmitra|Ha-Thuc, Viet|Srinivasan, Padmini', 'Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA. sanmitra-bhattacharya@uiowa.edu|Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA. sanmitra-bhattacharya@uiowa.edu|Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA. sanmitra-bhattacharya@uiowa.edu', 'United States|United States|United States', 0, 'Information Storage and Retrieval|MEDLINE|Medical Subject Headings|United States', 'Journal Article', '10.1093/bioinformatics/btr223', '21685060', 'Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents.'),
(76281, 2011, 'bio', 'A folding algorithm for extended RNA secondary structures', 'Höner zu Siederdissen, Christian|Bernhart, Stephan H|Stadler, Peter F|Hofacker, Ivo L', 'Institute for Theoretical Chemistry, University of Vienna, A-1090 Vienna, Austria. choener@tbi.inivie.ac.at|Institute for Theoretical Chemistry, University of Vienna, A-1090 Vienna, Austria. choener@tbi.inivie.ac.at|Institute for Theoretical Chemistry, University of Vienna, A-1090 Vienna, Austria. choener@tbi.inivie.ac.at|Institute for Theoretical Chemistry, University of Vienna, A-1090 Vienna, Austria. choener@tbi.inivie.ac.at', 'Austria|Austria|Austria|Austria', 0, 'Algorithms|Base Pairing|Models, Molecular|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr220', '21685061', 'RNA secondary structure contains many non-canonical base pairs of different pair families. Successful prediction of these structural features leads to improved secondary structures with applications in tertiary structure prediction and simultaneous folding and alignment.'),
(76282, 2011, 'bio', 'Error correction of high-throughput sequencing datasets with non-uniform coverage', 'Medvedev, Paul|Scott, Eric|Kakaradov, Boyko|Pevzner, Pavel', 'Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Escherichia coli|High-Throughput Nucleotide Sequencing|Models, Statistical|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btr208', '21685062', 'The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.'),
(76283, 2011, 'bio', 'Generative probabilistic models for protein-protein interaction networks--the biclique perspective', 'Schweiger, Regev|Linial, Michal|Linial, Nathan', 'School of Computer Science and Engineering, Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences and The Sudarsky Center for Computational Biology, The Hebrew University, Jerusalem, 91904 Israel. regevs01@cs.huji.ac.il|School of Computer Science and Engineering, Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences and The Sudarsky Center for Computational Biology, The Hebrew University, Jerusalem, 91904 Israel. regevs01@cs.huji.ac.il|School of Computer Science and Engineering, Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences and The Sudarsky Center for Computational Biology, The Hebrew University, Jerusalem, 91904 Israel. regevs01@cs.huji.ac.il', 'Israel|Israel|Israel', 0, 'Models, Statistical|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btr201', '21685063', 'Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein-protein interaction (PPI) network, nodes represent proteins and edges represent connections between them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here, we consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae. We are trying to distinguishing between a model family which performs a process of copying neighbors, represented by the duplication-divergence (DD) model, and models which do not copy neighbors, with the Barabxc3xa1si-Albert (BA) preferential attachment model as a leading example.'),
(76284, 2011, 'bio', 'RINQ: Reference-based Indexing for Network Queries', 'Gülsoy, Günhan|Kahveci, Tamer', 'Computer and Information Sciences and Engineering Department, University of Florida, Gainesville, FL 32611, USA. ggulsoy@cise.ufl.edu|Computer and Information Sciences and Engineering Department, University of Florida, Gainesville, FL 32611, USA. ggulsoy@cise.ufl.edu', 'United States|United States', 8, 'Algorithms|Animals|Computational Biology|Databases, Factual|Humans|Information Storage and Retrieval|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btr203', '21685064', 'We consider the problem of similarity queries in biological network databases. Given a database of networks, similarity query returns all the database networks whose similarity (i.e. alignment score) to a given query network is at least a specified similarity cutoff value. Alignment of two networks is a very costly operation, which makes exhaustive comparison of all the database networks with a query impractical. To tackle this problem, we develop a novel indexing method, named RINQ (Reference-based Indexing for Biological Network Queries). Our method uses a set of reference networks to eliminate a large portion of the database quickly for each query. A reference network is a small biological network. We precompute and store the alignments of all the references with all the database networks. When our database is queried, we align the query network with all the reference networks. Using these alignments, we calculate a lower bound and an approximate upper bound to the alignment score of each database network with the query network. With the help of upper and lower bounds, we eliminate the majority of the database networks without aligning them to the query network. We also quickly identify a small portion of these as guaranteed to be similar to the query. We perform pairwise alignment only for the remaining networks. We also propose a supervised method to pick references that have a large chance of filtering the unpromising database networks. Extensive experimental evaluation suggests that (i) our method reduced the running time of a single query on a database of around 300 networks from over 2 days to only 8 h, (ii) our method outperformed the state of the art method Closure Tree and SAGA by a factor of three or more, and (iii) our method successfully identified statistically and biologically significant relationships across networks and organisms.'),
(76285, 2011, 'bio', 'Environment specific substitution tables improve membrane protein alignment', 'Hill, Jamie R|Kelm, Sebastian|Shi, Jiye|Deane, Charlotte M', '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.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Humans|Membrane Proteins|Models, Molecular|Principal Component Analysis|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btr230', '21685065', 'Membrane proteins are both abundant and important in cells, but the small number of solved structures restricts our understanding of them. Here we consider whether membrane proteins undergo different substitutions from their soluble counterparts and whether these can be used to improve membrane protein alignments, and therefore improve prediction of their structure.'),
(76286, 2011, 'bio', 'Construction of co-complex score matrix for protein complex prediction from AP-MS data', 'Xie, Zhipeng|Kwoh, Chee Keong|Li, Xiao-Li|Wu, Min', 'School of Computer Science, Fudan University, Shanghai 200433, China.|School of Computer Science, Fudan University, Shanghai 200433, China.|School of Computer Science, Fudan University, Shanghai 200433, China.|School of Computer Science, Fudan University, Shanghai 200433, China.', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Likelihood Functions|Mass Spectrometry|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btr212', '21685066', 'Protein complexes are of great importance for unraveling the secrets of cellular organization and function. The AP-MS technique has provided an effective high-throughput screening to directly measure the co-complex relationship among multiple proteins, but its performance suffers from both false positives and false negatives. To computationally predict complexes from AP-MS data, most existing approaches either required the additional knowledge from known complexes (supervised learning), or had numerous parameters to tune.'),
(76287, 2011, 'bio', 'Uncover disease genes by maximizing information flow in the phenome-interactome network', 'Chen, Yong|Jiang, Tao|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 1000084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 1000084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 1000084, China.', 'China|China|China', 58, 'Gene Dosage|Genetic Predisposition to Disease|Humans|Melanoma|Phenotype|Proteins', 'Journal Article', '10.1093/bioinformatics/btr213', '21685067', 'Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein-protein interactions, no combinatorial approaches have been proposed in the literature.'),
(76288, 2011, 'bio', 'Physical Module Networks: an integrative approach for reconstructing transcription regulation', 'Novershtern, Noa|Regev, Aviv|Friedman, Nir', 'School of Computer Science, Hebrew University, Jerusalem 91904, Israel.|School of Computer Science, Hebrew University, Jerusalem 91904, Israel.|School of Computer Science, Hebrew University, Jerusalem 91904, Israel.', 'Israel|Israel|Israel', 0, 'Bayes Theorem|Cell Cycle|Epithelial Cells|Gene Expression Regulation|Gene Regulatory Networks|Humans|Influenza A Virus, H1N1 Subtype|Influenza, Human|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btr222', '21685068', 'Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.'),
(76289, 2011, 'bio', 'Identification of metabolic network models from incomplete high-throughput datasets', 'Berthoumieux, Sara|Brilli, Matteo|de Jong, Hidde|Kahn, Daniel|Cinquemani, Eugenio', 'INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr|INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr|INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr|INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr|INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr', 'France|France|France|France|France', 17, 'Algorithms|Computational Biology|Escherichia coli|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btr225', '21685069', 'High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance.'),
(76290, 2011, 'bio', 'TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages', 'Parikh, Ankur P|Wu, Wei|Curtis, Ross E|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.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 16, 'Algorithms|Breast Neoplasms|Cell Differentiation|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btr239', '21685070', 'Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells, the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.'),
(76291, 2011, 'bio', 'Optimally discriminative subnetwork markers predict response to chemotherapy', 'Dao, Phuong|Wang, Kendric|Collins, Colin|Ester, Martin|Lapuk, Anna|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University.|School of Computing Science, Simon Fraser University.|School of Computing Science, Simon Fraser University.|School of Computing Science, Simon Fraser University.|School of Computing Science, Simon Fraser University.|School of Computing Science, Simon Fraser University.', '-|-|-|-|-|-', 0, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Gene Expression Profiling|Humans', 'Journal Article', '10.1093/bioinformatics/btr245', '21685072', 'Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein-protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.'),
(76292, 2011, 'bio', 'Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs', 'Kam-Thong, Tony|Pütz, Benno|Karbalai, Nazanin|Müller-Myhsok, Bertram|Borgwardt, Karsten', 'Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de|Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de|Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de|Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de|Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 11, 'Computational Biology|Epistasis, Genetic|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btr218', '21685073', 'In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.'),
(76293, 2011, 'bio', 'Detecting epistatic effects in association studies at a genomic level based on an ensemble approach', 'Li, Jing|Horstman, Benjamin|Chen, Yixuan', 'Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu', 'United States|United States|United States', 30, 'Algorithms|Disease|Epistasis, Genetic|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btr227', '21685074', 'Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect single nucleotide polymorphisms (SNPs) essentially based on their marginal associations with phenotypes.'),
(76294, 2011, 'bio', 'Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering', 'Alexandrov, Theodore|Kobarg, Jan Hendrik', 'Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany. theodore@math.uni-bremen.de|Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany. theodore@math.uni-bremen.de', 'Germany|Germany', 0, 'Animals|Brain|Cluster Analysis|Mass Spectrometry|Neuroendocrine Tumors|Rats', 'Journal Article', '10.1093/bioinformatics/btr246', '21685075', 'Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability.'),
(76295, 2011, 'bio', 'Automatic 3D neuron tracing using all-path pruning', 'Peng, Hanchuan|Long, Fuhui|Myers, Gene', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janella.hhmi.org|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janella.hhmi.org|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janella.hhmi.org', 'United States|United States|United States', 92, 'Algorithms|Animals|Brain|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Neurons|Software', 'Journal Article', '10.1093/bioinformatics/btr237', '21685076', 'Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.'),
(76296, 2011, 'bio', 'Sequence-based prediction of protein crystallization, purification and production propensity', 'Mizianty, Marcin J|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.', 'Canada|Canada', 0, 'Amino Acids|Crystallography, X-Ray|Hydrophobic and Hydrophilic Interactions|Proteins|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btr229', '21685077', 'X-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.'),
(76297, 2011, 'bio', 'Tanglegrams for rooted phylogenetic trees and networks', 'Scornavacca, Celine|Zickmann, Franziska|Huson, Daniel H', 'Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany. scornava@informatik.uni-tuebingen.de|Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany. scornava@informatik.uni-tuebingen.de|Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany. scornava@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 46, 'Algorithms|Animals|Biological Evolution|Biology|Computational Biology|Computer Simulation|Ficus|Gene Transfer, Horizontal|Hybridization, Genetic|Phylogeny|Wasps', 'Journal Article', '10.1093/bioinformatics/btr210', '21685078', 'In systematic biology, one is often faced with the task of comparing different phylogenetic trees, in particular in multi-gene analysis or cospeciation studies. One approach is to use a tanglegram in which two rooted phylogenetic trees are drawn opposite each other, using auxiliary lines to connect matching taxa. There is an increasing interest in using rooted phylogenetic networks to represent evolutionary history, so as to explicitly represent reticulate events, such as horizontal gene transfer, hybridization or reassortment. Thus, the question arises how to define and compute a tanglegram for such networks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(76298, 2011, 'bio', 'Mapping ancestral genomes with massive gene loss: a matrix sandwich problem', 'Gavranovixc4x87, Haris|Chauve, Cedric|Salse, Jxc3xa9rxc3xb4me|Tannier, Eric', 'Faculty of Natural Sciences, University of Sarajevo, Bosnia and Herzegovina. haris.gavranovic@gmail.com|Faculty of Natural Sciences, University of Sarajevo, Bosnia and Herzegovina. haris.gavranovic@gmail.com|Faculty of Natural Sciences, University of Sarajevo, Bosnia and Herzegovina. haris.gavranovic@gmail.com|Faculty of Natural Sciences, University of Sarajevo, Bosnia and Herzegovina. haris.gavranovic@gmail.com', 'Bosnia and Herzegovina|Bosnia and Herzegovina|Bosnia and Herzegovina|Bosnia and Herzegovina', 11, 'Animals|Biological Evolution|Evolution, Molecular|Genome|Humans|Magnoliopsida|Mammals', 'Journal Article', '10.1093/bioinformatics/btr224', '21685079', 'Ancestral genomes provide a better way to understand the structural evolution of genomes than the simple comparison of extant genomes. Most ancestral genome reconstruction methods rely on universal markers, that is, homologous families of DNA segments present in exactly one exemplar in every considered species. Complex histories of genes or other markers, undergoing duplications and losses, are rarely taken into account. It follows that some ancestors are inaccessible by these methods, such as the proto-monocotyledon whose evolution involved massive gene loss following a whole genome duplication.'),
(76299, 2011, 'bio', 'Predicting site-specific human selective pressure using evolutionary signatures', 'Sadri, Javad|Diallo, Abdoulaye Banire|Blanchette, Mathieu', 'School of Computer Science, McGill University, 3630 University, Montreal, QC, Canada H3A 2B2.|School of Computer Science, McGill University, 3630 University, Montreal, QC, Canada H3A 2B2.|School of Computer Science, McGill University, 3630 University, Montreal, QC, Canada H3A 2B2.', 'Canada|Canada|Canada', 0, 'Animals|Artificial Intelligence|Biological Evolution|Genome|Genome, Human|Humans|Open Reading Frames|Phylogeny|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btr241', '21685080', 'The identification of non-coding functional regions of the human genome remains one of the main challenges of genomics. By observing how a given region evolved over time, one can detect signs of negative or positive selection hinting that the region may be functional. With the quickly increasing number of vertebrate genomes to compare with our own, this type of approach is set to become extremely powerful, provided the right analytical tools are available.'),
(76300, 2011, 'bio', 'PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions', 'Lin, Michael F|Jungreis, Irwin|Kellis, Manolis', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street 32-D510, Cambridge, MA 02139, USA. mlin@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street 32-D510, Cambridge, MA 02139, USA. mlin@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street 32-D510, Cambridge, MA 02139, USA. mlin@mit.edu', 'United States|United States|United States', 492, 'Animals|Base Sequence|Drosophila|Drosophila melanogaster|Gene Expression Profiling|Genomics|Mammals|Open Reading Frames|Schizosaccharomyces|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btr209', '21685081', 'As high-throughput transcriptome sequencing provides evidence for novel transcripts in many species, there is a renewed need for accurate methods to classify small genomic regions as protein coding or non-coding. We present PhyloCSF, a novel comparative genomics method that analyzes a multispecies nucleotide sequence alignment to determine whether it is likely to represent a conserved protein-coding region, based on a formal statistical comparison of phylogenetic codon models.'),
(76301, 2011, 'bio', 'The role of proteosome-mediated proteolysis in modulating potentially harmful transcription factor activity in Saccharomyces cerevisiae', 'Bonzanni, Nicola|Zhang, Nianshu|Oliver, Stephen G|Fisher, Jasmin', 'Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Gene Expression Regulation, Fungal|Histone Demethylases|Hydrolysis|Proteasome Endopeptidase Complex|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Stress, Physiological|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr211', '21685082', 'The appropriate modulation of the stress response to variable environmental conditions is necessary to maintain sustained viability in Saccharomyces cerevisiae. Particularly, controlling the abundance of proteins that may have detrimental effects on cell growth is crucial for rapid recovery from stress-induced quiescence.'),
(76302, 2011, 'bio', 'Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity', 'Furlotte, Nicholas A|Kang, Hyun Min|Ye, Chun|Eskin, Eleazar', 'Department of Computer Science, University of California, Los Angeles, CA 90024, USA. nfurlott@cs.ucla.edu|Department of Computer Science, University of California, Los Angeles, CA 90024, USA. nfurlott@cs.ucla.edu|Department of Computer Science, University of California, Los Angeles, CA 90024, USA. nfurlott@cs.ucla.edu|Department of Computer Science, University of California, Los Angeles, CA 90024, USA. nfurlott@cs.ucla.edu', 'United States|United States|United States|United States', 0, 'Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Plant|Humans|Models, Statistical|Yeasts', 'Journal Article', '10.1093/bioinformatics/btr221', '21685083', 'The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearsons correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearsons correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable.'),
(76303, 2011, 'bio', 'A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments', 'Mayhew, Michael B|Robinson, Joshua W|Jung, Boyoun|Haase, Steven B|Hartemink, Alexander J', 'Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu|Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu|Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu|Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu|Program in Computational Biology and Bioinformatics, Department of Computer Science, Center for Systems Biology, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA. michael.mayhew@duke.edu', 'United States|United States|United States|United States|United States', 4, 'Biomarkers|Cell Cycle|Models, Biological|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btr244', '21685084', 'To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cells position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers.'),
(76304, 2011, 'bio', 'Systematic exploration of error sources in pyrosequencing flowgram data', 'Balzer, Susanne|Malde, Ketil|Jonassen, Inge', 'Institute of Marine Research, P.O. Box 1870, N-5817 Bergen, Norway. susanne.balzer@imr.no|Institute of Marine Research, P.O. Box 1870, N-5817 Bergen, Norway. susanne.balzer@imr.no|Institute of Marine Research, P.O. Box 1870, N-5817 Bergen, Norway. susanne.balzer@imr.no', 'Norway|Norway|Norway', 67, 'Animals|Bass|Computer Simulation|High-Throughput Nucleotide Sequencing|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btr251', '21685085', '454 pyrosequencing, by Roche Diagnostics, has emerged as an alternative to Sanger sequencing when it comes to read lengths, performance and cost, but shows higher per-base error rates. Although there are several tools available for noise removal, targeting different application fields, data interpretation would benefit from a better understanding of the different error types.'),
(76305, 2011, 'bio', 'An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data', 'Jin, Guangxu|Zhao, Hong|Zhou, Xiaobo|Wong, Stephen T C', 'Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.|Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.|Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.|Systems Medicine and Bioengineering Department, Cornell University, Houston, TX 77030, USA.', 'United States|United States|United States|United States', 37, 'Cell Line, Tumor|Drug Combinations|Drug Interactions|Gene Expression Profiling|Humans|Models, Statistical|Pharmaceutical Preparations|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btr202', '21685086', 'Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data.'),
(76306, 2011, 'bio', 'Accurate estimation of heritability in genome wide studies using random effects models', 'Golan, David|Rosset, Saharon', 'School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel', 19, 'Algorithms|Genome|Genome-Wide Association Study|Humans|Likelihood Functions|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btr219', '21685087', 'Random effects models have recently been introduced as an approach for analyzing genome wide association studies (GWASs), which allows estimation of overall heritability of traits without explicitly identifying the genetic loci responsible. Using this approach, Yang et al. (2010) have demonstrated that the heritability of height is much higher than the ~10% associated with identified genetic factors. However, Yang et al. (2010) relied on a heuristic for performing estimation in this model.'),
(76307, 2011, 'bio', 'StructHDP: automatic inference of number of clusters and population structure from admixed genotype data', 'Shringarpure, Suyash|Won, Daegun|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. suyash@cs.cmu.edu|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. suyash@cs.cmu.edu|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. suyash@cs.cmu.edu', 'United States|United States|United States', 5, 'Animals|Cluster Analysis|Genetics, Population|Genotype|Humans|Models, Genetic|Population Groups|Songbirds', 'Journal Article', '10.1093/bioinformatics/btr242', '21685088', 'Clustering of genotype data is an important way of understanding similarities and differences between populations. A summary of populations through clustering allows us to make inferences about the evolutionary history of the populations. Many methods have been proposed to perform clustering on multilocus genotype data. However, most of these methods do not directly address the question of how many clusters the data should be divided into and leave that choice to the user.'),
(76308, 2011, 'bio', 'Reconstruction of genealogical relationships with applications to Phase III of HapMap', 'Kyriazopoulou-Panagiotopoulou, Sofia|Kashef Haghighi, Dorna|Aerni, Sarah J|Sundquist, Andreas|Bercovici, Sivan|Batzoglou, Serafim', 'Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu|Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu|Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu|Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu|Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu|Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu', 'United States|United States|United States|United States|United States|United States', 12, 'Algorithms|Animals|Genealogy and Heraldry|Humans|Markov Chains', 'Journal Article', '10.1093/bioinformatics/btr243', '21685089', 'Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt-niece versus niece-aunt or first cousins versus great aunt-niece).'),
(76309, 2011, 'bio', 'A method for probing the mutational landscape of amyloid structure', 'ODonnell, Charles W|Waldispxc3xbchl, Jxc3xa9rxc3xb4me|Lis, Mieszko|Halfmann, Randal|Devadas, Srinivas|Lindquist, Susan|Berger, Bonnie', 'Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States|United States|United States|United States', 39, 'Algorithms|Alzheimer Disease|Amyloid|Amyloid beta-Protein Precursor|Humans|Mutation|Protein Structure, Secondary|Yeasts', 'Journal Article', '10.1093/bioinformatics/btr238', '21685090', 'Proteins of all kinds can self-assemble into highly ordered xcexb2-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods.'),
(76310, 2011, 'bio', 'ccSVM: correcting Support Vector Machines for confounding factors in biological data classification', 'Li, Limin|Rakitsch, Barbara|Borgwardt, Karsten', 'Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany. limin.li@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany. limin.li@tuebingen.mpg.de|Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany. limin.li@tuebingen.mpg.de', 'Germany|Germany|Germany', 18, 'Age Factors|Area Under Curve|Artificial Intelligence|Computational Biology|Humans|Leukemia, Myeloid, Acute|Microarray Analysis|Phenotype|Plants|Sex Factors|Tuberculosis', 'Journal Article', '10.1093/bioinformatics/btr204', '21685091', 'Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.'),
(76311, 2011, 'bio', 'Ontology patterns for tabular representations of biomedical knowledge on neglected tropical diseases', 'Santana, Filipe|Schober, Daniel|Medeiros, Zulma|Freitas, Fred|Schulz, Stefan', 'Informatics Center, Federal University of Pernambuco (CIn/UFPE), Recife, Brazil. fss3@cin.ufpe.br|Informatics Center, Federal University of Pernambuco (CIn/UFPE), Recife, Brazil. fss3@cin.ufpe.br|Informatics Center, Federal University of Pernambuco (CIn/UFPE), Recife, Brazil. fss3@cin.ufpe.br|Informatics Center, Federal University of Pernambuco (CIn/UFPE), Recife, Brazil. fss3@cin.ufpe.br|Informatics Center, Federal University of Pernambuco (CIn/UFPE), Recife, Brazil. fss3@cin.ufpe.br', 'Brazil|Brazil|Brazil|Brazil|Brazil', 7, 'Animals|Disease Vectors|Humans|Internet|Knowledge|Neglected Diseases|Parasitic Diseases|Programming Languages|Software|Terminology as Topic|Tropical Climate', 'Journal Article', '10.1093/bioinformatics/btr226', '21685092', 'Ontology-like domain knowledge is frequently published in a tabular format embedded in scientific publications. We explore the re-use of such tabular content in the process of building NTDO, an ontology of neglected tropical diseases (NTDs), where the representation of the interdependencies between hosts, pathogens and vectors plays a crucial role.'),
(76312, 2011, 'bio', 'Detection and interpretation of metabolite-transcript coresponses using combined profiling data', 'Redestig, Henning|Costa, Ivan G', 'RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp', 'Japan|Japan', 20, 'Adaptation, Physiological|Arabidopsis|Computational Biology|Gene Expression Profiling|Markov Chains|Metabolic Networks and Pathways|Metabolomics|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btr231', '21685093', 'Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.'),
(76313, 2011, 'bio', 'From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems', 'Geistlinger, Ludwig|Csaba, Gergely|Küffner, Robert|Mulder, Nicola|Zimmer, Ralf', 'Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de|Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de|Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de|Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de|Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany', 44, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Neoplasms, Nerve Tissue|Proteins|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btr228', '21685094', 'Current gene set enrichment approaches do not take interactions and associations between set members into account. Mutual activation and inhibition causing positive and negative correlation among set members are thus neglected. As a consequence, inconsistent regulations and contextless expression changes are reported and, thus, the biological interpretation of the result is impeded.'),
(76314, 2011, 'bio', 'Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning', 'Airoldi, Edoardo M|Heller, Katherine A|Silva, Ricardo', 'Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA. airoldi@fas.harvard.edu|Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA. airoldi@fas.harvard.edu|Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA. airoldi@fas.harvard.edu', 'United States|United States|United States', 0, 'Bayes Theorem|Computational Biology|Proteins|Saccharomycetales|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btr236', '21685095', 'Proteins and protein complexes coordinate their activity to execute cellular functions. In a number of experimental settings, including synthetic genetic arrays, genetic perturbations and RNAi screens, scientists identify a small set of protein interactions of interest. A working hypothesis is often that these interactions are the observable phenotypes of some functional process, which is not directly observable. Confirmatory analysis requires finding other pairs of proteins whose interaction may be additional phenotypical evidence about the same functional process. Extant methods for finding additional protein interactions rely heavily on the information in the newly identified set of interactions. For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs. A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence.'),
(76315, 2011, 'bio', 'Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling', 'xc5x81abaj, Pawexc5x82 P|Leparc, Germxc3xa1n G|Linggi, Bryan E|Markillie, Lye Meng|Wiley, H Steven|Kreil, David P', 'Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.|Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.|Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.|Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.|Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.|Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Cell Line|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Microarray Analysis|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr247', '21685096', 'Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means.'),
(76316, 2011, 'bio', 'An integrative clustering and modeling algorithm for dynamical gene expression data', 'Sivriver, Julia|Habib, Naomi|Friedman, Nir', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.', 'Israel|Israel|Israel', 0, 'Algorithms|Animals|Cells, Cultured|Cluster Analysis|Dendritic Cells|Gene Expression Profiling|Gram-Negative Bacterial Infections|Lipopolysaccharides|Mice|Oligonucleotide Array Sequence Analysis|Poly I-C|RNA Virus Infections|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btr250', '21685097', 'The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge.'),
(76317, 2011, 'bio', 'A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules', 'Zhang, Shihua|Li, Qingjiao|Liu, Juan|Zhou, Xianghong Jasmine', 'Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn', 'United States|United States|United States|United States', 0, '3 Untranslated Regions|Algorithms|Cluster Analysis|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|MicroRNAs|Ovarian Neoplasms|Regulatory Elements, Transcriptional', 'Journal Article', '10.1093/bioinformatics/btr206', '21685098', 'It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA-gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes.'),
(76318, 2011, 'bio', 'Piecewise linear approximation of protein structures using the principle of minimum message length', 'Konagurthu, Arun S|Allison, Lloyd|Stuckey, Peter J|Lesk, Arthur M', 'Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia. arun.konagurthu@monash.edu|Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia. arun.konagurthu@monash.edu|Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia. arun.konagurthu@monash.edu|Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia. arun.konagurthu@monash.edu', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Clostridium|Hydrogen Bonding|Models, Molecular|Protein Folding|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1093/bioinformatics/btr240', '21685100', 'Simple and concise representations of protein-folding patterns provide powerful abstractions for visualizations, comparisons, classifications, searching and aligning structural data. Structures are often abstracted by replacing standard secondary structural features-that is, helices and strands of sheet-by vectors or linear segments. Relying solely on standard secondary structure may result in a significant loss of structural information. Further, traditional methods of simplification crucially depend on the consistency and accuracy of external methods to assign secondary structures to protein coordinate data. Although many methods exist automatically to identify secondary structure, the impreciseness of definitions, along with errors and inconsistencies in experimental structure data, drastically limit their applicability to generate reliable simplified representations, especially for structural comparison. This article introduces a mathematically rigorous algorithm to delineate protein structure using the elegant statistical and inductive inference framework of minimum message length (MML). Our method generates consistent and statistically robust piecewise linear explanations of protein coordinate data, resulting in a powerful and concise representation of the structure. The delineation is completely independent of the approaches of using hydrogen-bonding patterns or inspecting local substructural geometry that the current methods use. Indeed, as is common with applications of the MML criterion, this method is free of parameters and thresholds, in striking contrast to the existing programs which are often beset by them. The analysis of results over a large number of proteins suggests that the method produces consistent delineation of structures that encompasses, among others, the segments corresponding to standard secondary structure.'),
(76319, 2011, 'bio', 'QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin', 'Savol, Andrej J|Burger, Virginia M|Agarwal, Pratul K|Ramanathan, Arvind|Chennubhotla, Chakra S', 'Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA.|Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA.', 'United States|United States|United States|United States|United States', 0, 'Computer Simulation|Humans|Markov Chains|Models, Molecular|Molecular Dynamics Simulation|Motion|Protein Conformation|Ubiquitin', 'Journal Article', '10.1093/bioinformatics/btr248', '21685101', 'Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function. These growing datasets have necessitated a corresponding emphasis on trajectory analysis methods for characterizing simulation data, particularly since functional protein motions and transitions are often rare and/or intricate events. Observing that such events give rise to long-tailed spatial distributions, we recently developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic analysis (QAA), for identifying biophysically-relevant reaction coordinates and substates within MD simulations. Further characterization of conformation space should consider the temporal dynamics specific to each identified substate.'),
(76320, 2011, 'bio', 'Multi-view methods for protein structure comparison using latent dirichlet allocation', 'Shivashankar, S|Srivathsan, S|Ravindran, B|Tendulkar, Ashish V', 'Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.|Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.|Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.|Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.', '-|-|-|-', 15, 'Algorithms|Databases, Protein|Information Storage and Retrieval|Models, Molecular|Natural Language Processing|Proteins|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btr249', '21685102', 'With rapidly expanding protein structure databases, efficiently retrieving structures similar to a given protein is an important problem. It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements. Recently, researchers proposed vector space model of proteins using bag of fragments representation (FragBag), which corresponds to the basic information retrieval model.'),
(76321, 2011, 'bio', 'Template-free detection of macromolecular complexes in cryo electron tomograms', 'Xu, Min|Beck, Martin|Alber, Frank', 'Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States', 0, 'Cells|Cryoelectron Microscopy|Electron Microscope Tomography|Markov Chains|Multiprotein Complexes', 'Journal Article', '10.1093/bioinformatics/btr207', '21685103', 'Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.'),
(76322, 2011, 'bio', 'Sorting the nuclear proteome', 'Bauer, Denis C|Willadsen, Kai|Buske, Fabian A|Lê Cao, Kim-Anh|Bailey, Timothy L|Dellaire, Graham|Bodén, Mikael', 'Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.|Queensland Brain Institute, School of Chemistry and Molecular Biosciences, Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 16, 'Animals|Bayes Theorem|Cell Nucleus|Gene Expression|Intranuclear Inclusion Bodies|Mice|Nuclear Proteins|Protein Processing, Post-Translational|Protein Transport|Proteome|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr217', '21685104', 'Quantitative experimental analyses of the nuclear interior reveal a morphologically structured yet dynamic mix of membraneless compartments. Major nuclear events depend on the functional integrity and timely assembly of these intra-nuclear compartments. Yet, unknown drivers of protein mobility ensure that they are in the right place at the time when they are needed.'),
(76323, 2011, 'bio', 'vipR: variant identification in pooled DNA using R', 'Altmann, Andre|Weber, Peter|Quast, Carina|Rex-Haffner, Monika|Binder, Elisabeth B|Müller-Myhsok, Bertram', 'Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de|Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de|Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de|Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de|Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de|Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. altmann@mpipsykl.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 33, 'Disease|Gene Frequency|Genetic Predisposition to Disease|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Poisson Distribution|Polymorphism, Single Nucleotide|Software|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/btr205', '21685105', 'High-throughput-sequencing (HTS) technologies are the method of choice for screening the human genome for rare sequence variants causing susceptibility to complex diseases. Unfortunately, preparation of samples for a large number of individuals is still very cost- and labor intensive. Thus, recently, screens for rare sequence variants were carried out in samples of pooled DNA, in which equimolar amounts of DNA from multiple individuals are mixed prior to sequencing with HTS. The resulting sequence data, however, poses a bioinformatics challenge: the discrimination of sequencing errors from real sequence variants present at a low frequency in the DNA pool.'),
(76324, 2011, 'bio', 'IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming', 'Sato, Kengo|Kato, Yuki|Hamada, Michiaki|Akutsu, Tatsuya|Asai, Kiyoshi', 'Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr215', '21685106', 'Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy.'),
(76325, 2011, 'bio', 'Meta-IDBA: a de Novo assembler for metagenomic data', 'Peng, Yu|Leung, Henry C M|Yiu, S M|Chin, Francis Y L', 'Department of Computer Science, The University of Hong Kong, Hong Kong.|Department of Computer Science, The University of Hong Kong, Hong Kong.|Department of Computer Science, The University of Hong Kong, Hong Kong.|Department of Computer Science, The University of Hong Kong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 192, 'Algorithms|Escherichia coli|Genome, Bacterial|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btr216', '21685107', 'Next-generation sequencing techniques allow us to generate reads from a microbial environment in order to analyze the microbial community. However, assembling of a set of mixed reads from different species to form contigs is a bottleneck of metagenomic research. Although there are many assemblers for assembling reads from a single genome, there are no assemblers for assembling reads in metagenomic data without reference genome sequences. Moreover, the performances of these assemblers on metagenomic data are far from satisfactory, because of the existence of common regions in the genomes of subspecies and species, which make the assembly problem much more complicated.'),
(76326, 2011, 'bio', 'False positive peaks in ChIP-seq and other sequencing-based functional assays caused by unannotated high copy number regions', 'Pickrell, Joseph K|Gaffney, Daniel J|Gilad, Yoav|Pritchard, Jonathan K', 'Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. pickrell@uchicago.edu|Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. pickrell@uchicago.edu|Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. pickrell@uchicago.edu|Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. pickrell@uchicago.edu', 'United States|United States|United States|United States', 50, 'Base Sequence|Chromatin Immunoprecipitation|Computational Biology|Gene Dosage|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btr354', '21690102', 'Sequencing-based assays such as ChIP-seq, DNase-seq and MNase-seq have become important tools for genome annotation. In these assays, short sequence reads enriched for loci of interest are mapped to a reference genome to determine their origin. Here, we consider whether false positive peak calls can be caused by particular type of error in the reference genome: multicopy sequences which have been incorrectly assembled and collapsed into a single copy.'),
(76327, 2011, 'bio', 'RIP: the regulatory interaction predictor--a machine learning-based approach for predicting target genes of transcription factors', 'Bauer, Tobias|Eils, Roland|König, Rainer', 'Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 14, 'Binding Sites|Gene Expression Profiling|Gene Expression Regulation|Genomics|Humans|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btr366', '21690103', 'Understanding transcriptional gene regulation is essential for studying cellular systems. Identifying genome-wide targets of transcription factors (TFs) provides the basis to discover the involvement of TFs and TF cooperativeness in cellular systems and pathogenesis.'),
(76328, 2011, 'bio', 'SpEED: fast computation of sensitive spaced seeds', 'Ilie, Lucian|Ilie, Silvana|Bigvand, Anahita Mansouri', 'Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada. ilie@csd.uwo.ca|Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada. ilie@csd.uwo.ca|Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada. ilie@csd.uwo.ca', 'Canada|Canada|Canada', 26, 'Algorithms|Computational Biology|Sequence Alignment|Software', 'Evaluation Studies', '10.1093/bioinformatics/btr368', '21690104', 'Multiple spaced seeds represent the current state-of-the-art for similarity search in bioinformatics, with applications in various areas such as sequence alignment, read mapping, oligonucleotide design, etc. We present SpEED, a software program that computes highly sensitive multiple spaced seeds. SpEED can be several orders of magnitude faster and computes better seeds than the existing leading software programs.'),
(76329, 2011, 'bio', 'Varying coefficient model for gene-environment interaction: a non-linear look', 'Ma, Shujie|Yang, Lijian|Romero, Roberto|Cui, Yuehua', 'Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA.|Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA.|Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA.|Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA.', 'United States|United States|United States|United States', 30, 'Computational Biology|Computer Simulation|Environment|Genetic Predisposition to Disease|Humans|Infant, Newborn|Models, Genetic|Models, Statistical|Monte Carlo Method|Nonlinear Dynamics|Penetrance|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btr318', '21690105', 'The genetic basis of complex traits often involves the function of multiple genetic factors, their interactions and the interaction between the genetic and environmental factors. Gene-environment (G×E) interaction is considered pivotal in determining trait variations and susceptibility of many genetic disorders such as neurodegenerative diseases or mental disorders. Regression-based methods assuming a linear relationship between a disease response and the genetic and environmental factors as well as their interaction is the commonly used approach in detecting G×E interaction. The linearity assumption, however, could be easily violated due to non-linear genetic penetrance which induces non-linear G×E interaction.'),
(76330, 2011, 'bio', 'Identification of novel transcripts in annotated genomes using RNA-Seq', 'Roberts, Adam|Pimentel, Harold|Trapnell, Cole|Pachter, Lior', 'Department of Computer Science, UC Berkeley, Berkeley, CA, USA.|Department of Computer Science, UC Berkeley, Berkeley, CA, USA.|Department of Computer Science, UC Berkeley, Berkeley, CA, USA.|Department of Computer Science, UC Berkeley, Berkeley, CA, USA.', 'United States|United States|United States|United States', 612, 'Algorithms|Animals|Gene Expression Profiling|Genome|Humans|Molecular Sequence Annotation|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btr355', '21697122', 'We describe a new reference annotation based transcript assembly problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation.');
