INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78448, 2014, 'bio', 'SMaSH: a benchmarking toolkit for human genome variant calling', 'Talwalkar, Ameet|Liptrap, Jesse|Newcomb, Julie|Hartl, Christopher|Terhorst, Jonathan|Curtis, Kristal|Bresler, Maayan|Song, Yun S|Jordan, Michael I|Patterson, David', 'Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.|Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA 94720, USA, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Department of Statistics, UC Berkeley, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btu345', '24894505', 'Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers.'),
(78449, 2014, 'bio', 'SensA: web-based sensitivity analysis of SBML models', 'Floettmann, Max|Uhlendorf, Jannis|Scharp, Till|Klipp, Edda|Spiesser, Thomas W', 'Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Biochemical Phenomena|Computational Biology|Extracellular Signal-Regulated MAP Kinases|Internet|Models, Theoretical|Phosphorylation|Programming Languages|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btu378', '24903418', 'SensA is a web-based application for sensitivity analysis of mathematical models. The sensitivity analysis is based on metabolic control analysis, computing the local, global and time-dependent properties of model components. Interactive visualization facilitates interpretation of usually complex results. SensA can contribute to the analysis, adjustment and understanding of mathematical models for dynamic systems.'),
(78450, 2014, 'bio', 'Gene set analysis: limitations in popular existing methods and proposed improvements', 'Mishra, Pashupati|Törönen, Petri|Leino, Yrjö|Holm, Liisa', 'Institute of Biotechnology, University of Helsinki, Helsinki, Finland and CSC - IT Center for Science, Ltd., Espoo, Finland.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland and CSC - IT Center for Science, Ltd., Espoo, Finland.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland and CSC - IT Center for Science, Ltd., Espoo, Finland.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland and CSC - IT Center for Science, Ltd., Espoo, Finland.', 'Finland|Finland|Finland|Finland', 7, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Escherichia coli|Female|Gene Expression Profiling|Gene Expression Regulation, Leukemic|Humans|Leukemia|Male|Models, Statistical|Sex Factors|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btu374', '24903419', 'Gene set analysis is the analysis of a set of genes that collectively contribute to a biological process. Most popular gene set analysis methods are based on empirical P-value that requires large number of permutations. Despite numerous gene set analysis methods developed in the past decade, the most popular methods still suffer from serious limitations.'),
(78451, 2014, 'bio', 'SushiR: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures', 'Phanstiel, Douglas H|Boyle, Alan P|Araya, Carlos L|Snyder, Michael P', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.', 'United States|United States|United States|United States', 65, 'Algorithms|Computational Biology|Genome-Wide Association Study|Genomics|Internet|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btu379', '24903420', 'Interpretation and communication of genomic data require flexible and quantitative tools to analyze and visualize diverse data types, and yet, a comprehensive tool to display all common genomic data types in publication quality figures does not exist to date. To address this shortcoming, we present Sushi.R, an R/Bioconductor package that allows flexible integration of genomic visualizations into highly customizable, publication-ready, multi-panel figures from common genomic data formats including Browser Extensible Data (BED), bedGraph and Browser Extensible Data Paired-End (BEDPE). Sushi.R is open source and made publicly available through GitHub (https://github.com/dphansti/Sushi) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/Sushi.html).'),
(78452, 2014, 'bio', 'Improving translation initiation site and stop codon recognition by using more than two classes', 'Pérez-Rodríguez, Javier|Arroyo-Peña, Alexis G|García-Pedrajas, Nicolás', 'Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.|Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.|Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.', 'Spain|Spain|Spain', 0, 'Base Sequence|Codon, Initiator|Codon, Terminator|Computational Biology|Genome, Human|Humans|Protein Biosynthesis|ROC Curve|Reproducibility of Results|Sensitivity and Specificity|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btu369', '24903421', 'The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy.'),
(78453, 2014, 'bio', 'MAGI: a Nodejs web service for fast microRNA-Seq analysis in a GPU infrastructure', 'Kim, Jihoon|Levy, Eric|Ferbrache, Alex|Stepanowsky, Petra|Farcas, Claudiu|Wang, Shuang|Brunner, Stefan|Bath, Tyler|Wu, Yuan|Ohno-Machado, Lucila', 'Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.|Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 14, 'Computational Biology|Computer Graphics|Internet|MicroRNAs|Programming Languages|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btu377', '24907367', 'MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours->600% end-to-end performance improvement over state of the art. MAGIs salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication.'),
(78454, 2014, 'bio', 'Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction', 'Parker, Hilary S|Leek, Jeffrey T|Favorov, Alexander V|Considine, Michael|Xia, Xiaoxin|Chavan, Sameer|Chung, Christine H|Fertig, Elana J', 'Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.|Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms GosNIIGenetika, Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 23, 'Algorithms|Artifacts|Computational Biology|Genomics|Head and Neck Neoplasms|Humans|Models, Statistical|Papillomavirus Infections|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btu375', '24907368', 'Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori.'),
(78455, 2014, 'bio', 'ABRA: improved coding indel detection via assembly-based realignment', 'Mose, Lisle E|Wilkerson, Matthew D|Hayes, D Neil|Perou, Charles M|Parker, Joel S', 'Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, Department of Genetics, Division of Medical Oncology, Department of Internal Medicine, Multidisciplinary Thoracic Oncology Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States|United States', 70, 'Algorithms|Computational Biology|Gene Frequency|Genome, Human|Genotype|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu376', '24907369', 'Variant detection from next-generation sequencing (NGS) data is an increasingly vital aspect of disease diagnosis, treatment and research. Commonly used NGS-variant analysis tools generally rely on accurately mapped short reads to identify somatic variants and germ-line genotypes. Existing NGS read mappers have difficulty accurately mapping short reads containing complex variation (i.e. more than a single base change), thus making identification of such variants difficult or impossible. Insertions and deletions (indels) in particular have been an area of great difficulty. Indels are frequent and can have substantial impact on function, which makes their detection all the more imperative.'),
(78456, 2014, 'bio', 'MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach', 'Daly, Rónán|Rogers, Simon|Wandy, Joe|Jankevics, Andris|Burgess, Karl E V|Breitling, Rainer', 'School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester and Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 41, 'Algorithms|Bayes Theorem|Chromatography, Liquid|Cluster Analysis|Cysteic Acid|Data Interpretation, Statistical|Mass Spectrometry|Metabolomics|Normal Distribution|Probability|Reproducibility of Results|Software|Triazoles', 'Journal Article', '10.1093/bioinformatics/btu370', '24916385', 'The use of liquid chromatography coupled to mass spectrometry has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This article looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite.'),
(78457, 2014, 'bio', 'Network-guided regression for detecting associations between DNA methylation and gene expression', 'Wang, Zi|Curry, Edward|Montana, Giovanni', 'Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, Kings College London, St Thomas Hospital, London SE1 7EH, UK.|Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, Kings College London, St Thomas Hospital, London SE1 7EH, UK.|Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, Kings College London, St Thomas Hospital, London SE1 7EH, UK Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, Kings College London, St Thomas Hospital, London SE1 7EH, UK.', 'United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Computational Biology|DNA Methylation|Epistasis, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Models, Statistical|Neoplasms|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btu361', '24919878', 'High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. These data could yield discoveries that may lead to breakthroughs in the diagnosis and treatment of human disease, but require statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.'),
(78458, 2014, 'bio', 'Blue: correcting sequencing errors using consensus and context', 'Greenfield, Paul|Duesing, Konsta|Papanicolaou, Alexie|Bauer, Denis C', 'CSIRO Computational Informatics, School of IT, University of Sydney, CSIRO Animal, Food and Health Sciences, Sydney, NSW 2113, and CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia CSIRO Computational Informatics, School of IT, University of Sydney, CSIRO Animal, Food and Health Sciences, Sydney, NSW 2113, and CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia.|CSIRO Computational Informatics, School of IT, University of Sydney, CSIRO Animal, Food and Health Sciences, Sydney, NSW 2113, and CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia.|CSIRO Computational Informatics, School of IT, University of Sydney, CSIRO Animal, Food and Health Sciences, Sydney, NSW 2113, and CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia.|CSIRO Computational Informatics, School of IT, University of Sydney, CSIRO Animal, Food and Health Sciences, Sydney, NSW 2113, and CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia.', 'Australia|Australia|Australia|Australia', 42, 'Algorithms|Animals|Computational Biology|DNA, Bacterial|Databases, Genetic|Genome|Genome, Bacterial|Genome, Human|Humans|Ploidies|Reproducibility of Results|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btu368', '24919879', 'Bioinformatics tools, such as assemblers and aligners, are expected to produce more accurate results when given better quality sequence data as their starting point. This expectation has led to the development of stand-alone tools whose sole purpose is to detect and remove sequencing errors. A good error-correcting tool would be a transparent component in a bioinformatics pipeline, simply taking sequence data in any of the standard formats and producing a higher quality version of the same data containing far fewer errors. It should not only be able to correct all of the types of errors found in real sequence data (substitutions, insertions, deletions and uncalled bases), but it has to be both fast enough and scalable enough to be usable on the large datasets being produced by current sequencing technologies, and work on data derived from both haploid and diploid organisms.'),
(78459, 2014, 'bio', 'Predicting dynamic signaling network response under unseen perturbations', 'Zhu, Fan|Guan, Yuanfang', 'Department of Computational Medicine and Bioinformatics, Department of Internal Medicine and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, Department of Internal Medicine and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA Department of Computational Medicine and Bioinformatics, Department of Internal Medicine and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA Department of Computational Medicine and Bioinformatics, Department of Internal Medicine and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States', 8, 'Algorithms|Computational Biology|Computer Simulation|Genome, Human|Humans|Phosphorylation|Proteins|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btu382', '24919880', 'Predicting trajectories of signaling networks under complex perturbations is one of the most valuable, but challenging, tasks in systems biology. Signaling networks are involved in most of the biological pathways, and modeling their dynamics has wide applications including drug design and treatment outcome prediction.'),
(78460, 2014, 'bio', 'NetBioV: an R package for visualizing large network data in biology and medicine', 'Tripathi, Shailesh|Dehmer, Matthias|Emmert-Streib, Frank', 'Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK, Institute for Bioinformatics and Translational Research, UMIT, 6060, Hall in Tyrol, Austria and Department of Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK, Institute for Bioinformatics and Translational Research, UMIT, 6060, Hall in Tyrol, Austria and Department of Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK, Institute for Bioinformatics and Translational Research, UMIT, 6060, Hall in Tyrol, Austria and Department of Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast BT9 7BL, UK, Institute for Bioinformatics and Translational Research, UMIT, 6060, Hall in Tyrol, Austria and Department of Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany.', 'Austria|Austria|Austria', 20, 'Algorithms|Arabidopsis|Computational Biology|Computer Graphics|Gene Regulatory Networks|Humans|Lymphoma, B-Cell|Programming Languages|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btu384', '24928209', 'NetBioV (Network Biology Visualization) is an R package that allows the visualization of large network data in biology and medicine. The purpose of NetBioV is to enable an organized and reproducible visualization of networks by emphasizing or highlighting specific structural properties that are of biological relevance.'),
(78461, 2014, 'bio', 'Detecting differential protein expression in large-scale population proteomics', 'Ryu, So Young|Qian, Wei-Jun|Camp, David G|Smith, Richard D|Tompkins, Ronald G|Davis, Ronald W|Xiao, Wenzhong', 'Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.|Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA, Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.', 'United States|United States|United States|United States|United States|United States|United States', 12, 'Computational Biology|Computer Simulation|Gene Expression Regulation|Humans|Mass Spectrometry|Peptides|Proteins|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btu341', '24928210', 'Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different MS experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. To detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges.'),
(78462, 2014, 'bio', 'BioBlendobjects: metacomputing with Galaxy', 'Leo, Simone|Pireddu, Luca|Cuccuru, Gianmauro|Lianas, Luca|Soranzo, Nicola|Afgan, Enis|Zanetti, Gianluigi', 'CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.|CRS4, Polaris, 09010 Pula (CA), Universitxc3xa0 degli Studi di Cagliari, via Universitxc3xa0 40, 09124 Cagliari, Italy and Ruxc4x91er Boxc5xa1kovixc4x87 Institute, 10000 Zagreb, Croatia.', 'Croatia|Croatia|Croatia|Croatia|Croatia|Croatia|Croatia', 0, 'Algorithms|Automation|Computational Biology|Computer Graphics|Computer Systems|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btu386', '24928211', 'BioBlend.objects is a new component of the BioBlend package, adding an object-oriented interface for the Galaxy REST-based application programming interface. It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets and workflows.'),
(78463, 2014, 'bio', 'The Scramble conversion tool', 'Bonfield, James K', 'DNA Pipelines, Wellcome Trust Sanger Institute, Cambridgeshire, CB10 1SA, UK.', 'United Kingdom', 26, 'Computational Biology|Computers|Escherichia coli|Genome, Bacterial|Genome, Human|Humans|Information Storage and Retrieval|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btu390', '24930138', 'The reference CRAM file format implementation is in Java. We present Scramble: a new C implementation of SAM, BAM and CRAM file I/O.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78464, 2014, 'bio', 'circlize Implements and enhances circular visualization in R', 'Gu, Zuguang|Gu, Lei|Eils, Roland|Schlesner, Matthias|Brors, Benedikt', 'Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Center for Personalized Oncology (DKFZ-HIPO) and Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Center, Heidelberg University, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Graphics|Genome|Genomics|Internet|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btu393', '24930139', 'Circular layout is an efficient way for the visualization of huge amounts of genomic information. Here we present the circlize package, which provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of this package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, circlize gives users more convenience and freedom to design figures for better understanding genomic patterns behind multi-dimensional data.'),
(78465, 2014, 'bio', 'miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data', 'Lei, Jikai|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 0, 'Algorithms|Base Sequence|Benchmarking|Computational Biology|False Positive Reactions|Genome|MicroRNAs|Plants|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btu380', '24930140', 'Plant microRNA prediction tools that use small RNA-sequencing data are emerging quickly. These existing tools have at least one of the following problems: (i) high false-positive rate, (ii) long running time, (iii) work only for genomes in their databases, (iv) hard to install or use. We developed miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species. We tested miR-PREFeR on several plant species. The results show that miR-PREFeR is sensitive, accurate, fast and has low-memory footprint.'),
(78466, 2014, 'bio', 'Cloud4Psi: cloud computing for 3D protein structure similarity searching', 'Mrozek, Dariusz|Maxc5x82ysiak-Mrozek, Boxc5xbcena|Kxc5x82apcixc5x84ski, Artur', 'Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.', 'Poland|Poland|Poland', 0, 'Algorithms|Computational Biology|Computer Systems|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btu389', '24930141', 'Popular methods for 3D protein structure similarity searching, especially those that generate high-quality alignments such as Combinatorial Extension (CE) and Flexible structure Alignment by Chaining Aligned fragment pairs allowing Twists (FATCAT) are still time consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units.'),
(78467, 2014, 'bio', 'Evaluation and validation of de novo and hybrid assembly techniques to derive high-quality genome sequences', 'Utturkar, Sagar M|Klingeman, Dawn M|Land, Miriam L|Schadt, Christopher W|Doktycz, Mitchel J|Pelletier, Dale A|Brown, Steven D', 'Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37919, USA and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Contig Mapping|DNA, Bacterial|DNA, Ribosomal|Genomics|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu391', '24930142', 'To assess the potential of different types of sequence data combined with de novo and hybrid assembly approaches to improve existing draft genome sequences.'),
(78468, 2014, 'bio', 'LICRE: unsupervised feature correlation reduction for lipidomics', 'Wong, Gerard|Chan, Jeffrey|Kingwell, Bronwyn A|Leckie, Christopher|Meikle, Peter J', 'Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.|Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.|Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.|Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.|Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.', 'Australia|Australia|Australia|Australia|Australia', 5, 'Algorithms|Biomarkers|Cardiovascular Diseases|Chromatography, Liquid|Cluster Analysis|Computational Biology|Diabetes Mellitus|Humans|Lipids|ROC Curve|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btu381', '24930143', 'Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective.'),
(78469, 2014, 'bio', 'Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies', 'Messih, Mario Abdel|Lepore, Rosalba|Marcatili, Paolo|Tramontano, Anna', 'Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.|Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.', 'Denmark|Denmark|Denmark|Denmark', 0, 'Algorithms|Antibodies|Antigens|Complementarity Determining Regions|Computational Biology|Immunoglobulin Heavy Chains|Models, Molecular|Protein Conformation|Protein Engineering|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btu194', '24930144', 'Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition.'),
(78470, 2014, 'bio', 'Computational biology and bioinformatics', 'Batzoglou, Serafim|Schwartz, Russell', 'Computer Science Department, Stanford University and Department of Biological Sciences and Lane Center for Computational Biology, Carnegie Mellon University.|Computer Science Department, Stanford University and Department of Biological Sciences and Lane Center for Computational Biology, Carnegie Mellon University.', '-|-', 0, 'Computational Biology', 'Editorial', '10.1093/bioinformatics/btu304', '24931972', '-'),
(78471, 2014, 'bio', 'Cross-study validation for the assessment of prediction algorithms', 'Bernau, Christoph|Riester, Markus|Boulesteix, Anne-Laure|Parmigiani, Giovanni|Huttenhower, Curtis|Waldron, Levi|Trippa, Lorenzo', 'Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USALeibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USALeibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USALeibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.|Leibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USALeibniz Supercomputing Center, Garching, Department for Medical Informatics, Biometry and Epidemiology, Munich, Germany, Cambridge, MA, Dana-Farber Cancer Institute, Boston, Harvard School of Public Health, Boston, USA and City University of New York School of Public Health, Hunter College, New York, USA.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 35, 'Algorithms|Artificial Intelligence|Breast Neoplasms|Disease-Free Survival|Female|Gene Expression Profiling|Humans|Neoplasm Metastasis|Oligonucleotide Array Sequence Analysis|Prognosis|Reproducibility of Results', 'Evaluation Studies', '10.1093/bioinformatics/btu279', '24931973', 'Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings. Cross-validation within exemplary datasets may not adequately reflect performance in the broader application context.'),
(78472, 2014, 'bio', 'Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation', 'Äijö, Tarmo|Butty, Vincent|Chen, Zhi|Salo, Verna|Tripathi, Subhash|Burge, Christopher B|Lahesmaa, Riitta|Lähdesmäki, Harri', 'Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.|Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, FinlandDepartment of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Cell Differentiation|Gene Expression Profiling|Humans|Infant, Newborn|Normal Distribution|Sequence Analysis, RNA|Th17 Cells', 'Journal Article', '10.1093/bioinformatics/btu274', '24931974', 'Gene expression profiling using RNA-seq is a powerful technique for screening RNA species landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.'),
(78473, 2014, 'bio', 'Deep learning of the tissue-regulated splicing code', 'Leung, Michael K K|Xiong, Hui Yuan|Lee, Leo J|Frey, Brendan J', 'Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.', 'Canada|Canada|Canada|Canada', 203, 'Algorithms|Alternative Splicing|Animals|Artificial Intelligence|Bayes Theorem|Genomics|Humans|Mice|Neural Networks (Computer)|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btu277', '24931975', 'Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS.'),
(78474, 2014, 'bio', 'Functional association networks as priors for gene regulatory network inference', 'Studham, Matthew E|Tjärnberg, Andreas|Nordling, Torbjörn E M|Nelander, Sven|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btu285', '24931976', 'Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences.'),
(78475, 2014, 'bio', 'Graph-regularized dual Lasso for robust eQTL mapping', 'Cheng, Wei|Zhang, Xiang|Guo, Zhishan|Shi, Yu|Wang, Wei', 'Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC 27599, Department of EECS, Case Western Reserve University, OH 44106, USA Department of Mathematics, University of Science and Technology of China, Hefei 23002, China and Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC 27599, Department of EECS, Case Western Reserve University, OH 44106, USA Department of Mathematics, University of Science and Technology of China, Hefei 23002, China and Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC 27599, Department of EECS, Case Western Reserve University, OH 44106, USA Department of Mathematics, University of Science and Technology of China, Hefei 23002, China and Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC 27599, Department of EECS, Case Western Reserve University, OH 44106, USA Department of Mathematics, University of Science and Technology of China, Hefei 23002, China and Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC 27599, Department of EECS, Case Western Reserve University, OH 44106, USA Department of Mathematics, University of Science and Technology of China, Hefei 23002, China and Department of Computer Science, University of California, Los Angeles, CA 90095, USA.', 'China|China|China|China|China', 25, 'Algorithms|Chromosome Mapping|Cluster Analysis|Gene Expression|Gene Regulatory Networks|Genetic Markers|Linear Models|Polymorphism, Single Nucleotide|Protein Interaction Mapping|Quantitative Trait Loci|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btu293', '24931977', 'As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks, (ii) the incompleteness of the networks and the noise in them are not considered, (iii) other available information, such as location of genetic markers and pathway information are not integrated.'),
(78476, 2014, 'bio', 'Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics', 'Penzlin, Anke|Lindner, Martin S|Doellinger, Joerg|Dabrowski, Piotr Wojtek|Nitsche, Andreas|Renard, Bernhard Y', 'Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, GermanyResearch Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, GermanyResearch Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Centre for Biological Threats and Special Pathogens 1 (ZBS 1), Centre for Biological Threats and Special Pathogens 6 (ZBS 6) and Central Administration 4 (IT), Robert Koch Institute, 13353 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 19, 'Algorithms|Bacterial Proteins|Cowpox virus|Environmental Microbiology|Mass Spectrometry|Peptides|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btu267', '24931978', 'Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions.'),
(78477, 2014, 'bio', 'Metabolite identification through multiple kernel learning on fragmentation trees', 'Shen, Huibin|Dührkop, Kai|Böcker, Sebastian|Rousu, Juho', 'Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, GermanyDepartment of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.|Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.|Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.|Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, GermanyDepartment of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.', 'Germany|Germany|Germany|Germany', 57, 'Algorithms|Artificial Intelligence|Metabolomics|Molecular Structure|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btu275', '24931979', 'Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints, predicted fingerprints, in turn, can be used to score candidate molecular structures.'),
(78478, 2014, 'bio', 'Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach', 'Kotera, Masaaki|Tabei, Yasuo|Yamanishi, Yoshihiro|Muto, Ai|Moriya, Yuki|Tokimatsu, Toshiaki|Goto, Susumu', 'Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanGraduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan, Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan and Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Metabolic Networks and Pathways|Metabolome|Metabolomics|Support Vector Machine', 'Evaluation Studies', '10.1093/bioinformatics/btu265', '24931980', 'Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale.'),
(78479, 2014, 'bio', 'MIRA: mutual information-based reporter algorithm for metabolic networks', 'Cicek, A Ercument|Roeder, Kathryn|Ozsoyoglu, Gultekin', 'Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.', '-|-|-', 3, 'Algorithms|Child|Child Development Disorders, Pervasive|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation|Humans|Metabolic Networks and Pathways|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btu290', '24931981', 'Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRAs results are biologically sound, empirically significant and more reliable than RA.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78480, 2014, 'bio', 'GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database', 'Leslie, Richard|ODonnell, Christopher J|Johnson, Andrew D', 'Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USACardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA.|Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USACardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA.|Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA.', 'United States|United States|United States', 0, 'Databases, Genetic|Genes|Genome-Wide Association Study|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btu273', '24931982', 'We created a deeply extracted and annotated database of genome-wide association studies (GWAS) results. GRASP v1.0 contains >6.2 million SNP-phenotype association from among 1390 GWAS studies. We re-annotated GWAS results with 16 annotation sources including some rarely compared to GWAS results (e.g. RNAediting sites, lincRNAs, PTMs).'),
(78481, 2014, 'bio', 'EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traits', 'Arkin, Yaara|Rahmani, Elior|Kleber, Marcus E|Laaksonen, Reijo|März, Winfried|Halperin, Eran', 'The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USA.|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USA.|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USA.|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USAThe Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USA.|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USAThe Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USAThe Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-803|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USAThe Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-8036, Austria, Synlab Academy, Synlab Services GmbH, Mannheim D-68165, Germany, Department of Molecular Microbiology and Biotechnology, George Wise Faculty of Life Science, Tel-Aviv University, Tel-Aviv 69978, Israel and International Computer Science Institute, Berkeley, CA 94704, USAThe Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, V Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim D-68167, Germany, Zora Biosciences Oy, Espoo 02150, Finland, Medical School, University of Tampere, Tampere 33104, Finland, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz A-803', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Algorithms|Case-Control Studies|Epistasis, Genetic|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btu261', '24931983', 'Gene-gene interactions are of potential biological and medical interest, as they can shed light on both the inheritance mechanism of a trait and on the underlying biological mechanisms. Evidence of epistatic interactions has been reported in both humans and other organisms. Unlike single-locus genome-wide association studies (GWAS), which proved efficient in detecting numerous genetic loci related with various traits, interaction-based GWAS have so far produced very few reproducible discoveries. Such studies introduce a great computational and statistical burden by necessitating a large number of hypotheses to be tested including all pairs of single nucleotide polymorphisms (SNPs). Thus, many software tools have been developed for interaction-based case-control studies, some leading to reliable discoveries. For quantitative data, on the other hand, only a handful of tools exist, and the computational burden is still substantial.'),
(78482, 2014, 'bio', 'Detecting independent and recurrent copy number aberrations using interval graphs', 'Wu, Hsin-Ta|Hajirasouliha, Iman|Raphael, Benjamin J', 'Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA.', 'United States|United States|United States', 21, 'Algorithms|DNA Copy Number Variations|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btu276', '24931984', 'Somatic copy number aberrations SCNAS: are frequent in cancer genomes, but many of these are random, passenger events. A common strategy to distinguish functional aberrations from passengers is to identify those aberrations that are recurrent across multiple samples. However, the extensive variability in the length and position of SCNA: s makes the problem of identifying recurrent aberrations notoriously difficult.'),
(78483, 2014, 'bio', 'Privacy preserving protocol for detecting genetic relatives using rare variants', 'Hormozdiari, Farhad|Joo, Jong Wha J|Wadia, Akshay|Guan, Feng|Ostrosky, Rafail|Sahai, Amit|Eskin, Eleazar', 'Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.|Department of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USADepartment of Computer Science, Bioinformatics IDP, Department of Mathematics and Department of Human Genetics, University of California, LA 90095, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Genetic Privacy|Genetic Variation|Genome, Human|Genomics|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Pedigree', 'Journal Article', '10.1093/bioinformatics/btu294', '24931985', 'High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.'),
(78484, 2014, 'bio', 'Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing', 'Rampášek, Ladislav|Arbabi, Aryan|Brudno, Michael', 'Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.|Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.|Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, CanadaDepartment of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, CanadaDepartment of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.', 'Canada|Canada|Canada', 11, 'Algorithms|Computer Simulation|DNA|DNA Copy Number Variations|Female|Fetus|Genetic Testing|Genome, Human|Genotype|Humans|Male|Models, Statistical|Polymorphism, Single Nucleotide|Pregnancy|Prenatal Diagnosis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu292', '24931986', 'The past several years have seen the development of methodologies to identify genomic variation within a fetus through the non-invasive sequencing of maternal blood plasma. These methods are based on the observation that maternal plasma contains a fraction of DNA (typically 5-15%) originating from the fetus, and such methodologies have already been used for the detection of whole-chromosome events (aneuploidies), and to a more limited extent for smaller (typically several megabases long) copy number variants (CNVs).'),
(78485, 2014, 'bio', 'New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence', 'Cao, Mengfei|Pietras, Christopher M|Feng, Xian|Doroschak, Kathryn J|Schaffner, Thomas|Park, Jisoo|Zhang, Hao|Cowen, Lenore J|Hescott, Benjamin J', 'Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA and Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 51, 'Algorithms|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btu263', '24931987', 'It has long been hypothesized that incorporating models of network noise as well as edge directions and known pathway information into the representation of protein-protein interaction (PPI) networks might improve their utility for functional inference. However, a simple way to do this has not been obvious. We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph.'),
(78486, 2014, 'bio', 'DrugComboRanker: drug combination discovery based on target network analysis', 'Huang, Lei|Li, Fuhai|Sheng, Jianting|Xia, Xiaofeng|Ma, Jinwen|Zhan, Ming|Wong, Stephen T C', 'Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USADepartment of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.|Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.', 'China|China|China|China|China|China|China', 0, 'Adenocarcinoma|Adenocarcinoma of Lung|Algorithms|Antineoplastic Combined Chemotherapy Protocols|Bayes Theorem|Drug Discovery|Genomics|Humans|Lung Neoplasms|Neoplasms|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btu278', '24931988', 'Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities.'),
(78487, 2014, 'bio', 'Scale-space measures for graph topology link protein network architecture to function', 'Hulsman, Marc|Dimitrakopoulos, Christos|de Ridder, Jeroen', 'Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628CD Delft, The Netherlands.|Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628CD Delft, The Netherlands.|Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628CD Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 10, 'Algorithms|Artificial Intelligence|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btu283', '24931989', 'The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale).'),
(78488, 2014, 'bio', 'Gene network inference by probabilistic scoring of relationships from a factorized model of interactions', 'Zitnik, Marinka|Zupan, Blaž', 'Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USAFaculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.', 'Slovenia|Slovenia', 3, 'Algorithms|Endoplasmic Reticulum-Associated Degradation|Epistasis, Genetic|Gene Regulatory Networks|Glycosylation|Models, Statistical|Mutation|Phenotype|Phosphatidylserines', 'Journal Article', '10.1093/bioinformatics/btu287', '24931990', 'Epistasis analysis is an essential tool of classical genetics for inferring the order of function of genes in a common pathway. Typically, it considers single and double mutant phenotypes and for a pair of genes observes whether a change in the first gene masks the effects of the mutation in the second gene. Despite the recent emergence of biotechnology techniques that can provide gene interaction data on a large, possibly genomic scale, few methods are available for quantitative epistasis analysis and epistasis-based network reconstruction.'),
(78489, 2014, 'bio', 'An efficient parallel algorithm for accelerating computational protein design', 'Zhou, Yichao|Xu, Wei|Donald, Bruce R|Zeng, Jianyang', 'Institute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USA.|Institute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USA.|Institute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USAInstitute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USA.|Institute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USA.', 'China|China|China|China', 14, 'Algorithms|Amino Acid Sequence|Computational Biology|Protein Conformation|Protein Engineering', 'Journal Article', '10.1093/bioinformatics/btu264', '24931991', 'Structure-based computational protein design (SCPR) is an important topic in protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably finds the global minimum energy conformation (GMEC) solution.'),
(78490, 2014, 'bio', 'A statistical approach for inferring the 3D structure of the genome', 'Varoquaux, Nelle|Ay, Ferhat|Noble, William Stafford|Vert, Jean-Philippe', 'Centre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USACentre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USACentre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.|Centre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.|Centre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USACentre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.|Centre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USACentre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USACentre for Computational Biology, Mines ParisTech, Fontainebleau F-77300, Institut Curie, U900, INSERM, Paris, F-75248, France, Department of Genome Sciences and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.', 'France|France|France|France', 0, 'Algorithms|Animals|Chromosomes|DNA|Genome|Genomics|Mice|Models, Molecular|Models, Statistical|Nucleic Acid Conformation|Poisson Distribution', 'Journal Article', '10.1093/bioinformatics/btu268', '24931992', 'Recent technological advances allow the measurement, in a single Hi-C experiment, of the frequencies of physical contacts among pairs of genomic loci at a genome-wide scale. The next challenge is to infer, from the resulting DNA-DNA contact maps, accurate 3D models of how chromosomes fold and fit into the nucleus. Many existing inference methods rely on multidimensional scaling (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts. These approaches, however, often optimize a heuristic objective function and require strong assumptions about the biophysics of DNA to transform interaction frequencies to spatial distance, and thereby may lead to incorrect structure reconstruction.'),
(78491, 2014, 'bio', 'Tertiary structure-based prediction of conformational B-cell epitopes through B factors', 'Ren, Jing|Liu, Qian|Ellis, John|Li, Jinyan', 'Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.|Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.|Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.|Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Artificial Intelligence|Crystallography, X-Ray|Epitopes, B-Lymphocyte|HIV Envelope Protein gp120|Hemagglutinin Glycoproteins, Influenza Virus|Models, Molecular|Protein Structure, Quaternary|Protein Structure, Tertiary', 'Journal Article', '10.1093/bioinformatics/btu281', '24931993', 'B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods.'),
(78492, 2014, 'bio', 'BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles', 'Videm, Pavankumar|Rose, Dominic|Costa, Fabrizio|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, DenmarkBioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, DenmarkBioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, DenmarkBioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, DenmarkBioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany and Centre for Non-coding RNA in Technology and Health, Bagsvaerd, Denmark.', 'Germany|Germany|Germany|Germany', 12, 'Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|RNA, Small Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btu270', '24931994', 'Non-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. However the vast majority of ncRNAs still have no functional annotation. One prominent approach for putative function assignment is clustering of transcripts according to sequence and secondary structure. However sequence information is changed by post-transcriptional modifications, and secondary structure is only a proxy for the true 3D conformation of the RNA polymer. A different type of information that does not suffer from these issues and that can be used for the detection of RNA classes, is the pattern of processing and its traces in small RNA-seq reads data. Here we introduce BlockClust, an efficient approach to detect transcripts with similar processing patterns. We propose a novel way to encode expression profiles in compact discrete structures, which can then be processed using fast graph-kernel techniques. We perform both unsupervised clustering and develop family specific discriminative models, finally we show how the proposed approach is scalable, accurate and robust across different organisms, tissues and cell lines.'),
(78493, 2014, 'bio', 'RNA-Skim: a rapid method for RNA-Seq quantification at transcript level', 'Zhang, Zhaojun|Wang, Wei', 'Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and Department of Computer Science, University of California, Los Angeles, CA, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and Department of Computer Science, University of California, Los Angeles, CA, USA.', 'United States|United States', 21, 'Algorithms|Animals|Female|Gene Expression Profiling|Genome|High-Throughput Nucleotide Sequencing|Male|Mice|Mice, Inbred C57BL|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btu288', '24931995', 'RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base pair-level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and it is a critical component in RNA-Seq differential expression analysis. Most existing RNA-Seq quantification tools require the alignments of fragments to either a genome or a transcriptome, entailing a time-consuming and intricate alignment step. To improve the performance of RNA-Seq quantification, an alignment-free method, Sailfish, has been recently proposed to quantify transcript abundances using all k-mers in the transcriptome, demonstrating the feasibility of designing an efficient alignment-free method for transcriptome quantification. Even though Sailfish is substantially faster than alternative alignment-dependent methods such as Cufflinks, using all k-mers in the transcriptome quantification impedes the scalability of the method.'),
(78494, 2014, 'bio', 'ExSPAnder: a universal repeat resolver for DNA fragment assembly', 'Prjibelski, Andrey D|Vasilinetc, Irina|Bankevich, Anton|Gurevich, Alexey|Krivosheeva, Tatiana|Nurk, Sergey|Pham, Son|Korobeynikov, Anton|Lapidus, Alla|Pevzner, Pavel A', 'Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USAAlgorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USAAlgorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.|Algorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USAAlgorithmic Biology Lab, St. Petersburg Academic University, St. Petersburg, Russia, Department of Mathematics and Mechanics, Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia and Department of Computer Science and Engineering, University of California, San Diego, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Actinomycetales|Algorithms|DNA|Gene Library|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btu266', '24931996', 'Next-generation sequencing (NGS) technologies have raised a challenging de novo genome assembly problem that is further amplified in recently emerged single-cell sequencing projects. While various NGS assemblers can use information from several libraries of read-pairs, most of them were originally developed for a single library and do not fully benefit from multiple libraries. Moreover, most assemblers assume uniform read coverage, condition that does not hold for single-cell projects where utilization of read-pairs is even more challenging. We have developed an exSPAnder algorithm that accurately resolves repeats in the case of both single and multiple libraries of read-pairs in both standard and single-cell assembly projects.'),
(78495, 2014, 'bio', 'Ragout-a reference-assisted assembly tool for bacterial genomes', 'Kolmogorov, Mikhail|Raney, Brian|Paten, Benedict|Pham, Son', 'St. Petersburg University of the Russian Academy of Sciences, Bioinformatics Institute, St. Petersburg, Russia, UCSC, 1156 High Street, Santa Cruz, CA and Department of Computer Science and Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, USASt. Petersburg University of the Russian Academy of Sciences, Bioinformatics Institute, St. Petersburg, Russia, UCSC, 1156 High Street, Santa Cruz, CA and Department of Computer Science and Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, USA.|St. Petersburg University of the Russian Academy of Sciences, Bioinformatics Institute, St. Petersburg, Russia, UCSC, 1156 High Street, Santa Cruz, CA and Department of Computer Science and Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, USA.|St. Petersburg University of the Russian Academy of Sciences, Bioinformatics Institute, St. Petersburg, Russia, UCSC, 1156 High Street, Santa Cruz, CA and Department of Computer Science and Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, USA.|St. Petersburg University of the Russian Academy of Sciences, Bioinformatics Institute, St. Petersburg, Russia, UCSC, 1156 High Street, Santa Cruz, CA and Department of Computer Science and Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, USA.', 'United States|United States|United States|United States', 74, 'Algorithms|Chromosomes, Bacterial|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Phylogeny|Reference Standards|Sequence Analysis, DNA|Software|Staphylococcus aureus|Synteny', 'Journal Article', '10.1093/bioinformatics/btu280', '24931998', 'Bacterial genomes are simpler than mammalian ones, and yet assembling the former from the data currently generated by high-throughput short-read sequencing machines still results in hundreds of contigs. To improve assembly quality, recent studies have utilized longer Pacific Biosciences (PacBio) reads or jumping libraries to connect contigs into larger scaffolds or help assemblers resolve ambiguities in repetitive regions of the genome. However, their popularity in contemporary genomic research is still limited by high cost and error rates. In this work, we explore the possibility of improving assemblies by using complete genomes from closely related species/strains. We present Ragout, a genome rearrangement approach, to address this problem. In contrast with most reference-guided algorithms, where only one reference genome is used, Ragout uses multiple references along with the evolutionary relationship among these references in order to determine the correct order of the contigs. Additionally, Ragout uses the assembly graph and multi-scale synteny blocks to reduce assembly gaps caused by small contigs from the input assembly. In simulations as well as real datasets, we believe that for common bacterial species, where many complete genome sequences from related strains have been available, the current high-throughput short-read sequencing paradigm is sufficient to obtain a single high-quality scaffold for each chromosome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78496, 2014, 'bio', 'Stochastic EM-based TFBS motif discovery with MITSU', 'Kilpatrick, Alastair M|Ward, Bruce|Aitken, Stuart', 'School of Informatics, University of Edinburgh, Informatics Forum, Edinburgh EH8 9AB, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR and MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.|School of Informatics, University of Edinburgh, Informatics Forum, Edinburgh EH8 9AB, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR and MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.|School of Informatics, University of Edinburgh, Informatics Forum, Edinburgh EH8 9AB, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR and MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.', 'United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Binding Sites|Escherichia coli|Humans|Models, Statistical|Nucleotide Motifs|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Stochastic Processes|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btu286', '24931999', 'The Expectation-Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm, however, the application of sEM to motif discovery has not been fully explored.'),
(78497, 2014, 'bio', 'AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references', 'Bao, Ergude|Jiang, Tao|Girke, Thomas', 'Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States', 0, 'Algorithms|Arabidopsis|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Reference Standards|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu291', '24932000', 'De novo assemblies of genomes remain one of the most challenging applications in next-generation sequencing. Usually, their results are incomplete and fragmented into hundreds of contigs. Repeats in genomes and sequencing errors are the main reasons for these complications. With the rapidly growing number of sequenced genomes, it is now feasible to improve assemblies by guiding them with genomes from related species.'),
(78498, 2014, 'bio', 'Accurate viral population assembly from ultra-deep sequencing data', 'Mangul, Serghei|Wu, Nicholas C|Mancuso, Nicholas|Zelikovsky, Alex|Sun, Ren|Eskin, Eleazar', 'Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.|Computer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USAComputer Science Department, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA, Department of Computer Science, Georgia State University, Atlanta, GA, 30303 and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Algorithms|Genome, Viral|Genomics|HIV|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu295', '24932001', 'Next-generation sequencing technologies sequence viruses with ultra-deep coverage, thus promising to revolutionize our understanding of the underlying diversity of viral populations. While the sequencing coverage is high enough that even rare viral variants are sequenced, the presence of sequencing errors makes it difficult to distinguish between rare variants and sequencing errors.'),
(78499, 2014, 'bio', 'Inferring gene ontologies from pairwise similarity data', 'Kramer, Michael|Dutkowski, Janusz|Yu, Michael|Bafna, Vineet|Ideker, Trey', 'Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Medicine and Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|Genomics|Semantics|Yeasts', 'Journal Article', '10.1093/bioinformatics/btu282', '24932003', 'While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene-gene pairwise similarities from -omics data, infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact, and respect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge-none has been evaluated for GO inference.'),
(78500, 2014, 'bio', 'Automated detection and tracking of many cells by using 4D live-cell imaging data', 'Tokunaga, Terumasa|Hirose, Osamu|Kawaguchi, Shotaro|Toyoshima, Yu|Teramoto, Takayuki|Ikebata, Hisaki|Kuge, Sayuri|Ishihara, Takeshi|Iino, Yuichi|Yoshida, Ryo', 'The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.', '-|-|-|-|-|-|-|-|-|-', 0, 'Algorithms|Animals|Caenorhabditis elegans|Cell Tracking|Imaging, Three-Dimensional|Microscopy, Confocal|Microscopy, Fluorescence|Neurons|Time-Lapse Imaging', 'Journal Article', '10.1093/bioinformatics/btu271', '24932004', 'Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses, detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves.'),
(78501, 2014, 'bio', 'Using association rule mining to determine promising secondary phenotyping hypotheses', 'Oellrich, Anika|Jacobsen, Julius|Papatheodorou, Irene|Sanger Mouse Genetics Project|Smedley, Damian', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB1 10SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB1 10SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB1 10SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB1 10SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB1 10SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Animals|Ataxia|Data Mining|Disease|Genes|Humans|Mice|Mitochondrial Diseases|Muscle Weakness|Phenotype|Ubiquinone', 'Journal Article', '10.1093/bioinformatics/btu260', '24932005', 'Large-scale phenotyping projects such as the Sanger Mouse Genetics project are ongoing efforts to help identify the influences of genes and their modification on phenotypes. Gene-phenotype relations are crucial to the improvement of our understanding of human heritable diseases as well as the development of drugs. However, given that there are xe2x88xbc: 20 000 genes in higher vertebrate genomes and the experimental verification of gene-phenotype relations requires a lot of resources, methods are needed that determine good candidates for testing.'),
(78502, 2014, 'bio', 'Inductive matrix completion for predicting gene-disease associations', 'Natarajan, Nagarajan|Dhillon, Inderjit S', 'Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.', 'United States|United States', 0, 'Algorithms|Databases, Genetic|Disease|Gene Expression Profiling|Genes|Humans|Principal Component Analysis', 'Journal Article', '10.1093/bioinformatics/btu269', '24932006', 'Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations, it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive, it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive.'),
(78503, 2014, 'bio', 'Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks', 'Zarringhalam, Kourosh|Enayetallah, Ahmed|Reddy, Padmalatha|Ziemek, Daniel', 'Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, GermanyComputational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, Germany.|Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, Germany.|Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, Germany.|Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, Germany.', 'Germany|Germany|Germany|Germany', 8, 'Algorithms|Antibodies, Monoclonal|Bayes Theorem|Colitis, Ulcerative|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|Graft Rejection|Humans|Infliximab|Kidney Transplantation|Phenotype|Regression Analysis|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btu272', '24932007', 'Understanding and predicting an individuals response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.'),
(78504, 2014, 'bio', 'A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data', 'Hajirasouliha, Iman|Mahmoody, Ahmad|Raphael, Benjamin J', 'Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USADepartment of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USADepartment of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USA.', 'United States|United States|United States', 65, 'Algorithms|Cluster Analysis|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Leukemia, Myeloid, Acute|Mutation|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu284', '24932008', 'High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells.'),
(78505, 2014, 'bio', 'Pareto-optimal phylogenetic tree reconciliation', 'Libeskind-Hadas, Ran|Wu, Yi-Chieh|Bansal, Mukul S|Kellis, Manolis', 'Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 and Broad Institute, Cambridge, MA 02142, USA.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 and Broad Institute, Cambridge, MA 02142, USA.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 and Broad Institute, Cambridge, MA 02142, USA.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 and Broad Institute, Cambridge, MA 02142, USADepartment of Computer Science, Harvey Mudd College, Claremont, CA 91711, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 and Broad Institute, Cambridge, MA 02142, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Genomics|Multigene Family|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btu289', '24932009', 'Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem.'),
(78506, 2014, 'bio', 'Evaluating synteny for improved comparative studies', 'Ghiurcuta, Cristina G|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, EPFL-IC-LCBB INJ 230, Station 14, CH-1015 Lausanne, Switzerland.|Laboratory for Computational Biology and Bioinformatics, EPFL-IC-LCBB INJ 230, Station 14, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 0, 'Genome, Fungal|Genomics|Sequence Alignment|Software|Synteny|Yeasts', 'Journal Article', '10.1093/bioinformatics/btu259', '24932010', 'Comparative genomics aims to understand the structure and function of genomes by translating knowledge gained about some genomes to the object of study. Early approaches used pairwise comparisons, but today researchers are attempting to leverage the larger potential of multi-way comparisons. Comparative genomics relies on the structuring of genomes into syntenic blocks: blocks of sequence that exhibit conserved features across the genomes. Syntenic blocs are required for complex computations to scale to the billions of nucleotides present in many genomes, they enable comparisons across broad ranges of genomes because they filter out much of the individual variability, they highlight candidate regions for in-depth studies, and they facilitate whole-genome comparisons through visualization tools. However, the concept of syntenic block remains loosely defined. Tools for the identification of syntenic blocks yield quite different results, thereby preventing a systematic assessment of the next steps in an analysis. Current tools do not include measurable quality objectives and thus cannot be benchmarked against themselves. Comparisons among tools have also been neglected-what few results are given use superficial measures unrelated to quality or consistency.'),
(78507, 2014, 'bio', 'Large scale analysis of signal reachability', 'Todor, Andrei|Gabr, Haitham|Dobra, Alin|Kahveci, Tamer', 'CISE Department, University of Florida, Gainesville, FL 32611, USA.|CISE Department, University of Florida, Gainesville, FL 32611, USA.|CISE Department, University of Florida, Gainesville, FL 32611, USA.|CISE Department, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|Leukemia|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btu262', '24932011', 'Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge.'),
(78508, 2014, 'bio', 'DAFGA: diversity analysis of functional gene amplicons', 'Kim, Yongkyu|Liesack, Werner', 'Department of Biogeochemistry, Max Planck Institute for Terrestrial Microbiology. 35043 Marburg, Germany.|Department of Biogeochemistry, Max Planck Institute for Terrestrial Microbiology. 35043 Marburg, Germany.', 'Germany|Germany', 3, 'Cluster Analysis|Computational Biology|Evolution, Molecular|Genes, rRNA|Phylogeny|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1093/bioinformatics/btu394', '24939150', 'Diversity analysis of functional marker genes provides physiological insights into microbial guilds that perform an ecologically relevant process. However, it is challenging to group functional gene sequences to valid taxonomic units, primarily because of differences in the evolutionary rates of individual genes and possible horizontal gene transfer events. We developed a python script package named DAFGA, which estimates the evolutionary rate of a particular functional gene in a standardized manner by relating its sequence divergence to that of the 16S rRNA gene. As a result, DAFGA provides gene-specific parameter sets for operational taxonomic unit clustering and taxonomic assignment at desired rank, and it can be implemented into the diversity measurements offered by QIIME.'),
(78509, 2014, 'bio', 'Literome: PubMed-scale genomic knowledge base in the cloud', 'Poon, Hoifung|Quirk, Chris|DeZiel, Charlie|Heckerman, David', 'Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Automation|Computational Biology|Genetic Association Studies|Genome|Genomics|Genotype|Humans|Internet|Knowledge Bases|Phenotype|Polymorphism, Single Nucleotide|PubMed|Software', 'Journal Article', '10.1093/bioinformatics/btu383', '24939151', 'Advances in sequencing technology have led to an exponential growth of genomics data, yet it remains a formidable challenge to interpret such data for identifying disease genes and drug targets. There has been increasing interest in adopting a systems approach that incorporates prior knowledge such as gene networks and genotype-phenotype associations. The majority of such knowledge resides in text such as journal publications, which has been undergoing its own exponential growth. It has thus become a significant bottleneck to identify relevant knowledge for genomic interpretation as well as to keep up with new genomics findings.'),
(78510, 2014, 'bio', 'Omega: an overlap-graph de novo assembler for metagenomics', 'Haider, Bahlul|Ahn, Tae-Hyuk|Bushnell, Brian|Chai, Juanjuan|Copeland, Alex|Pan, Chongle', 'Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 and U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA, 94598 USA.', 'United States|United States|United States|United States|United States|United States', 53, 'Algorithms|Computational Biology|Computers|DNA, Bacterial|Genome, Bacterial|Internet|Metagenome|Metagenomics|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu395', '24947750', 'Metagenomic sequencing allows reconstruction of microbial genomes directly from environmental samples. Omega (overlap-graph metagenome assembler) was developed for assembling and scaffolding Illumina sequencing data of microbial communities.'),
(78511, 2014, 'bio', 'Correcting for link loss in causal network inference caused by regulator interference', 'Wang, Ying|Penfold, Christopher A|Hodgson, David A|Gifford, Miriam L|Burroughs, Nigel J', 'Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Arabidopsis|Circadian Rhythm|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Models, Statistical|Probability|Programming Languages|Streptomyces coelicolor', 'Journal Article', '10.1093/bioinformatics/btu388', '24947751', 'There are a number of algorithms to infer causal regulatory networks from time series (gene expression) data. Here we analyse the phenomena of regulator interference, where regulators with similar dynamics mutually suppress both the probability of regulating a target and the associated link strength, for instance, interference between two identical strong regulators reduces link probabilities by xe2x88xbc50%.'),
(78512, 2014, 'bio', 'PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures', 'Zheng, Yinan|Fei, Zhe|Zhang, Wei|Starren, Justin B|Liu, Lei|Baccarelli, Andrea A|Li, Yi|Hou, Lifang', 'Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.|Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, Institute of Human Genetics, University of Illinois at Chicago, Chicago, IL 60612, Division of Health and Biomedical Informatics, Departments of Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115 and The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 5, 'Air Pollutants|Algorithms|Biomarkers|Computational Biology|Gene Expression Profiling|Humans|MicroRNAs|Phenotype|Regression Analysis|Respiratory Function Tests|Software', 'Journal Article', '10.1093/bioinformatics/btu396', '24947752', 'MicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Owning to the dynamic nature of miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNA expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used univariate association testing or the site-by-site (SBS) testing may underutilize the longitudinal feature of the data, leading to underpowered results and less biologically meaningful results.'),
(78513, 2014, 'bio', 'BEETL-fastq: a searchable compressed archive for DNA reads', 'Janin, Lilian|Schulz-Trieglaff, Ole|Cox, Anthony J', 'Computational Biology Group, Illumina Cambridge Ltd., Little Chesterford, Essex CB10 1XL, UK.|Computational Biology Group, Illumina Cambridge Ltd., Little Chesterford, Essex CB10 1XL, UK.|Computational Biology Group, Illumina Cambridge Ltd., Little Chesterford, Essex CB10 1XL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Computer Simulation|DNA|Data Compression|Genome|Genome, Human|Genotype|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu387', '24950811', 'FASTQ is a standard file format for DNA sequencing data, which stores both nucleotides and quality scores. A typical sequencing study can easily generate hundreds of gigabytes of FASTQ files, while public archives such as ENA and NCBI and large international collaborations such as the Cancer Genome Atlas can accumulate many terabytes of data in this format. Compression tools such as gzip are often used to reduce the storage burden but have the disadvantage that the data must be decompressed before they can be used. Here, we present BEETL-fastq, a tool that not only compresses FASTQ-formatted DNA reads more compactly than gzip but also permits rapid search for k-mer queries within the archived sequences. Importantly, the full FASTQ record of each matching read or read pair is returned, allowing the search results to be piped directly to any of the many standard tools that accept FASTQ data as input.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78514, 2014, 'bio', 'TRES predicts transcription control in embryonic stem cells', 'Pooley, Christopher|Ruau, David|Lombard, Patrick|Gottgens, Berthold|Joshi, Anagha', 'The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 8GR, UK and Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, Cambridge University, Cambridge, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 8GR, UK and Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, Cambridge University, Cambridge, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 8GR, UK and Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, Cambridge University, Cambridge, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 8GR, UK and Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, Cambridge University, Cambridge, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 8GR, UK and Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, Cambridge University, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Animals|Chromatin Immunoprecipitation|Computational Biology|Embryonic Stem Cells|Gene Expression Regulation|Humans|Internet|Mice|Oligonucleotide Array Sequence Analysis|Sequence Analysis|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btu399', '24958811', 'Unraveling transcriptional circuits controlling embryonic stem cell maintenance and fate has great potential for improving our understanding of normal development as well as disease. To facilitate this, we have developed a novel web tool called TRES that predicts the likely upstream regulators for a given gene list. This is achieved by integrating transcription factor (TF) binding events from 187 ChIP-sequencing and ChIP-on-chip datasets in murine and human embryonic stem (ES) cells with over 1000 mammalian TF sequence motifs. Using 114 TF perturbation gene sets, as well as 115 co-expression clusters in ES cells, we validate the utility of this approach.'),
(78515, 2014, 'bio', 'A multiobjective method for robust identification of bacterial small non-coding RNAs', 'Arnedo, Javier|Romero-Zaliz, Rocío|Zwir, Igor|Del Val, Coral', 'Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA.|Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA.|Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA.|Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA.', 'Spain|Spain|Spain|Spain', 7, 'Algorithms|Artificial Intelligence|Genomics|RNA, Bacterial|RNA, Small Untranslated|Salmonella typhimurium|Sinorhizobium meliloti', 'Journal Article', '10.1093/bioinformatics/btu398', '24958812', 'Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species.'),
(78516, 2014, 'bio', 'CNV-guided multi-read allocation for ChIP-seq', 'Zhang, Qi|Kelexc5x9f, Sxc3xbcndxc3xbcz', 'Department of Biostatistics and Medical Informatics, 425 Henry Mall and Department of Statistics, 1300 University Avenue, Madison, WI 53706, USA.|Department of Biostatistics and Medical Informatics, 425 Henry Mall and Department of Statistics, 1300 University Avenue, Madison, WI 53706, USA Department of Biostatistics and Medical Informatics, 425 Henry Mall and Department of Statistics, 1300 University Avenue, Madison, WI 53706, USA.', 'United States|United States', 4, 'Algorithms|Base Sequence|Chromatin Immunoprecipitation|DNA Copy Number Variations|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btu402', '24966364', 'In chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and other short-read sequencing experiments, a considerable fraction of the short reads align to multiple locations on the reference genome (multi-reads). Inferring the origin of multi-reads is critical for accurately mapping reads to repetitive regions. Current state-of-the-art multi-read allocation algorithms rely on the read counts in the local neighborhood of the alignment locations and ignore the variation in the copy numbers of these regions. Copy-number variation (CNV) can directly affect the read densities and, therefore, bias allocation of multi-reads.'),
(78517, 2014, 'bio', 'H3M2: detection of runs of homozygosity from whole-exome sequencing data', 'Magi, Alberto|Tattini, Lorenzo|Palombo, Flavia|Benelli, Matteo|Gialluisi, Alessandro|Giusti, Betti|Abbate, Rosanna|Seri, Marco|Gensini, Gian Franco|Romeo, Giovanni|Pippucci, Tommaso', 'Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.|Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands Department of Experimental and Clinical Medicine, University of Florence, Florence 50019, Medical Genetics Unit, Polyclinic SantOrsola-Malpighi, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Diagnostic Genetic Unit, Careggi Hospital, Florence 50019, Italy and Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 EN, The Netherlands.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Chromosomes, Human|Exome|Genomics|Genotype|Homozygote|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu401', '24966365', 'Runs of homozygosity (ROH) are sizable chromosomal stretches of homozygous genotypes, ranging in length from tens of kilobases to megabases. ROHs can be relevant for population and medical genetics, playing a role in predisposition to both rare and common disorders. ROHs are commonly detected by single nucleotide polymorphism (SNP) microarrays, but attempts have been made to use whole-exome sequencing (WES) data. Currently available methods developed for the analysis of uniformly spaced SNP-array maps do not fit easily to the analysis of the sparse and non-uniform distribution of the WES target design.'),
(78518, 2014, 'bio', 'TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs', 'Savojardo, Castrense|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.|Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.|Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.|Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Amino Acid Motifs|Artificial Intelligence|Binding Sites|Computational Biology|Internet|Mitochondrial Proteins|Protein Sorting Signals|Proteolysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btu411', '24974200', 'Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.'),
(78519, 2014, 'bio', 'The Amordad database engine for metagenomics', 'Behnam, Ehsan|Smith, Andrew D', 'Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.', 'United States|United States', 0, 'Databases, Genetic|Information Storage and Retrieval|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btu405', '24974201', 'Several technical challenges in metagenomic data analysis, including assembling metagenomic sequence data or identifying operational taxonomic units, are both significant and well known. These forms of analysis are increasingly cited as conceptually flawed, given the extreme variation within traditionally defined species and rampant horizontal gene transfer. Furthermore, computational requirements of such analysis have hindered content-based organization of metagenomic data at large scale.'),
(78520, 2014, 'bio', 'Toward better understanding of artifacts in variant calling from high-coverage samples', 'Li, Heng', 'Medical Population Genetics Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.', 'United States', 0, 'Artifacts|Cell Line|Databases, Genetic|Genome, Human|Genomics|Genotype|Haploidy|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btu356', '24974202', 'Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods.'),
(78521, 2014, 'bio', 'Efficient initial volume determination from electron microscopy images of single particles', 'Vargas, Javier|Álvarez-Cabrera, Ana-Lucia|Marabini, Roberto|Carazo, Jose M|Sorzano, C O S', 'Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.|Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.|Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.|Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.|Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.', 'Spain|Spain|Spain|Spain|Spain', 35, 'Algorithms|Imaging, Three-Dimensional|Macromolecular Substances|Microscopy, Electron, Transmission|Models, Molecular|Time Factors', 'Journal Article', '10.1093/bioinformatics/btu404', '24974203', 'Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. The reconstruction process leading to the final 3D map requires an approximate initial model. Generation of an initial model is still an open and challenging problem in single-particle analysis.'),
(78522, 2014, 'bio', 'Learning protein-DNA interaction landscapes by integrating experimental data through computational models', 'Zhong, Jianling|Wasson, Todd|Hartemink, Alexander J', 'Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.|Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States', 0, 'Binding, Competitive|Computational Biology|DNA|DNA-Binding Proteins|Gene Expression Regulation|Models, Biological|Nucleosomes|Protein Binding|Thermodynamics|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btu408', '24974204', 'Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape.'),
(78523, 2014, 'bio', 'Drug repositioning by integrating target information through a heterogeneous network model', 'Wang, Wenhui|Yang, Sen|Zhang, Xiang|Li, Jing', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States|United States', 115, 'Algorithms|Computational Biology|Disease|Drug Repositioning|Humans|Models, Theoretical|Molecular Targeted Therapy|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btu403', '24974205', 'The emergence of network medicine not only offers more opportunities for better and more complete understanding of the molecular complexities of diseases, but also serves as a promising tool for identifying new drug targets and establishing new relationships among diseases that enable drug repositioning. Computational approaches for drug repositioning by integrating information from multiple sources and multiple levels have the potential to provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level.'),
(78524, 2014, 'bio', 'bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS)', 'Malaspinas, Anna-Sapfo|Tange, Ole|Moreno-Mayar, José Víctor|Rasmussen, Morten|DeGiorgio, Michael|Wang, Yong|Valdiosera, Cristina E|Politis, Gustavo|Willerslev, Eske|Nielsen, Rasmus', 'Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, Department of Biology, Pennsylvania State University, Wartik Laboratory, University Park, PA 16802, Centre for Theoretical Evolutionary Genomics, Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720-3140, Ancestry.com DNA LLC, San Francisco, CA 94107, Department of Archaeology, Environment and Community Planning Faculty of Humanities and Social Sciences, La Trobe University, Melbourne, VIC 3086, Australia, INCUAPA-CONICET, Universidad del Centro de la Provincia de Buenos Aires, 7600 Olavarría, Argentina and Facultad de Ciencias Naturales y Museo de La Plata, 1900 La Plata, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 24, 'Genetics, Population|Genome, Human|Genomics|Genotype|Humans|Phylogeography|Software', 'Journal Article', '10.1093/bioinformatics/btu410', '24974206', 'We present bammds, a practical tool that allows visualization of samples sequenced by second-generation sequencing when compared with a reference panel of individuals (usually genotypes) using a multidimensional scaling algorithm. Our tool is aimed at determining the ancestry of unknown samples-typical of ancient DNA data-particularly when only low amounts of data are available for those samples.'),
(78525, 2014, 'bio', 'COSMOS: Python library for massively parallel workflows', 'Gafni, Erik|Luquette, Lovelace J|Lancaster, Alex K|Hawkins, Jared B|Jung, Jae-Yoon|Souilmi, Yassine|Wall, Dennis P|Tonellato, Peter J', 'Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Genomics|High-Throughput Nucleotide Sequencing|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btu385', '24982428', 'Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services.'),
(78526, 2014, 'bio', 'GATB: Genome Assembly & Analysis Tool Box', 'Drezen, Erwan|Rizk, Guillaume|Chikhi, Rayan|Deltel, Charles|Lemaitre, Claire|Peterlongo, Pierre|Lavenier, Dominique', 'INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.|INRIA/IRISA/GenScale, Campus de Beaulieu, 35042 Rennes Cedex, France and Department of Computer Science and Engineering, Pennsylvania State University, PA 16802, USA.', 'France|France|France|France|France|France|France', 0, 'Algorithms|Biostatistics|Computer Graphics|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btu406', '24990603', 'Efficient and fast next-generation sequencing (NGS) algorithms are essential to analyze the terabytes of data generated by the NGS machines. A serious bottleneck can be the design of such algorithms, as they require sophisticated data structures and advanced hardware implementation.'),
(78527, 2014, 'bio', 'RAPIDR: an analysis package for non-invasive prenatal testing of aneuploidy', 'Lo, Kitty K|Boustred, Christopher|Chitty, Lyn S|Plagnol, Vincent', 'University College London Genetics Institute, University College London, UK and North East Thames Regional Genetics Service, Great Ormond Street Hospital NHS Foundation Trust, London, UK.|University College London Genetics Institute, University College London, UK and North East Thames Regional Genetics Service, Great Ormond Street Hospital NHS Foundation Trust, London, UK.|University College London Genetics Institute, University College London, UK and North East Thames Regional Genetics Service, Great Ormond Street Hospital NHS Foundation Trust, London, UK.|University College London Genetics Institute, University College London, UK and North East Thames Regional Genetics Service, Great Ormond Street Hospital NHS Foundation Trust, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Aneuploidy|Artifacts|Computational Biology|Female|Humans|Pregnancy|Prenatal Diagnosis|Sex Factors|Software|Trisomy|Turner Syndrome', 'Journal Article', '10.1093/bioinformatics/btu419', '24990604', 'Non-invasive prenatal testing (NIPT) of fetal aneuploidy using cell-free fetal DNA is becoming part of routine clinical practice. RAPIDR (Reliable Accurate Prenatal non-Invasive Diagnosis R package) is an easy-to-use open-source R package that implements several published NIPT analysis methods. The input to RAPIDR is a set of sequence alignment files in the BAM format, and the outputs are calls for aneuploidy, including trisomies 13, 18, 21 and monosomy X as well as fetal sex. RAPIDR has been extensively tested with a large sample set as part of the RAPID project in the UK. The package contains quality control steps to make it robust for use in the clinical setting.'),
(78528, 2014, 'bio', 'ARBitrator: a software pipeline for on-demand retrieval of auto-curated nifH sequences from GenBank', 'Heller, Philip|Tripp, H James|Turk-Kubo, Kendra|Zehr, Jonathan P', 'Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA, Department of Energy (DOE) Joint Genome Institute, Walnut Creek, CA 94598, USA and Department of Ocean Sciences, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA, Department of Energy (DOE) Joint Genome Institute, Walnut Creek, CA 94598, USA and Department of Ocean Sciences, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA, Department of Energy (DOE) Joint Genome Institute, Walnut Creek, CA 94598, USA and Department of Ocean Sciences, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA, Department of Energy (DOE) Joint Genome Institute, Walnut Creek, CA 94598, USA and Department of Ocean Sciences, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States', 29, 'Computational Biology|Databases, Nucleic Acid|False Positive Reactions|Information Storage and Retrieval|Oxidoreductases|Phylogeny|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btu417', '24990605', 'Studies of the biochemical functions and activities of uncultivated microorganisms in the environment require analysis of DNA sequences for phylogenetic characterization and for the development of sequence-based assays for the detection of microorganisms. The numbers of sequences for genes that are indicators of environmentally important functions such as nitrogen (N2) fixation have been rapidly growing over the past few decades. Obtaining these sequences from the National Center for Biotechnology Informations GenBank database is problematic because of annotation errors, nomenclature variation and paralogues, moreover, GenBanks structure and tools are not conducive to searching solely by function. For some genes, such as the nifH gene commonly used to assess community potential for N2 fixation, manual collection and curation are becoming intractable because of the large number of sequences in GenBank and the large number of highly similar paralogues. If analysis is to keep pace with sequence discovery, an automated retrieval and curation system is necessary.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78529, 2014, 'bio', 'Intensity drift removal in LC/MS metabolomics by common variance compensation', 'Fernández-Albert, Francesc|Llorach, Rafael|Garcia-Aloy, Mar|Ziyatdinov, Andrey|Andres-Lacueva, Cristina|Perera, Alexandre', 'Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.|Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.|Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.|Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.|Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.|Department dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 32, 'Analysis of Variance|Biostatistics|Chromatography, Liquid|Mass Spectrometry|Metabolomics|Principal Component Analysis|Quality Control', 'Journal Article', '10.1093/bioinformatics/btu423', '24990606', 'Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context.'),
(78530, 2014, 'bio', 'Fast and accurate imputation of summary statistics enhances evidence of functional enrichment', 'Pasaniuc, Bogdan|Zaitlen, Noah|Shi, Huwenbo|Bhatia, Gaurav|Gusev, Alexander|Pickrell, Joseph|Hirschhorn, Joel|Strachan, David P|Patterson, Nick|Price, Alkes L', 'Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St Georges, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biostatistics|Case-Control Studies|Cohort Studies|Genome-Wide Association Study|Genotype|Humans|Linkage Disequilibrium|Phenotype|Polymorphism, Single Nucleotide|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btu416', '24990607', 'Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available.'),
(78531, 2014, 'bio', 'PyWATER: a PyMOL plug-in to find conserved water molecules in proteins by clustering', 'Patel, Hitesh|Grüning, Björn A|Günther, Stefan|Merfort, Irmgard', 'Pharmaceutical Biology and Biotechnology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Stefan-Meier-Str. 19, D-79104 Freiburg, Germany and Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Str. 9, D-79104 Freiburg, Germany.|Pharmaceutical Biology and Biotechnology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Stefan-Meier-Str. 19, D-79104 Freiburg, Germany and Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Str. 9, D-79104 Freiburg, Germany.|Pharmaceutical Biology and Biotechnology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Stefan-Meier-Str. 19, D-79104 Freiburg, Germany and Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Str. 9, D-79104 Freiburg, Germany.|Pharmaceutical Biology and Biotechnology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Stefan-Meier-Str. 19, D-79104 Freiburg, Germany and Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Str. 9, D-79104 Freiburg, Germany.', 'Germany|Germany|Germany|Germany', 19, 'Cluster Analysis|Computational Biology|Computer Graphics|Crystallography, X-Ray|Models, Molecular|Protein Structure, Secondary|Proteins|Software|User-Computer Interface|Water', 'Journal Article', '10.1093/bioinformatics/btu424', '24990608', 'Conserved water molecules play a crucial role in protein structure, stabilization of secondary structure, protein activity, flexibility and ligand binding. Clustering of water molecules in superimposed protein structures, obtained by X-ray crystallography at high resolution, is an established method to identify consensus water molecules in all known protein structures of the same family. PyWATER is an easy-to-use PyMOL plug-in and identifies conserved water molecules in the protein structure of interest. PyWATER can be installed via the user interface of PyMOL. No programming or command-line knowledge is required for its use.'),
(78532, 2014, 'bio', 'Genome editing assessment using CRISPR Genome Analyzer (CRISPR-GA)', 'Güell, Marc|Yang, Luhan|Church, George M', 'Department of Genetics and Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA 02115 and Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138, USA.|Department of Genetics and Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA 02115 and Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138, USA Department of Genetics and Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA 02115 and Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138, USA.|Department of Genetics and Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA 02115 and Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138, USA Department of Genetics and Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA 02115 and Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138, USA.', 'United States|United States|United States', 0, 'Alleles|Clustered Regularly Interspaced Short Palindromic Repeats|Genetic Engineering|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btu427', '24990609', 'Clustered regularly interspaced short palindromic repeats (CRISPR)-based technologies have revolutionized human genome engineering and opened countless possibilities to basic science, synthetic biology and gene therapy. Albeit the enormous potential of these tools, their performance is far from perfect. It is essential to perform a posterior careful analysis of the gene editing experiment. However, there are no computational tools for genome editing assessment yet, and current experimental tools lack sensitivity and flexibility. We present a platform to assess the quality of a genome editing experiment only with three mouse clicks. The method evaluates next-generation data to quantify and characterize insertions, deletions and homologous recombination. CRISPR Genome Analyzer provides a report for the locus selected, which includes a quantification of the edited site and the analysis of the different alterations detected. The platform maps the reads, estimates and locates insertions and deletions, computes the allele replacement efficiency and provides a report integrating all the information.'),
(78533, 2014, 'bio', 'ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli', 'Agostini, Federico|Cirillo, Davide|Livi, Carmen Maria|Delli Ponti, Riccardo|Tartaglia, Gian Gaetano', 'Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.|Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain Gene Function and Evolution, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Escherichia coli|Escherichia coli Proteins|Gene Expression|Gene Expression Regulation, Bacterial|Hydrophobic and Hydrophilic Interactions|Internet|Protein Structure, Secondary|Proteomics|Solubility', 'Journal Article', '10.1093/bioinformatics/btu420', '24990610', 'Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions, (ii) identification of soluble fragments within each sequences, (iii) exhaustive single-point mutation analysis.'),
(78534, 2015, 'bio', 'Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge', 'Biehl, Michael|Sadowski, Peter|Bhanot, Gyan|Bilal, Erhan|Dayarian, Adel|Meyer, Pablo|Norel, Raquel|Rhrissorrakrai, Kahn|Zeller, Michael D|Hormoz, Sahand', 'Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.|Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 6, 'Algorithms|Animals|Bronchi|Cells, Cultured|Databases, Factual|Epithelial Cells|Gene Expression Profiling|Gene Expression Regulation|Humans|Oligonucleotide Array Sequence Analysis|Phosphoproteins|Phosphorylation|Rats|Software|Species Specificity|Systems Biology|Translational Medical Research', 'Comparative Study', '10.1093/bioinformatics/btu407', '24994890', 'Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data.'),
(78535, 2014, 'bio', 'FindPath: a Matlab solution for in silico design of synthetic metabolic pathways', 'Vieira, Gilles|Carnicer, Marc|Portais, Jean-Charles|Heux, Stéphanie', 'Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France.|Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France.|Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France.|Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France and CNRS, UMR5504, F-31400 Toulouse, France.', 'France|France|France|France', 0, 'Computational Biology|Computer Simulation|Databases, Factual|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btu422', '24994891', 'Several methods and computational tools have been developed to design novel metabolic pathways. A major challenge is evaluating the metabolic efficiency of the designed pathways in the host organism. Here we present FindPath, a unified system to predict and rank possible pathways according to their metabolic efficiency in the cellular system. This tool uses a chemical reaction database to generate possible metabolic pathways and exploits constraint-based models (CBMs) to identify the most efficient synthetic pathway to achieve the desired metabolic function in a given host microorganism. FindPath can be used with common tools for CBM manipulation and uses the standard SBML format for both input and output files.'),
(78536, 2014, 'bio', 'YASARA View - molecular graphics for all devices - from smartphones to workstations', 'Krieger, Elmar|Vriend, Gert', 'Centre for Molecular and Biomolecular Informatics, NCMLS, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands.|Centre for Molecular and Biomolecular Informatics, NCMLS, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands.', 'Netherlands|Netherlands', 0, 'Algorithms|Cell Phone|Computer Graphics|Computers|Models, Molecular|Proteins', 'Journal Article', '10.1093/bioinformatics/btu426', '24996895', 'Todays graphics processing units (GPUs) compose the scene from individual triangles. As about 320 triangles are needed to approximate a single sphere-an atom-in a convincing way, visualizing larger proteins with atomic details requires tens of millions of triangles, far too many for smooth interactive frame rates. We describe a new approach to solve this molecular graphics problem, which shares the work between GPU and multiple CPU cores, generates high-quality results with perfectly round spheres, shadows and ambient lighting and requires only OpenGL 1.0 functionality, without any pixel shader Z-buffer access (a feature which is missing in most mobile devices).'),
(78537, 2014, 'bio', 'TAPAS: tools to assist the targeted protein quantification of human alternative splice variants', 'Yang, Jae-Seong|Sabidó, Eduard|Serrano, Luis|Kiel, Christina', 'EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.|EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.|EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.|EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Proteomics Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 1, 'Algorithms|Alternative Splicing|Databases, Protein|Humans|Internet|Mass Spectrometry|Peptides|Protein Isoforms|Proteomics', 'Journal Article', '10.1093/bioinformatics/btu428', '24996896', 'In proteomes of higher eukaryotes, many alternative splice variants can only be detected by their shared peptides. This makes it highly challenging to use peptide-centric mass spectrometry to distinguish and to quantify protein isoforms resulting from alternative splicing events.'),
(78538, 2014, 'bio', 'Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach', 'Yu, Tianwei|Jones, Dean P', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health and Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health and Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA.', 'United States|United States', 0, 'Artificial Intelligence|Biostatistics|Chromatography, Liquid|Humans|Mass Spectrometry|Metabolomics|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btu430', '25005748', 'Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.'),
(78539, 2014, 'bio', 'MCMC_CLIB-an advanced MCMC sampling package for ODE models', 'Kramer, Andrei|Stathopoulos, Vassilios|Girolami, Mark|Radde, Nicole', 'Institute for Systems Theory and Automatic Control, University of Stuttgart, 70569 Stuttgart, Germany, Department of Statistical Science, University College, London WC1E 6BT, UK and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.|Institute for Systems Theory and Automatic Control, University of Stuttgart, 70569 Stuttgart, Germany, Department of Statistical Science, University College, London WC1E 6BT, UK and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.|Institute for Systems Theory and Automatic Control, University of Stuttgart, 70569 Stuttgart, Germany, Department of Statistical Science, University College, London WC1E 6BT, UK and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.|Institute for Systems Theory and Automatic Control, University of Stuttgart, 70569 Stuttgart, Germany, Department of Statistical Science, University College, London WC1E 6BT, UK and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Markov Chains|Models, Statistical|Monte Carlo Method|Software', 'Journal Article', '10.1093/bioinformatics/btu429', '25005749', 'We present a new C implementation of an advanced Markov chain Monte Carlo (MCMC) method for the sampling of ordinary differential equation (ode) model parameters. The software mcmc_clib uses the simplified manifold Metropolis-adjusted Langevin algorithm (SMMALA), which is locally adaptive, it uses the parameter manifolds geometry (the Fisher information) to make efficient moves. This adaptation does not diminish with MC length, which is highly advantageous compared with adaptive Metropolis techniques when the parameters have large correlations and/or posteriors substantially differ from multivariate Gaussians. The software is standalone (not a toolbox), though dependencies include the GNU scientific library and sundials libraries for ode integration and sensitivity analysis.'),
(78540, 2014, 'bio', 'Fast spatial ancestry via flexible allele frequency surfaces', 'Rañola, John Michael|Novembre, John|Lange, Kenneth', 'Department of Statistics, University of Washington, Seattle, WA 98195, Department of Human Genetics, University of Chicago, Chicago, IL 60637 and Department of Biomathematics, Human Genetics, and Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, Department of Human Genetics, University of Chicago, Chicago, IL 60637 and Department of Biomathematics, Human Genetics, and Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Statistics, University of Washington, Seattle, WA 98195, Department of Human Genetics, University of Chicago, Chicago, IL 60637 and Department of Biomathematics, Human Genetics, and Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA.', 'United States|United States|United States', 8, 'Algorithms|Bayes Theorem|Gene Frequency|Genome, Human|Humans|Phylogeography|Polymorphism, Single Nucleotide|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btu418', '25012181', 'Unique modeling and computational challenges arise in locating the geographic origin of individuals based on their genetic backgrounds. Single-nucleotide polymorphisms (SNPs) vary widely in informativeness, allele frequencies change non-linearly with geography and reliable localization requires evidence to be integrated across a multitude of SNPs. These problems become even more acute for individuals of mixed ancestry. It is hardly surprising that matching genetic models to computational constraints has limited the development of methods for estimating geographic origins. We attack these related problems by borrowing ideas from image processing and optimization theory. Our proposed model divides the region of interest into pixels and operates SNP by SNP. We estimate allele frequencies across the landscape by maximizing a product of binomial likelihoods penalized by nearest neighbor interactions. Penalization smooths allele frequency estimates and promotes estimation at pixels with no data. Maximization is accomplished by a minorize-maximize (MM) algorithm. Once allele frequency surfaces are available, one can apply Bayes rule to compute the posterior probability that each pixel is the pixel of origin of a given person. Placement of admixed individuals on the landscape is more complicated and requires estimation of the fractional contribution of each pixel to a persons genome. This estimation problem also succumbs to a penalized MM algorithm.'),
(78541, 2014, 'bio', 'repfdr: a tool for replicability analysis for genome-wide association studies', 'Heller, Ruth|Yaacoby, Shay|Yekutieli, Daniel', 'Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv 6997801, Israel.|Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv 6997801, Israel.|Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv 6997801, Israel.', 'Israel|Israel|Israel', 0, 'Bayes Theorem|Biostatistics|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btu434', '25012182', 'Identification of single nucleotide polymorphisms that are associated with a phenotype in more than one study is of great scientific interest in the genome-wide association studies (GWAS) research. The empirical Bayes approach for discovering whether results have been replicated across studies was shown to be a reliable method, and close to optimal in terms of power.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78542, 2014, 'bio', 'MAGNA: Maximizing Accuracy in Global Network Alignment', 'Saraph, Vikram|Milenkovixc4x87, Tijana', 'Department of Computer Science and Engineering, University of Notre Dame, IN 46556, Department of Computer Science, Brown University, RI 02912, ECK Institute for Global Health, University of Notre Dame, IN 46556 and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, IN 46556, USA Department of Computer Science and Engineering, University of Notre Dame, IN 46556, Department of Computer Science, Brown University, RI 02912, ECK Institute for Global Health, University of Notre Dame, IN 46556 and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, IN 46556, USA.|Department of Computer Science and Engineering, University of Notre Dame, IN 46556, Department of Computer Science, Brown University, RI 02912, ECK Institute for Global Health, University of Notre Dame, IN 46556 and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, IN 46556, USA Department of Computer Science and Engineering, University of Notre Dame, IN 46556, Department of Computer Science, Brown University, RI 02912, ECK Institute for Global Health, University of Notre Dame, IN 46556 and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, IN 46556, USA Department of Computer Science and Engineering, University of Notre Dame, IN 46556, Department of Computer Science, Brown University, RI 02912, ECK Institute for Global Health, University of Notre Dame, IN 46556 and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, IN 46556, USA.', 'United States|United States', 95, 'Algorithms|Computational Biology|Humans|Protein Interaction Mapping|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btu409', '25015987', 'Biological network alignment aims to identify similar regions between networks of different species. Existing methods compute node similarities to rapidly identify from possible alignments the high-scoring alignments with respect to the overall node similarity. But, the accuracy of the alignments is then evaluated with some other measure that is different than the node similarity used to construct the alignments. Typically, one measures the amount of conserved edges. Thus, the existing methods align similar nodes between networks hoping to conserve many edges (after the alignment is constructed!).'),
(78543, 2014, 'bio', 'proovread: large-scale high-accuracy PacBio correction through iterative short read consensus', 'Hackl, Thomas|Hedrich, Rainer|Schultz, Jörg|Förster, Frank', 'Department for Molecular Plant Physiology and Biophysics, University of Würzburg, Julius-von-Sachs-Platz 2, 97082 Würzburg, Germany and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany Department for Molecular Plant Physiology and Biophysics, University of Würzburg, Julius-von-Sachs-Platz 2, 97082 Würzburg, Germany and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany.|Department for Molecular Plant Physiology and Biophysics, University of Würzburg, Julius-von-Sachs-Platz 2, 97082 Würzburg, Germany and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany.|Department for Molecular Plant Physiology and Biophysics, University of Würzburg, Julius-von-Sachs-Platz 2, 97082 Würzburg, Germany and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany.|Department for Molecular Plant Physiology and Biophysics, University of Würzburg, Julius-von-Sachs-Platz 2, 97082 Würzburg, Germany and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany.', 'Germany|Germany|Germany|Germany', 209, 'Arabidopsis|Base Sequence|Consensus Sequence|Escherichia coli|Gene Expression Profiling|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu392', '25015988', 'Today, the base code of DNA is mostly determined through sequencing by synthesis as provided by the Illumina sequencers. Although highly accurate, resulting reads are short, making their analyses challenging. Recently, a new technology, single molecule real-time (SMRT) sequencing, was developed that could address these challenges, as it generates reads of several thousand bases. But, their broad application has been hampered by a high error rate. Therefore, hybrid approaches that use high-quality short reads to correct erroneous SMRT long reads have been developed. Still, current implementations have great demands on hardware, work only in well-defined computing infrastructures and reject a substantial amount of reads. This limits their usability considerably, especially in the case of large sequencing projects.'),
(78544, 2014, 'bio', 'Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression', 'Cheng, Chun-Pei|Kuo, I-Ying|Alakus, Hakan|Frazer, Kelly A|Harismendy, Olivier|Wang, Yi-Ching|Tseng, Vincent S', 'Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Ph|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Ph|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Childrens Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Biomarkers, Tumor|Carcinoma, Squamous Cell|CpG Islands|DNA Methylation|Disease Progression|Epigenesis, Genetic|Esophageal Neoplasms|Esophageal Squamous Cell Carcinoma|Gene Regulatory Networks|Humans|Promoter Regions, Genetic', 'Journal Article', '10.1093/bioinformatics/btu433', '25015989', 'A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable biomarkers. Importantly, the integration of both expression and epigenetic alterations is more likely to identify relevant biomarkers.'),
(78545, 2014, 'bio', 'GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination', 'Gotz, Lou|Abrahams, Jodie L|Mariethoz, Julien|Rudd, Pauline M|Karlsson, Niclas G|Packer, Nicolle H|Campbell, Matthew P|Lisacek, Frederique', 'Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.|Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 21, 'Glycoside Hydrolases|Knowledge Bases|Oligosaccharides|Software', 'Journal Article', '10.1093/bioinformatics/btu425', '25015990', 'Sequencing oligosaccharides by exoglycosidases, either sequentially or in an array format, is a powerful tool to unambiguously determine the structure of complex N- and O-link glycans. Here, we introduce GlycoDigest, a tool that simulates exoglycosidase digestion, based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase. The tool allows the targeted design of glycosidase enzyme mixtures by allowing researchers to model the action of exoglycosidases, thereby validating and improving the efficiency and accuracy of glycan analysis.'),
(78546, 2014, 'bio', 'A new approach for detecting riboswitches in DNA sequences', 'Havill, Jessen T|Bhatiya, Chinmoy|Johnson, Steven M|Sheets, Joseph D|Thompson, Jeffrey S', 'Department of Mathematics and Computer Science, Department of Biology, Denison University, Granville, OH 43023, Capco, New York, NY, 10005, Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109 and College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA.|Department of Mathematics and Computer Science, Department of Biology, Denison University, Granville, OH 43023, Capco, New York, NY, 10005, Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109 and College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA.|Department of Mathematics and Computer Science, Department of Biology, Denison University, Granville, OH 43023, Capco, New York, NY, 10005, Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109 and College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA.|Department of Mathematics and Computer Science, Department of Biology, Denison University, Granville, OH 43023, Capco, New York, NY, 10005, Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109 and College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA.|Department of Mathematics and Computer Science, Department of Biology, Denison University, Granville, OH 43023, Capco, New York, NY, 10005, Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109 and College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA.', 'United States|United States|United States|United States|United States', 22, 'Algorithms|Riboswitch|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu479', '25015992', 'Riboswitches are short sequences of messenger RNA that can change their structural conformation to regulate the expression of adjacent genes. Computational prediction of putative riboswitches can provide direction to molecular biologists studying riboswitch-mediated gene expression.'),
(78547, 2014, 'bio', 'Multi-factor data normalization enables the detection of copy number aberrations in amplicon sequencing data', 'Boeva, Valentina|Popova, Tatiana|Lienard, Maxime|Toffoli, Sebastien|Kamal, Maud|Le Tourneau, Christophe|Gentien, David|Servant, Nicolas|Gestraud, Pierre|Rio Frio, Thomas|Hupé, Philippe|Barillot, Emmanuel|Laes, Jean-François', 'Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, |Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, Institut Curie, Centre de Recherche, 26 rue dUlm, Paris 75248, Mines ParisTech, Fontainebleau 77300, Inserm, U830, Genetics and Biology of Cancers, Paris 75248, France, Institut de Pathologie et de Génétique, Gosselies 6041, Belgium, Clinical Research Department, Department of Medical Oncology, Plateforme de Génomique, Département de recherche translationnelle, Centre de recherche, Next-generation sequencing platform, Institut Curie, CNRS, UMR144, Subcellular Structure and cellular Dynamics, Paris 75248, France and OncoDNA, Gosselies 6041, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belg', 57, 'Comparative Genomic Hybridization|DNA, Neoplasm|Exome|Female|Gene Dosage|Genes, Neoplasm|High-Throughput Nucleotide Sequencing|Humans|Male|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu436', '25016581', 'Because of its low cost, amplicon sequencing, also known as ultra-deep targeted sequencing, is now becoming widely used in oncology for detection of actionable mutations, i.e. mutations influencing cell sensitivity to targeted therapies. Amplicon sequencing is based on the polymerase chain reaction amplification of the regions of interest, a process that considerably distorts the information on copy numbers initially present in the tumor DNA. Therefore, additional experiments such as single nucleotide polymorphism (SNP) or comparative genomic hybridization (CGH) arrays often complement amplicon sequencing in clinics to identify copy number status of genes whose amplification or deletion has direct consequences on the efficacy of a particular cancer treatment. So far, there has been no proven method to extract the information on gene copy number aberrations based solely on amplicon sequencing.'),
(78548, 2015, 'bio', 'The Bioinformatics Open Source Conference (BOSC) 2013', 'Harris, Nomi L|Cock, Peter J A|Chapman, Brad A|Goecks, Jeremy|Hotz, Hans-Rudolf|Lapp, Hilmar', 'Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, MS 972R525, Berkeley, CA 94720, USA, Information and Computational Sciences, The James Hutton Institute, Dundee DD2 5DA, UK, Bioinformatics Core, Harvard School of Public Health, Boston, MA 02115, USA, The Computational Biology Institute, George Washington University, Washington, DC 20052, USA, Bioinformatics Department, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland and National Evolutionary Synthesis Center (NESCent), Durham, NC 27705, USA.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Biomedical Research|Computational Biology|Congresses as Topic|Genomics|Humans', 'Journal Article', '10.1093/bioinformatics/btu413', '25024288', '-'),
(78549, 2015, 'bio', 'Summary of the BioLINK SIG 2013 meeting at ISMB/ECCB 2013', 'Verspoor, Karin|Shatkay, Hagit|Hirschman, Lynette|Blaschke, Christian|Valencia, Alfonso', 'Department of Computing and Information Systems, The University of Melbourne, Parkville VIC 3010, Australia Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA, MITRE Corporation, Bedford, MA 01730, USA, Spanish Institute of Bioinformatics, Madrid, Spain and Spanish National Centre for Cancer Research, Madrid, Spain.|Department of Computing and Information Systems, The University of Melbourne, Parkville VIC 3010, Australia Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA, MITRE Corporation, Bedford, MA 01730, USA, Spanish Institute of Bioinformatics, Madrid, Spain and Spanish National Centre for Cancer Research, Madrid, Spain.|Department of Computing and Information Systems, The University of Melbourne, Parkville VIC 3010, Australia Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA, MITRE Corporation, Bedford, MA 01730, USA, Spanish Institute of Bioinformatics, Madrid, Spain and Spanish National Centre for Cancer Research, Madrid, Spain.|Department of Computing and Information Systems, The University of Melbourne, Parkville VIC 3010, Australia Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA, MITRE Corporation, Bedford, MA 01730, USA, Spanish Institute of Bioinformatics, Madrid, Spain and Spanish National Centre for Cancer Research, Madrid, Spain.|Department of Computing and Information Systems, The University of Melbourne, Parkville VIC 3010, Australia Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA, MITRE Corporation, Bedford, MA 01730, USA, Spanish Institute of Bioinformatics, Madrid, Spain and Spanish National Centre for Cancer Research, Madrid, Spain.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Computational Biology|Congresses as Topic|Data Mining|Humans|Periodicals as Topic|Research Report', 'Journal Article', '10.1093/bioinformatics/btu412', '25024289', 'The ISMB Special Interest Group on Linking Literature, Information and Knowledge for Biology (BioLINK) organized a one-day workshop at ISMB/ECCB 2013 in Berlin, Germany. The theme of the workshop was Roles for text mining in biomedical knowledge discovery and translational medicine. This summary reviews the outcomes of the workshop. Meeting themes included concept annotation methods and applications, extraction of biological relationships and the use of text-mined data for biological data analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78550, 2014, 'bio', 'An improved method for computing q-values when the distribution of effect sizes is asymmetric', 'Orr, Megan|Liu, Peng|Nettleton, Dan', 'Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.|Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.|Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States', 0, 'Arabidopsis|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Statistical Distributions|Zea mays', 'Journal Article', '10.1093/bioinformatics/btu432', '25024290', 'Asymmetry is frequently observed in the empirical distribution of test statistics that results from the analysis of gene expression experiments. This asymmetry indicates an asymmetry in the distribution of effect sizes. A common method for identifying differentially expressed (DE) genes in a gene expression experiment while controlling false discovery rate (FDR) is Storeys q-value method. This method ranks genes based solely on the P-values from each gene in the experiment.'),
(78551, 2014, 'bio', 'Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion', 'Bollig-Fischer, Aliccia|Marchetti, Luca|Mitrea, Cristina|Wu, Jiusheng|Kruger, Adxc3xa9le|Manca, Vincenzo|Drxc4x83ghici, Sorin', 'Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.|Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA Barbara Ann Karmanos Cancer Institute and Department of Oncology, Wayne State University, Detroit, MI 48201, USA, Department of Computer Science, University of Verona, 37134 Verona, Italy, The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068 Rovereto, Italy, Department of Computer Science, Wayne State University and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Breast Neoplasms|Cell Adhesion|Cell Line, Tumor|Cell-Matrix Junctions|E2F2 Transcription Factor|Female|Gene Expression Regulation, Neoplastic|Gene Knockdown Techniques|Genes, erbB-2|Humans|Models, Genetic|Signal Transduction|Transcription, Genetic|Transcriptome|Up-Regulation', 'Journal Article', '10.1093/bioinformatics/btu400', '25028721', 'Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies, however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes.'),
(78552, 2014, 'bio', 'ASP-G: an ASP-based method for finding attractors in genetic regulatory networks', 'Mushthofa, Mushthofa|Torres, Gustavo|Van de Peer, Yves|Marchal, Kathleen|De Cock, Martine', 'Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, |Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, |Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Gene Regulatory Networks', 'Journal Article', '10.1093/bioinformatics/btu481', '25028722', 'Boolean network models are suitable to simulate GRNs in the absence of detailed kinetic information. However, reducing the biological reality implies making assumptions on how genes interact (interaction rules) and how their state is updated during the simulation (update scheme). The exact choice of the assumptions largely determines the outcome of the simulations. In most cases, however, the biologically correct assumptions are unknown. An ideal simulation thus implies testing different rules and schemes to determine those that best capture an observed biological phenomenon. This is not trivial because most current methods to simulate Boolean network models of GRNs and to compute their attractors impose specific assumptions that cannot be easily altered, as they are built into the system.'),
(78553, 2014, 'bio', 'Journaled string tree-a scalable data structure for analyzing thousands of similar genomes on your laptop', 'Rahn, René|Weese, David|Reinert, Knut', 'Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany.', 'Germany|Germany|Germany', 18, 'Algorithms|Computers|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btu438', '25028723', 'Next-generation sequencing (NGS) has revolutionized biomedical research in the past decade and led to a continuous stream of developments in bioinformatics, addressing the need for fast and space-efficient solutions for analyzing NGS data. Often researchers need to analyze a set of genomic sequences that stem from closely related species or are indeed individuals of the same species. Hence, the analyzed sequences are similar. For analyses where local changes in the examined sequence induce only local changes in the results, it is obviously desirable to examine identical or similar regions not repeatedly.'),
(78554, 2014, 'bio', 'AffyPipe: an open-source pipeline for Affymetrix Axiom genotyping workflow', 'Nicolazzi, Ezequiel L|Iamartino, Daniela|Williams, John L', 'Fondazione Parco Tecnologico Padano, Lodi (LO), 26900, Italy.|Fondazione Parco Tecnologico Padano, Lodi (LO), 26900, Italy.|Fondazione Parco Tecnologico Padano, Lodi (LO), 26900, Italy.', 'Italy|Italy|Italy', 0, 'Genotype|Genotyping Techniques|Polymorphism, Single Nucleotide|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btu486', '25028724', 'The Affymetrix Axiom genotyping standard and best practice workflow for Linux and Mac users consists of three stand-alone executable programs (Affymetrix Power Tools) and an R package (SNPolisher). Currently, SNP analysis has to be performed in a step-by-step procedure. Manual intervention and/or programming skills by the user is required at each intermediate point, as Affymetrix Power Tools programs do not produce input files for the program next-in-line. An additional problem is that the output format of genotypes is not compatible with most analysis software currently available. AffyPipe solves all the above problems, by automating both standard and best practice workflows for any species genotyped with the Axiom technology. AffyPipe does not require programming skills and performs all the steps necessary to obtain a final genotype file. Furthermore, users can directly edit SNP probes and export genotypes in PLINK format.'),
(78555, 2014, 'bio', 'Resolving complex tandem repeats with long reads', 'Ummat, Ajay|Bashir, Ali', 'Department of Genetics and Genomic Science and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Science and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States', 28, 'Genome, Human|Humans|Markov Chains|Microsatellite Repeats|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu437', '25028725', 'Resolving tandemly repeated genomic sequences is a necessary step in improving our understanding of the human genome. Short tandem repeats (TRs), or microsatellites, are often used as molecular markers in genetics, and clinically, variation in microsatellites can lead to genetic disorders like Huntingtons diseases. Accurately resolving repeats, and in particular TRs, remains a challenging task in genome alignment, assembly and variation calling. Though tools have been developed for detecting microsatellites in short-read sequencing data, these are limited in the size and types of events they can resolve. Single-molecule sequencing technologies may potentially resolve a broader spectrum of TRs given their increased length, but require new approaches given their significantly higher raw error profiles. However, due to inherent error profiles of the single-molecule technologies, these reads presents a unique challenge in terms of accurately identifying and estimating the TRs.'),
(78556, 2014, 'bio', 'MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing', 'Calabrese, Claudia|Simone, Domenico|Diroma, Maria Angela|Santorsola, Mariangela|Guttà, Cristiano|Gasparre, Giuseppe|Picardi, Ernesto|Pesole, Graziano|Attimonelli, Marcella', 'Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.|Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Department of Biosciences, University of Milan, 20133 Milan, Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, 70126 Bari, Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Institute of Biomembranes and Bioenergetics, National Research Council, 70126 Bari, National Institute of Biostructures and Biosystems, 00136 Rome and Center of Excellence in Genomics for Biomedicine and Agri-food (CEGBA), University of Bari, 70126 Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 76, 'DNA, Mitochondrial|Genetic Variation|Genome, Mitochondrial|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btu483', '25028726', 'The increasing availability of mitochondria-targeted and off-target sequencing data in whole-exome and whole-genome sequencing studies (WXS and WGS) has risen the demand of effective pipelines to accurately measure heteroplasmy and to easily recognize the most functionally important mitochondrial variants among a huge number of candidates. To this purpose, we developed MToolBox, a highly automated pipeline to reconstruct and analyze human mitochondrial DNA from high-throughput sequencing data.'),
(78557, 2014, 'bio', 'Gustaf: Detecting and correctly classifying SVs in the NGS twilight zone', 'Trappe, Kathrin|Emde, Anne-Katrin|Ehrlich, Hans-Christian|Reinert, Knut', 'Department of Computer Science, Freie Universitxc3xa4t Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA Department of Computer Science, Freie Universitxc3xa4t Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA.|Department of Computer Science, Freie Universitxc3xa4t Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA.|Department of Computer Science, Freie Universitxc3xa4t Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA.|Department of Computer Science, Freie Universitxc3xa4t Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA.', 'Germany|Germany|Germany|Germany', 0, 'Genomic Structural Variation|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Sequence Deletion|Software|Translocation, Genetic', 'Journal Article', '10.1093/bioinformatics/btu431', '25028727', 'The landscape of structural variation (SV) including complex duplication and translocation patterns is far from resolved. SV detection tools usually exhibit low agreement, are often geared toward certain types or size ranges of variation and struggle to correctly classify the type and exact size of SVs.'),
(78558, 2014, 'bio', 'lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R', 'Hoffman, Gabriel E|Mezey, Jason G|Schadt, Eric E', 'Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA.', 'United States|United States|United States', 0, 'Genome-Wide Association Study|Genotype|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/btu435', '25035399', 'The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic markers while including prespecified covariates such as sex. Here we develop an efficient implementation of the linear mixed model that allows composite hypothesis tests to consider genotype interactions with variables such as other genotypes, environment, sex or ancestry. Our R package, lrgpr, allows interactive model fitting and examination of regression diagnostics to facilitate exploratory data analysis in the context of the linear mixed model. By leveraging parallel and out-of-core computing for datasets too large to fit in main memory, lrgpr is applicable to large GWAS datasets and next-generation sequencing data.'),
(78559, 2014, 'bio', 'Monte Carlo algorithms for Brownian phylogenetic models', 'Horvilleur, Benjamin|Lartillot, Nicolas', 'Université de Lyon, Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie, Biologie Évolutive, F-69622 Villeurbanne, France.|Université de Lyon, Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie, Biologie Évolutive, F-69622 Villeurbanne, France.', 'France|France', 0, 'Algorithms|Markov Chains|Models, Genetic|Monte Carlo Method|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btu485', '25053744', 'Brownian models have been introduced in phylogenetics for describing variation in substitution rates through time, with applications to molecular dating or to the comparative analysis of variation in substitution patterns among lineages. Thus far, however, the Monte Carlo implementations of these models have relied on crude approximations, in which the Brownian process is sampled only at the internal nodes of the phylogeny or at the midpoints along each branch, and the unknown trajectory between these sampled points is summarized by simple branchwise average substitution rates.'),
(78560, 2014, 'bio', 'sapFinder: an R/Bioconductor package for detection of variant peptides in shotgun proteomics experiments', 'Wen, Bo|Xu, Shaohang|Sheynkman, Gloria M|Feng, Qiang|Lin, Liang|Wang, Quanhui|Xu, Xun|Wang, Jun|Liu, Siqi', 'BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, Ch|BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China BGI-Shenzhen, Shenzhen 518083, China, Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA, Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China, Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia and Macau University of Science and Technology, Taipa, Macau 999078, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Amino Acid Substitution|Genetic Variation|High-Throughput Nucleotide Sequencing|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btu397', '25053745', 'Single nucleotide variations (SNVs) located within a reading frame can result in single amino acid polymorphisms (SAPs), leading to alteration of the corresponding amino acid sequence as well as function of a protein. Accurate detection of SAPs is an important issue in proteomic analysis at the experimental and bioinformatic level. Herein, we present sapFinder, an R software package, for detection of the variant peptides based on tandem mass spectrometry (MS/MS)-based proteomics data. This package automates the construction of variation-associated databases from public SNV repositories or sample-specific next-generation sequencing (NGS) data and the identification of SAPs through database searching, post-processing and generation of HTML-based report with visualized interface.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(78561, 2015, 'bio', 'Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge', 'Dayarian, Adel|Romero, Roberto|Wang, Zhiming|Biehl, Michael|Bilal, Erhan|Hormoz, Sahand|Meyer, Pablo|Norel, Raquel|Rhrissorrakrai, Kahn|Bhanot, Gyan|Luo, Feng|Tarca, Adi L', 'Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.|Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Animals|Cells, Cultured|Databases, Factual|Epithelial Cells|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Lung|Oligonucleotide Array Sequence Analysis|Phosphoproteins|Phosphorylation|Rats|Software|Species Specificity|Systems Biology|Translational Medical Research', 'Comparative Study', '10.1093/bioinformatics/btu490', '25061067', 'Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue.'),
(78562, 2014, 'bio', 'Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis', 'Zhao, Sihai Dave|Parmigiani, Giovanni|Huttenhower, Curtis|Waldron, Levi', 'Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.|Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.|Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.|Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.', 'United States|United States|United States|United States', 19, 'Algorithms|Breast Neoplasms|Female|Gene Expression Profiling|Genomics|Humans|Ovarian Neoplasms|Survival Analysis|Urinary Bladder Neoplasms', 'Journal Article', '10.1093/bioinformatics/btu488', '25061068', 'The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated.'),
(78563, 2014, 'bio', 'Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data', 'Zhang, Junpeng|Le, Thuc Duy|Liu, Lin|Liu, Bing|He, Jianfeng|Goodall, Gregory J|Li, Jiuyong', 'Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.|Faculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Childrens Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Epithelial-Mesenchymal Transition|Gene Expression Profiling|Gene Regulatory Networks|Humans|MicroRNAs|Neoplasms|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btu489', '25061069', 'MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships.'),
(78564, 2014, 'bio', 'STAMP: statistical analysis of taxonomic and functional profiles', 'Parks, Donovan H|Tyson, Gene W|Hugenholtz, Philip|Beiko, Robert G', 'Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada.|Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada.|Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada.|Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Advanced Water Management Centre, The University of Queensland, Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia and Dalhousie University, Halifax, Nova Scotia, Canada.', 'Australia|Australia|Australia|Australia', 413, 'Bacteria|Classification|Confidence Intervals|Cyanobacteria|Data Interpretation, Statistical|Genome, Bacterial|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btu494', '25061070', 'STAMP is a graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots.'),
(78565, 2014, 'bio', 'FisHiCal: an R package for iterative FISH-based calibration of Hi-C data', 'Shavit, Yoli|Hamey, Fiona Kathryn|Lio, Pietro', 'Computer Laboratory, University of Cambridge, Cambridge CB3 0FD and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, UK.|Computer Laboratory, University of Cambridge, Cambridge CB3 0FD and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, UK.|Computer Laboratory, University of Cambridge, Cambridge CB3 0FD and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Calibration|Cell Line|Chromatin|Humans|Image Processing, Computer-Assisted|In Situ Hybridization, Fluorescence|K562 Cells|Software', 'Journal Article', '10.1093/bioinformatics/btu491', '25061071', 'The fluorescence in situ hybridization (FISH) method has been providing valuable information on physical distances between loci (via image analysis) for several decades. Recently, high-throughput data on nearby chemical contacts between and within chromosomes became available with the Hi-C method. Here, we present FisHiCal, an R package for an iterative FISH-based Hi-C calibration that exploits in full the information coming from these methods. We describe here our calibration model and present 3D inference methods that we have developed for increasing its usability, namely, 3D reconstruction through local stress minimization and detection of spatial inconsistencies. We next confirm our calibration across three human cell lines and explain how the output of our methods could inform our model, defining an iterative calibration pipeline, with applications for quality assessment and meta-analysis.'),
(78566, 2014, 'bio', 'Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations', 'Vogt, Ingo|Prinz, Jeanette|Worf, Karolina|Campillos, Monica', 'German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany.|German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany.|German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany.|German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany and Technical University Munich WZW Chair of Bioinformatics, 80333 Munich, Germany.', 'Germany|Germany|Germany|Germany', 4, 'Animals|Disease|Drug-Related Side Effects and Adverse Reactions|Gene Expression|Genes|Genotype|Humans|Mice|Mutation|Organ Specificity|Phenotype|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btu487', '25061072', 'Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states.'),
(78567, 2014, 'bio', 'Compression and fast retrieval of SNP data', 'Sambo, Francesco|Di Camillo, Barbara|Toffolo, Gianna|Cobelli, Claudio', 'Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy', 3, 'Algorithms|Data Compression|Gene Frequency|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btu495', '25064564', 'The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data.'),
(78568, 2014, 'bio', 'img2net: automated network-based analysis of imaged phenotypes', 'Breuer, David|Nikoloski, Zoran', 'Mathematical Modeling and Systems Biology, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.|Mathematical Modeling and Systems Biology, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.', 'Germany|Germany', 2, 'Actin Cytoskeleton|Image Processing, Computer-Assisted|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btu503', '25064565', 'Automated analysis of imaged phenotypes enables fast and reproducible quantification of biologically relevant features. Despite recent developments, recordings of complex networked structures, such as leaf venation patterns, cytoskeletal structures or traffic networks, remain challenging to analyze. Here we illustrate the applicability of img2net to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties and statistically comparing networks of different types or under different conditions. The software can be readily used for analyzing image data of arbitrary 2D and 3D network-like structures.'),
(78569, 2014, 'bio', 'Kotai Antibody Builder: automated high-resolution structural modeling of antibodies', 'Yamashita, Kazuo|Ikeda, Kazuyoshi|Amada, Karlou|Liang, Shide|Tsuchiya, Yuko|Nakamura, Haruki|Shirai, Hiroki|Standley, Daron M', 'WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka 565-0871, National Institute of Biomedical Innovation, Ibaraki City, Osaka 567-0085, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871 and Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Antibodies|Complementarity Determining Regions|Internet|Models, Molecular|Sequence Alignment|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btu510', '25064566', 'Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge.'),
(78570, 2014, 'bio', 'CCMpred--fast and precise prediction of protein residue-residue contacts from correlated mutations', 'Seemayer, Stefan|Gruber, Markus|Söding, Johannes', 'Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377, Munich and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.|Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377, Munich and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.|Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377, Munich and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377, Munich and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.', 'Germany|Germany|Germany', 0, 'Mutation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btu500', '25064567', 'Recent breakthroughs in protein residue-residue contact prediction have made reliable de novo prediction of protein structures possible. The key was to apply statistical methods that can distinguish direct couplings between pairs of columns in a multiple sequence alignment from merely correlated pairs, i.e. to separate direct from indirect effects. Two classes of such methods exist, either relying on regularized inversion of the covariance matrix or on pseudo-likelihood maximization (PLM). Although PLM-based methods offer clearly higher precision, available tools are not sufficiently optimized and are written in interpreted languages that introduce additional overheads. This impedes the runtime and large-scale contact prediction for larger protein families, multi-domain proteins and protein-protein interactions.'),
(78571, 2014, 'bio', 'e-Driver: a novel method to identify protein regions driving cancer', 'Porta-Pardo, Eduard|Godzik, Adam', 'Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.|Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.', 'United States|United States', 0, 'Genes, Neoplasm|Genomics|Humans|Mutation Rate|Mutation, Missense|Neoplasm Proteins|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btu499', '25064568', 'Most approaches used to identify cancer driver genes focus, true to their name, on entire genes and assume that a gene, treated as one entity, has a specific role in cancer. This approach may be correct to describe effects of gene loss or changes in gene expression, however, mutations may have different effects, including their relevance to cancer, depending on which region of the gene they affect. Except for rare and well-known exceptions, there are not enough data for reliable statistics for individual positions, but an intermediate level of analysis, between an individual position and the entire gene, may give us better statistics than the former and better resolution than the latter approach.'),
(78572, 2014, 'bio', 'Comparison of the mammalian insulin signalling pathway to invertebrates in the context of FOXO-mediated ageing', 'Papatheodorou, Irene|Petrovs, Rudolfs|Thornton, Janet M', 'EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK.|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK.|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Caenorhabditis elegans|Drosophila melanogaster|Forkhead Transcription Factors|Insulin|Longevity|Mice|Models, Biological|Mutation|Signal Transduction|Software|Somatomedins', 'Comparative Study', '10.1093/bioinformatics/btu493', '25064569', 'A large number of experimental studies on ageing focus on the effects of genetic perturbations of the insulin/insulin-like growth factor signalling pathway (IIS) on lifespan. Short-lived invertebrate laboratory model organisms are extensively used to quickly identify ageing-related genes and pathways. It is important to extrapolate this knowledge to longer lived mammalian organisms, such as mouse and eventually human, where such analyses are difficult or impossible to perform. Computational tools are needed to integrate and manipulate pathway knowledge in different species.'),
(78573, 2014, 'bio', 'CCBuilder: an interactive web-based tool for building, designing and assessing coiled-coil protein assemblies', 'Wood, Christopher W|Bruning, Marc|Ibarra, Amaurys xc3x81|Bartlett, Gail J|Thomson, Andrew R|Sessions, Richard B|Brady, R Leo|Woolfson, Derek N', 'School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.|School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK School of Chemistry, University of Bristol, Cantocks Close, BS8 1TS and School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, BS8 1TD, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Internet|Models, Molecular|Protein Engineering|Protein Folding|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/btu502', '25064570', 'The ability to accurately model protein structures at the atomistic level underpins efforts to understand protein folding, to engineer natural proteins predictably and to design proteins de novo. Homology-based methods are well established and produce impressive results. However, these are limited to structures presented by and resolved for natural proteins. Addressing this problem more widely and deriving truly ab initio models requires mathematical descriptions for protein folds, the means to decorate these with natural, engineered or de novo sequences, and methods to score the resulting models.');
