INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79997, 2016, 'bio', 'Message from the ISCB: 2016 ISCB Accomplishment by a Senior Scientist Award Given to Søren Brunak', 'Fogg, Christiana N|Kovats, Diane K', 'Freelance Science Writer, Kensington, MD, USA dkovats@iscb.org.|ISCB, Bethesda, MD, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw291', '27246924', '-'),
(79998, 2016, 'bio', 'Message from the ISCB: 2016 Outstanding Contributions to ISCB Award: Burkhard Rost', 'Fogg, Christiana N|Kovats, Diane K', 'Freelance Science Writer, Kensington, MD, USA and dkovats@iscb.org.|ISCB, Bethesda, MD, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw292', '27246925', '-'),
(79999, 2016, 'bio', 'Benchmarking the next generation of homology inference tools', 'Saripella, Ganapathi Varma|Sonnhammer, Erik L L|Forslund, Kristoffer', 'Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Stockholm SE-10691, Sweden.|Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Stockholm SE-10691, Sweden.|European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg 69117, Germany.', 'Sweden|Sweden|Germany', 0, 'Benchmarking|Databases, Protein|High-Throughput Nucleotide Sequencing|Proteins|Sequence Homology|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btw305', '27256311', 'Over the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the next generation of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA.'),
(80000, 2017, 'bio', 'COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge', 'Lu, Yang Young|Chen, Ting|Fuhrman, Jed A|Sun, Fengzhu', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, CA, USA.|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Cluster Analysis|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw290', '27256312', 'The advent of next-generation sequencing technologies enables researchers to sequence complex microbial communities directly from the environment. Because assembly typically produces only genome fragments, also known as contigs, instead of an entire genome, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. OTU clustering is also referred to as binning. We present COCACOLA, a general framework automatically bin contigs into OTUs based on sequence composition and coverage across multiple samples.'),
(80001, 2016, 'bio', 'CART-a chemical annotation retrieval toolkit', 'Deghou, Samy|Zeller, Georg|Iskar, Murat|Driessen, Marja|Castillo, Mercedes|van Noort, Vera|Bork, Peer', 'Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany Max Delbrück Centre for Molecular Medicine, Berlin, Germany Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Belgium|Germany', 0, 'Computational Biology|Computer Graphics|Data Curation|Databases, Factual|Electronic Data Processing|Information Storage and Retrieval|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw233', '27256313', 'Data on bioactivities of drug-like chemicals are rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases.'),
(80002, 2016, 'bio', 'RANKS: a flexible tool for node label ranking and classification in biological networks', 'Valentini, Giorgio|Armano, Giuliano|Frasca, Marco|Lin, Jianyi|Mesiti, Marco|Re, Matteo', 'AnacletoLab, Department of Computer Science, University of Milan, 20135 Milan, Italy.|Department of Electric and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.|AnacletoLab, Department of Computer Science, University of Milan, 20135 Milan, Italy.|AnacletoLab, Department of Computer Science, University of Milan, 20135 Milan, Italy.|AnacletoLab, Department of Computer Science, University of Milan, 20135 Milan, Italy.|AnacletoLab, Department of Computer Science, University of Milan, 20135 Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 19, 'Algorithms|Computational Biology|Databases, Factual|Drug Repositioning|Genomics|Humans|Proteins|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw235', '27256314', 'RANKS is a flexible software package that can be easily applied to any bioinformatics task formalizable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implemented.'),
(80003, 2016, 'bio', 'SETH detects and normalizes genetic variants in text', 'Thomas, Philippe|Rocktäschel, Tim|Hakenberg, Jörg|Lichtblau, Yvonne|Leser, Ulf', 'Language Technology Lab, DFKI Berlin, Germany Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.|University College London, Gower Street, LondonWC1E 6BT, UK.|Illumina, Inc, 451 El Camino Real, Santa Clara, CA 95050, USA.|Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.|Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.', 'Germany|United Kingdom|United Kingdom|Germany|Germany', 0, 'Computational Biology|Data Curation|Data Mining|Genes|Genetic Variation|Humans|Information Storage and Retrieval|Natural Language Processing|PubMed|Publications|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btw234', '27256315', ': Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However, finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for isolated application and evaluation as well as a thorough documentation for integration into other applications.'),
(80004, 2017, 'bio', 'Designing robust watermark barcodes for multiplex long-read sequencing', 'Ezpeleta, Joaquín|Krsticevic, Flavia J|Bulacio, Pilar|Tapia, Elizabeth', 'Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Rosario, Argentina.|Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Rosario, Argentina.|Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Rosario, Argentina.|Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Rosario, Argentina.', 'Argentina|Argentina|Argentina|Argentina', 1, 'Computer Simulation|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw322', '27259539', 'To attain acceptable sample misassignment rates, current approaches to multiplex single-molecule real-time sequencing require upstream quality improvement, which is obtained from multiple passes over the sequenced insert and significantly reduces the effective read length. In order to fully exploit the raw read length on multiplex applications, robust barcodes capable of dealing with the full single-pass error rates are needed.'),
(80005, 2016, 'bio', 'UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling', 'Bhattacharya, Debswapna|Cao, Renzhi|Cheng, Jianlin', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science Informatics Institute, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 0, 'Benchmarking|Computational Biology|Models, Molecular|Models, Statistical|Physics|Probability|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw316', '27259540', 'Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named foldons through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling.'),
(80006, 2016, 'bio', 'MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data', 'Ulyantsev, Vladimir I|Kazakov, Sergey V|Dubinkina, Veronika B|Tyakht, Alexander V|Alexeev, Dmitry G', 'ITMO University, Saint Petersburg, Russian Federation.|ITMO University, Saint Petersburg, Russian Federation.|Federal Research and Clinical Centre of Physical-Chemical Medicine, Moscow, Russian Federation Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation.|Federal Research and Clinical Centre of Physical-Chemical Medicine, Moscow, Russian Federation Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation.|Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russian Federation', 15, 'Algorithms|Computational Biology|Databases, Genetic|Metagenome|Metagenomics|Microbiota', 'Journal Article', '10.1093/bioinformatics/btw312', '27259541', 'High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features.'),
(80007, 2016, 'bio', 'Group-combined P-values with applications to genetic association studies', 'Hu, Xiaonan|Zhang, Wei|Zhang, Sanguo|Ma, Shuangge|Li, Qizhai', 'School of Mathematical Sciences, University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management.|Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.|School of Mathematical Sciences, University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management.|Department of Biostatistics, Yale University, New Haven, CT, USA.|Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.', 'United States|China|United States|United States|China', 8, 'Databases, Genetic|Genetic Association Studies|Genome-Wide Association Study|Genomics|Genotype|Linkage Disequilibrium|Models, Statistical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw314', '27259542', 'In large-scale genetic association studies with tens of hundreds of single nucleotide polymorphisms (SNPs) genotyped, the traditional statistical framework of logistic regression using maximum likelihood estimator (MLE) to infer the odds ratios of SNPs may not work appropriately. This is because a large number of odds ratios need to be estimated, and the MLEs may be not stable when some of the SNPs are in high linkage disequilibrium. Under this situation, the P-value combination procedures seem to provide good alternatives as they are constructed on the basis of single-marker analysis.'),
(80008, 2016, 'bio', 'DBSI server: DNA binding site identifier', 'Sukumar, Shravan|Zhu, Xiaolei|Ericksen, Spencer S|Mitchell, Julie C', 'Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.|School of Life Sciences, Anhui University, Hefei, Anhui Province 230601, China.|Small Molecule Screening Facility.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706, USA.', 'United States|China|United States|United States', 0, 'Binding Sites|DNA|DNA-Binding Proteins|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw315', '27259543', ': Protein-nucleic acid interactions are among the most important intermolecular interactions in the regulation of cellular events. Identifying residues involved in these interactions from protein structure alone is an important challenge. Here we introduce the webserver interface to DNA Binding Site Identifier (DBSI), a powerful structure-based SVM model for the prediction and visualization of DNA binding sites on protein structures. DBSI has been shown to be a top-performing model to predict DNA binding sites on the surface of a protein or peptide and shows promise in predicting RNA binding sites.'),
(80009, 2016, 'bio', 'HDTD: analyzing multi-tissue gene expression data', 'Touloumis, Anestis|Marioni, John C|Tavaré, Simon', 'CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK.|CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Computational Biology|Gene Expression|Gene Expression Profiling|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw224', '27266441', 'By collecting multiple samples per subject, researchers can characterize intra-subject variation using physiologically relevant measurements such as gene expression profiling. This can yield important insights into fundamental biological questions ranging from cell type identity to tumour development. For each subject, the data measurements can be written as a matrix with the different subsamples (e.g. multiple tissues) indexing the columns and the genes indexing the rows. In this context, neither the genes nor the tissues are expected to be independent and straightforward application of traditional statistical methods that ignore this two-way dependence might lead to erroneous conclusions. Herein, we present a suite of tools embedded within the R/Bioconductor package HDTD for robustly estimating and performing hypothesis tests about the mean relationship and the covariance structure within the rows and columns. We illustrate the utility of HDTD by applying it to analyze data generated by the Genotype-Tissue Expression consortium.'),
(80010, 2016, 'bio', 'RJIVE for exploration of multi-source molecular data', 'OConnell, Michael J|Lock, Eric F', 'Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.|Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Electronic Data Processing|Humans|Metabolomics|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw324', '27273669', ': The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas.'),
(80011, 2016, 'bio', 'MetalPredator: a web server to predict iron-sulfur cluster binding proteomes', 'Valasatava, Yana|Rosato, Antonio|Banci, Lucia|Andreini, Claudia', 'Magnetic Resonance Center (CERM).|Magnetic Resonance Center (CERM) Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy.|Magnetic Resonance Center (CERM) Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy.|Magnetic Resonance Center (CERM) Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy.', '-|Italy|Italy|Italy', 17, 'Amino Acid Sequence|Forecasting|Internet|Iron|Proteome|Software|Sulfur', 'Journal Article', '10.1093/bioinformatics/btw238', '27273670', 'The prediction of the iron-sulfur proteome is highly desirable for biomedical and biological research but a freely available tool to predict iron-sulfur proteins has not been developed yet.'),
(80012, 2016, 'bio', 'MIA: non-targeted mass isotopolome analysis', 'Weindl, Daniel|Wegner, Andre|Hiller, Karsten', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg', 0, 'Gas Chromatography-Mass Spectrometry|Isotope Labeling|Mass Spectrometry|Metabolic Networks and Pathways|Metabolomics', 'Journal Article', '10.1093/bioinformatics/btw317', '27273671', 'MIA detects and visualizes isotopic enrichment in gas chromatography electron ionization mass spectrometry (GC-EI-MS) datasets in a non-targeted manner. It provides an easy-to-use graphical user interface that allows for visual mass isotopomer distribution analysis across multiple datasets. MIA helps to reveal changes in metabolic fluxes, visualizes metabolic proximity of isotopically enriched compounds and shows the fate of the applied stable isotope labeled tracer.'),
(80013, 2016, 'bio', 'Predicting regulatory variants with composite statistic', 'Li, Mulin Jun|Pan, Zhicheng|Liu, Zipeng|Wu, Jiexing|Wang, Panwen|Zhu, Yun|Xu, Feng|Xia, Zhengyuan|Sham, Pak Chung|Kocher, Jean-Pierre A|Li, Miaoxin|Liu, Jun S|Wang, Junwen', 'Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901xe2x80x89MA, USA, Centre for Genomic Sciences.|Centre for Genomic Sciences, Department of Psychiatry.|Centre for Genomic Sciences, Department of Anaesthesiology.|Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901xe2x80x89MA, USA.|Centre for Genomic Sciences.|Centre for Genomic Sciences, School of Biomedical Sciences.|Centre for Genomic Sciences.|Department of Anaesthesiology.|Centre for Genomic Sciences, Department of Psychiatry.|Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and.|Centre for Genomic Sciences, Department of Psychiatry, Centre for Reproduction, Development and Growth, LKS Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.|Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901xe2x80x89MA, USA, Center for Statistical Science, Tsinghua University, Beijing 100084, China and.|Centre for Genomic Sciences, Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|China|China|United States', 0, 'Algorithms|Biological Evolution|Genetic Variation|Humans|Models, Theoretical|Molecular Sequence Annotation|RNA, Untranslated|Software', 'Journal Article', '10.1093/bioinformatics/btw288', '27273672', 'Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.'),
(80014, 2017, 'bio', 'Accurate self-correction of errors in long reads using de Bruijn graphs', 'Salmela, Leena|Walve, Riku|Rivals, Eric|Ukkonen, Esko', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|LIRMM and Institut de Biologie Computationelle, CNRS and Universitxc3xa9 Montpellier, Montpellier, France.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|France|Finland', 33, 'Algorithms|Escherichia coli|Genome|High-Throughput Nucleotide Sequencing|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw321', '27273673', 'New long read sequencing technologies, like PacBio SMRT and Oxford NanoPore, can produce sequencing reads up to 50 000xe2x80x89bp long but with an error rate of at least 15%. Reducing the error rate is necessary for subsequent utilization of the reads in, e.g. de novo genome assembly. The error correction problem has been tackled either by aligning the long reads against each other or by a hybrid approach that uses the more accurate short reads produced by second generation sequencing technologies to correct the long reads.'),
(80015, 2016, 'bio', 'pong: fast analysis and visualization of latent clusters in population genetic data', 'Behr, Aaron A|Liu, Katherine Z|Liu-Fang, Gracie|Nakka, Priyanka|Ramachandran, Sohini', 'Department of Ecology and Evolutionary Biology Department of Computer Science, Brown University, Providence, RI, USA.|Department of Computer Science, Brown University, Providence, RI, USA.|Computer Science Department, Wellesley College, Wellesley, MA, USA.|Department of Ecology and Evolutionary Biology Center for Computational Molecular Biology, Brown University, Providence, RI, USA.|Department of Ecology and Evolutionary Biology Center for Computational Molecular Biology, Brown University, Providence, RI, USA.', 'United States|United States|United States|United States|United States', 33, 'Algorithms|Cluster Analysis|Computer Graphics|Data Mining|Genetics, Population|Humans|Population Groups|Programming Languages|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btw327', '27283948', 'A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixed-membership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixed-membership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining.'),
(80016, 2016, 'bio', 'A knowledge-based approach for predicting gene-disease associations', 'Zhou, Hongyi|Skolnick, Jeffrey', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia', 30, 'Algorithms|Databases, Genetic|Disease|Epistasis, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Knowledge Bases|Models, Genetic|Protein Interaction Mapping|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btw358', '27283949', 'Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method.'),
(80017, 2016, 'bio', 'KCMBT: a k-mer Counter based on Multiple Burst Trees', 'Mamun, Abdullah-Al|Pal, Soumitra|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.', 'United States|United States|United States', 10, 'Algorithms|Base Sequence|Computational Biology|Genome|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw345', '27283950', 'A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications.'),
(80018, 2016, 'bio', 'GHap: an R package for genome-wide haplotyping', 'Utsunomiya, Yuri T|Milanesi, Marco|Utsunomiya, Adam T H|Ajmone-Marsan, Paolo|Garcia, José F', 'Departamento de Medicina Veterinária Preventiva e Reprodução Animal, UNESP - Univ. Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, 14884-900 Jaboticabal, São Paulo, Brazil International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, 16050-680 Araçatuba, São Paulo, Brazil.|International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, 16050-680 Araçatuba, São Paulo, Brazil Departamento de Apoio, Produção e Saúde Animal, UNESP - Univ. Estadual Paulista, Faculdade de Medicina Veterinária de Araçatuba, 16050-680 Araçatuba, São Paulo, Brazil.|International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, 16050-680 Araçatuba, São Paulo, Brazil Departamento de Apoio, Produção e Saúde Animal, UNESP - Univ. Estadual Paulista, Faculdade de Medicina Veterinária de Araçatuba, 16050-680 Araçatuba, São Paulo, Brazil.|Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy.|Departamento de Medicina Veterinária Preventiva e Reprodução Animal, UNESP - Univ. Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, 14884-900 Jaboticabal, São Paulo, Brazil International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, 16050-680 Araçatuba, São Paulo, Brazil Departamento de Apoio, Produção e Saúde Animal, UNESP - Univ. Estadual Paulista, Faculdade de Medicina Veterinária de Araçatuba, 16050-680 Araçatuba, São Paulo, Brazil.', 'Brazil|Brazil|Brazil|Italy|Brazil', 10, 'Computational Biology|Computer Simulation|Electronic Data Processing|Genotype|Haplotypes|Humans|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/btw356', '27283951', 'The GHap R package was designed to call haplotypes from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers.'),
(80019, 2016, 'bio', 'TaggerOne: joint named entity recognition and normalization with semi-Markov Models', 'Leaman, Robert|Lu, Zhiyong', 'National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA.', 'United States|United States', 86, 'Computational Biology|Data Mining|Disease|Forecasting|Information Storage and Retrieval|Knowledge Bases|Machine Learning|Pattern Recognition, Automated|Semantics|Software|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btw343', '27283952', 'Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization.'),
(80020, 2016, 'bio', 'Zerone: a ChIP-seq discretizer for multiple replicates with built-in quality control', 'Cuscxc3xb3, Pol|Filion, Guillaume J', 'Genome Architecture, Gene Regulation, Stem Cells and Cancer Programme, Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Barcelona 08003, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Genome Architecture, Gene Regulation, Stem Cells and Cancer Programme, Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Barcelona 08003, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain.', 'Spain|Spain', 3, 'Animals|Chromatin Immunoprecipitation|DNA Replication|Genome|Quality Control|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw336', '27288492', 'Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard method to investigate chromatin protein composition. As the number of community-available ChIP-seq profiles increases, it becomes more common to use data from different sources, which makes joint analysis challenging. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing datasets, especially when they are produced by different laboratories.'),
(80021, 2016, 'bio', 'Meshable: searching PubMed abstracts by utilizing MeSH and MeSH-derived topical terms', 'Kim, Sun|Yeganova, Lana|Wilbur, W John', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 18, 'Abstracting and Indexing as Topic|MEDLINE|Medical Subject Headings|PubMed|Search Engine', 'Journal Article', '10.1093/bioinformatics/btw331', '27288493', 'Medical Subject Headings (MeSH(®)) is a controlled vocabulary for indexing and searching biomedical literature. MeSH terms and subheadings are organized in a hierarchical structure and are used to indicate the topics of an article. Biologists can use either MeSH terms as queries or the MeSH interface provided in PubMed(®) for searching PubMed abstracts. However, these are rarely used, and there is no convenient way to link standardized MeSH terms to user queries. Here, we introduce a web interface which allows users to enter queries to find MeSH terms closely related to the queries. Our method relies on co-occurrence of text words and MeSH terms to find keywords that are related to each MeSH term. A query is then matched with the keywords for MeSH terms, and candidate MeSH terms are ranked based on their relatedness to the query. The experimental results show that our method achieves the best performance among several term extraction approaches in terms of topic coherence. Moreover, the interface can be effectively used to find full names of abbreviations and to disambiguate user queries.'),
(80022, 2016, 'bio', 'bdvis: visualizing biodiversity data in R', 'Barve, Vijay|Otegui, Javier', 'Florida Museum of Natural History, University of Florida, Gainesville, FL, USA.|University of Colorado Museum of Natural History, Boulder, CO, USA.', 'United States|United States', 2, 'Biodiversity|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw333', '27288494', 'Biodiversity studies are relying increasingly on primary biodiversity records (PBRs) for modelling and analysis. Because biodiversity data are frequently harvested-i.e. not collected by the researcher for that particular study, but obtained from data aggregators such as the Global Biodiversity Information Facility-researchers need to be aware of strengths and weaknesses of their data before they venture into further analysis. R is becoming a lingua franca of data exploration and analysis. Here, we describe an R package, bdvis, which facilitates efforts to understand the gaps and strengths of PBR data with quick and useful visualization functions.'),
(80023, 2016, 'bio', 'Inferring the perturbation time from biological time course data', 'Yang, Jing|Penfold, Christopher A|Grant, Murray R|Rattray, Magnus', 'Faculty of Life Sciences, University of Manchester, Manchester, UK.|Warwick Systems Biology Centre, University of Warwick, Coventry, UK.|School of Biosciences, University of Exeter, Exeter, UK.|Faculty of Life Sciences, University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Arabidopsis|Bayes Theorem|Models, Genetic|Models, Statistical|Pseudomonas syringae|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btw329', '27288495', 'Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the specific time when the two time course datasets first begin to diverge after a perturbation, we call this the perturbation time. Estimation of the perturbation time for different variables in a biological process allows us to identify the sequence of events following a perturbation and therefore provides valuable insights into likely causal relationships.'),
(80024, 2016, 'bio', 'The Glycome Analytics Platform: an integrative framework for glycobioinformatics', 'Barnett, Christopher B|Aoki-Kinoshita, Kiyoko F|Naidoo, Kevin J', 'Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa.|Department of Bioinformatics, Faculty of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan.|Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa.', 'South Africa|Japan|South Africa', 1, 'Carbohydrates|Databases, Factual|Glycomics|Humans|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw341', '27288496', 'Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata.'),
(80025, 2016, 'bio', 'RTFBSDB: an integrated framework for transcription factor binding site analysis', 'Wang, Zhong|Martins, André L|Danko, Charles G', 'Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.|Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.|Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.', 'United States|United States|United States', 12, 'Animals|Binding Sites|Computational Biology|Genome|Humans|Nucleotide Motifs|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw338', '27288497', 'Transcription factors (TFs) regulate complex programs of gene transcription by binding to short DNA sequence motifs. Here, we introduce rtfbsdb, a unified framework that integrates a database of more than 65 000 TF binding motifs with tools to easily and efficiently scan target genome sequences. Rtfbsdb clusters motifs with similar DNA sequence specificities and integrates RNA-seq or PRO-seq data to restrict analyses to motifs recognized by TFs expressed in the cell type of interest. Our package allows common analyses to be performed rapidly in an integrated environment.'),
(80026, 2016, 'bio', 'Prediction of conserved long-range RNA-RNA interactions in full viral genomes', 'Fricke, Markus|Marz, Manja', 'Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.|Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany FLI Leibniz Institute for Age Research, Jena, Germany.', 'Germany|Germany', 11, 'Computer Simulation|Genome, Viral|RNA, Viral|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw323', '27288498', 'Long-range RNA-RNA interactions (LRIs) play an important role in viral replication, however, only a few of these interactions are known and only for a small number of viral species. Up to now, it has been impossible to screen a full viral genome for LRIs experimentally or in silico Most known LRIs are cross-reacting structures (pseudoknots) undetectable by most bioinformatical tools.'),
(80027, 2016, 'bio', 'GenVisR: Genomic Visualizations in R', 'Skidmore, Zachary L|Wagner, Alex H|Lesurf, Robert|Campbell, Katie M|Kunisaki, Jason|Griffith, Obi L|Griffith, Malachi', 'McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA Department of Medicine Siteman Cancer Center Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA Siteman Cancer Center Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.', 'United States|United States|United States|United States|United States|United States|United States', 71, 'Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btw325', '27288499', 'Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor.'),
(80028, 2016, 'bio', 'Rapid and powerful detection of subtle allelic imbalance from exome sequencing data with hapLOHseq', 'San Lucas, F Anthony|Sivakumar, Smruthy|Vattathil, Selina|Fowler, Jerry|Vilar, Eduardo|Scheet, Paul', 'Department of Translational Molecular Pathology The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.|Department of Translational Molecular Pathology Department of Epidemiology.|Department of Translational Molecular Pathology Department of Epidemiology.|Department of Epidemiology.|Department of Translational Molecular Pathology Department of Gastrointestinal Medical Oncology Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA Department of Epidemiology.', 'United States|United States|United States|United States|United States|United States', 9, 'Allelic Imbalance|Exome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw340', '27288500', 'The detection of subtle genomic allelic imbalance events has many potential applications. For example, identifying cancer-associated allelic imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle allelic imbalance events from next-generation sequencing data.'),
(80029, 2016, 'bio', 'RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing data', 'Stokowy, Tomasz|Garbulowski, Mateusz|Fiskerstrand, Torunn|Holdhus, Rita|Labun, Kornel|Sztromwasser, Pawel|Gilissen, Christian|Hoischen, Alexander|Houge, Gunnar|Petersen, Kjell|Jonassen, Inge|Steen, Vidar M', 'Department of Clinical Science, University of Bergen, Bergen 5020, Norway Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway.|Department of Informatics, Silesian University of Technology, Gliwice 44-100, Poland.|Department of Clinical Science, University of Bergen, Bergen 5020, Norway Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5021, Norway.|Department of Clinical Science, University of Bergen, Bergen 5020, Norway.|Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway.|Department of Clinical Science, University of Bergen, Bergen 5020, Norway Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway.|Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525, The Netherlands.|Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525, The Netherlands.|Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5021, Norway.|Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway.|Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway.|Department of Clinical Science, University of Bergen, Bergen 5020, Norway Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5021, Norway.', 'Norway|Poland|Norway|Norway|Norway|Norway|Netherlands|Netherlands|Norway|Norway|Norway|Norway', 0, 'Exome|Genetic Variation|Genome, Human|Humans|Rare Diseases|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw359', '27288501', 'The search for causative genetic variants in rare diseases of presumed monogenic inheritance has been boosted by the implementation of whole exome (WES) and whole genome (WGS) sequencing. In many cases, WGS seems to be superior to WES, but the analysis and visualization of the vast amounts of data is demanding.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80030, 2016, 'bio', 'Web-based network analysis and visualization using CellMaps', 'Salavert, Francisco|García-Alonso, Luz|Sánchez, Rubén|Alonso, Roberto|Bleda, Marta|Medina, Ignacio|Dopazo, Joaquín', 'Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain Bull-CIPF, Computational Genomics Chair, Valencia 46012, Spain.|Department of Medicine, University of Cambridge, School of Clinical Medicine, Addenbrookes Hospital, Hills Road, Cambridge CB2 0QQ, UK.|HPC Service, UIS, University of Cambridge, Cambridge, UK.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain Functional Genomics Node, (INB, PRB2, ISCIII) at CIPF, Valencia 46012, Spain.', 'Spain|Spain|Spain|Spain|United Kingdom|United Kingdom|Spain', 0, 'Biochemical Phenomena|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btw332', '27296979', ': CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API.'),
(80031, 2016, 'bio', 'Unbiased probabilistic taxonomic classification for DNA barcoding', 'Somervuo, Panu|Koskela, Sonja|Pennanen, Juho|Henrik Nilsson, R|Ovaskainen, Otso', 'Department of Biosciences, University of Helsinki, Helsinki, Finland.|Department of Biosciences, University of Helsinki, Helsinki, Finland.|Department of Biosciences, University of Helsinki, Helsinki, Finland.|Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.|Department of Biosciences, University of Helsinki, Helsinki, Finland Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.', 'Finland|Finland|Finland|Sweden|Finland', 0, 'DNA Barcoding, Taxonomic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btw346', '27296980', 'When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors.'),
(80032, 2016, 'bio', 'CONDOP: an R package for CONdition-Dependent Operon Predictions', 'Fortino, Vittorio|Tagliaferri, Roberto|Greco, Dario', 'Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, Italy.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland.', 'Finland|Italy|Finland', 0, 'High-Throughput Nucleotide Sequencing|Operon|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw330', '27296981', 'The use of high-throughput RNA sequencing to predict dynamic operon structures in prokaryotic genomes has recently gained popularity in bioinformatics. We provide the R implementation of a novel method that uses transcriptomic features extracted from RNA-seq transcriptome profiles to develop ensemble classifiers for condition-dependent operon predictions. The CONDOP package provides a deeper insight into RNA-seq data analysis and allows scientists to highlight the operon organization in the context of transcriptional regulation with a few lines of code.'),
(80033, 2016, 'bio', 'GSA-Lightning: ultra-fast permutation-based gene set analysis', 'Chang, Billy Heung Wing|Tian, Weidong', 'State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200436, Peoples Republic of China Jockey Club School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, Hong Kong SAR.|State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200436, Peoples Republic of China.', 'China|China', 0, 'Animals|Genes|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw349', '27296982', 'The computational speed of many gene set analysis methods can be slow due to the computationally demanding permutation step. This article introduces GSA-Lightning, a fast implementation of permutation-based gene set analysis. GSA-Lightning achieves significant speedup compared with existing methods, particularly when the number of gene sets and permutations are large.'),
(80034, 2016, 'bio', 'A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits', 'Marchetti-Bowick, Micol|Yin, Junming|Howrylak, Judie A|Xing, Eric P', 'Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Management Information Systems, University of Arizona, Tucson, AZ, USA.|Division of Pulmonary and Critical Care Medicine, Penn State University, Milton S. Hershey Medical Center, Hershey, PA, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 4, 'Chromosome Mapping|Genome|Genome-Wide Association Study|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw347', '27296983', 'Despite the widespread popularity of genome-wide association studies (GWAS) for genetic mapping of complex traits, most existing GWAS methodologies are still limited to the use of static phenotypes measured at a single time point. In this work, we propose a new method for association mapping that considers dynamic phenotypes measured at a sequence of time points. Our approach relies on the use of Time-Varying Group Sparse Additive Models (TV-GroupSpAM) for high-dimensional, functional regression.'),
(80035, 2016, 'bio', 'Exploring the structure and function of temporal networks with dynamic graphlets', 'Hulovatyy, Y|Chen, H|Milenkovixc4x87, T', 'Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw310', '27301405', '-'),
(80036, 2016, 'bio', 'ISMB 2016 Proceedings', 'Baldi, Pierre|Przytycka, Teresa', 'Department of Computer Science University of California, Irvine.|National Center of Biotechnology Information, NIH.', '-|-', 0, '-', 'Editorial', '10.1093/bioinformatics/btw326', '27307605', '-'),
(80037, 2016, 'bio', 'Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks', 'Glicksberg, Benjamin S|Li, Li|Badgeley, Marcus A|Shameer, Khader|Kosoy, Roman|Beckmann, Noam D|Pho, Nam|Hakenberg, Jxc3xb6rg|Ma, Meng|Ayers, Kristin L|Hoffman, Gabriel E|Dan Li, Shuyu|Schadt, Eric E|Patel, Chirag J|Chen, Rong|Dudley, Joel T', 'Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Biomedical Informatics, Harvard Medical School, Boston, 02115xe2x80x89MA, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Biomedical Informatics, Harvard Medical School, Boston, 02115xe2x80x89MA, USA.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology.|Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA Department of Population Health Science and Policy, New York City, NY 10029, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 16, 'African Americans|Databases, Factual|Electronic Health Records|European Continental Ancestry Group|Hispanic Americans|Humans', 'Comparative Study', '10.1093/bioinformatics/btw282', '27307606', 'Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations, hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).'),
(80038, 2016, 'bio', 'A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps', 'Hu, Xihao|Shi, Christina Huan|Yip, Kevin Y', 'Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|Department of Computer Science and Engineering Hong Kong Bioinformatics Centre CUHK-BGI Innovation Institute of Trans-omics Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.', '-|-|Hong Kong', 0, 'DNA|Genome|Genomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw256', '27307607', 'The three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into general categories based on some global properties of the contact maps.'),
(80039, 2016, 'bio', 'Convolutional neural network architectures for predicting DNA-protein binding', 'Zeng, Haoyang|Edwards, Matthew D|Liu, Ge|Gifford, David K', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.', 'United States|United States|United States|United States', 158, 'Algorithms|DNA|Neural Networks (Computer)|Protein Binding|Proteins', 'Journal Article', '10.1093/bioinformatics/btw255', '27307608', 'Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.'),
(80040, 2016, 'bio', 'Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways', 'Jo, Kyuri|Jung, Inuk|Moon, Ji Hwan|Kim, Sun', 'Department of Computer Science and Engineering.|Interdisciplinary Program in Bioinformatics.|Interdisciplinary Program in Bioinformatics.|Department of Computer Science and Engineering Interdisciplinary Program in Bioinformatics Bioinformatics Institute, Seoul National University, Seoul 08826, Korea.', '-|-|-|-', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw275', '27307609', 'To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension.'),
(80041, 2016, 'bio', 'A cross-species bi-clustering approach to identifying conserved co-regulated genes', 'Sun, Jiangwen|Jiang, Zongliang|Tian, Xiuchun|Bi, Jinbo', 'Department of Computer Science and Engineering.|Center for Regenerative Biology and Department of Animal Science, University of Connecticut, Storrs, CT 06269, USA.|Center for Regenerative Biology and Department of Animal Science, University of Connecticut, Storrs, CT 06269, USA.|Department of Computer Science and Engineering.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Cluster Analysis|Female|Gene Expression|Gene Expression Profiling|Humans|Mice', 'Journal Article', '10.1093/bioinformatics/btw278', '27307610', 'A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes.'),
(80042, 2016, 'bio', 'Analysis of differential splicing suggests different modes of short-term splicing regulation', 'Topa, Hande|Honkela, Antti', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo 00076, Finland Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki 00014, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki 00014, Finland.', 'Finland|Finland', 6, 'Alternative Splicing|Gene Expression Profiling|Humans|Protein Isoforms|RNA Precursors|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw283', '27307611', 'Alternative splicing is an important mechanism in which the regions of pre-mRNAs are differentially joined in order to form different transcript isoforms. Alternative splicing is involved in the regulation of normal physiological functions but also linked to the development of diseases such as cancer. We analyse differential expression and splicing using RNA-sequencing time series in three different settings: overall gene expression levels, absolute transcript expression levels and relative transcript expression levels.'),
(80043, 2016, 'bio', 'Using genomic annotations increases statistical power to detect eGenes', 'Duong, Dat|Zou, Jennifer|Hormozdiari, Farhad|Sul, Jae Hoon|Ernst, Jason|Han, Buhm|Eskin, Eleazar', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Department of Computer Science Department of Biological Chemistry.|Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.|Department of Computer Science Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Genetic Variation|Genomics|Genotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btw272', '27307612', 'Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.'),
(80044, 2016, 'bio', 'A network-driven approach for genome-wide association mapping', 'Lee, Seunghak|Kong, Soonho|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 4, 'Chromosome Mapping|Genetic Association Studies|Genome-Wide Association Study|Genotype|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw270', '27307613', 'It remains a challenge to detect associations between genotypes and phenotypes because of insufficient sample sizes and complex underlying mechanisms involved in associations. Fortunately, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes, giving us new opportunities to detect true genotype-phenotype associations while unveiling their association mechanisms.'),
(80045, 2016, 'bio', 'deBWT: parallel construction of Burrows-Wheeler Transform for large collection of genomes with de Bruijn-branch encoding', 'Liu, Bo|Zhu, Dixian|Wang, Yadong', 'Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|China', 0, 'Algorithms|Genome, Human|Genomics|Humans|Programming Languages|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw266', '27307614', 'With the development of high-throughput sequencing, the number of assembled genomes continues to rise. It is critical to well organize and index many assembled genomes to promote future genomics studies. Burrows-Wheeler Transform (BWT) is an important data structure of genome indexing, which has many fundamental applications, however, it is still non-trivial to construct BWT for large collection of genomes, especially for highly similar or repetitive genomes. Moreover, the state-of-the-art approaches cannot well support scalable parallel computing owing to their incremental nature, which is a bottleneck to use modern computers to accelerate BWT construction.'),
(80046, 2016, 'bio', 'DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank', 'Yuan, Qingjun|Gao, Junning|Wu, Dongliang|Zhang, Shihua|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'School of Computer Science, Fudan University, Shanghai, China Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|School of Computer Science, Fudan University, Shanghai, China Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|School of Computer Science, Fudan University, Shanghai, China Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan Department of Computer Science, Aalto University, Finland.|School of Computer Science, Fudan University, Shanghai, China Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China Centre for Computational System Biology, Fudan University, Shanghai, China.', 'China|China|China|China|Finland|China', 57, 'Computational Biology|Databases, Pharmaceutical|Drug Delivery Systems|Drug Discovery|Drug Interactions|Genomics|Pharmaceutical Preparations|Software|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bioinformatics/btw244', '27307615', 'Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets.'),
(80047, 2016, 'bio', 'Prediction of ribosome footprint profile shapes from transcript sequences', 'Liu, Tzu-Yu|Song, Yun S', 'Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Electrical Engineering and Computer Sciences.|Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Electrical Engineering and Computer Sciences Department of Statistics and Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.', 'United States|United States', 7, 'Models, Theoretical|Protein Biosynthesis|RNA, Messenger|Ribosomes|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btw253', '27307616', 'Ribosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism.'),
(80048, 2016, 'bio', 'RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes', 'Srivastava, Avi|Sarkar, Hirak|Gupta, Nitish|Patro, Rob', 'Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA.|Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA.|Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA.|Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA.', 'United States|United States|United States|United States', 39, 'Algorithms|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw277', '27307617', 'The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.'),
(80049, 2016, 'bio', 'Compacting de Bruijn graphs from sequencing data quickly and in low memory', 'Chikhi, Rayan|Limasset, Antoine|Medvedev, Paul', 'CNRS, CRIStAL, Lille, France.|ENS Cachan Brittany, Bruz, France.|Department of Computer Science and Engineering, The Pennsylvania State University, USA Department of Biochemistry and Molecular Biology, The Pennsylvania State University, USA Genome Sciences Institute of the Huck, The Pennsylvania State University, USA.', 'France|France|United States', 0, 'Algorithms|Humans|Programming Languages|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw279', '27307618', 'As the quantity of data per sequencing experiment increases, the challenges of fragment assembly are becoming increasingly computational. The de Bruijn graph is a widely used data structure in fragment assembly algorithms, used to represent the information from a set of reads. Compaction is an important data reduction step in most de Bruijn graph based algorithms where long simple paths are compacted into single vertices. Compaction has recently become the bottleneck in assembly pipelines, and improving its running time and memory usage is an important problem.'),
(80050, 2016, 'bio', 'phRAIDER: Pattern-Hunter based Rapid Ab Initio Detection of Elementary Repeats', 'Schaeffer, Carly E|Figueroa, Nathaniel D|Liu, Xiaolin|Karro, John E', 'Department of Computer Science and Software Engineering.|Department of Computer Science and Software Engineering.|Department of Cell, Molecular, and Structural Biology.|Department of Computer Science and Software Engineering Department of Cell, Molecular, and Structural Biology Department of Microbiology Department of Statistics, Miami University, Oxford, OH, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|DNA Transposable Elements|Gene Library|Genome, Human|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw258', '27307619', 'Transposable elements (TEs) and repetitive DNA make up a sizable fraction of Eukaryotic genomes, and their annotation is crucial to the study of the structure, organization, and evolution of any newly sequenced genome. Although RepeatMasker and nHMMER are useful for identifying these repeats, they require a pre-compiled repeat library-which is not always available. De novo identification tools such as Recon, RepeatScout or RepeatGluer serve to identify TEs purely from sequence content, but are either limited by runtimes that prohibit whole-genome use or degrade in quality in the presence of substitutions that disrupt the sequence patterns.'),
(80051, 2016, 'bio', 'Genome assembly from synthetic long read clouds', 'Kuleshov, Volodymyr|Snyder, Michael P|Batzoglou, Serafim', 'Department of Computer Science, Stanford University 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 Computer Science, Stanford University.', 'United States|United States|United States', 0, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw267', '27307620', 'Despite rapid progress in sequencing technology, assembling de novo the genomes of new species as well as reconstructing complex metagenomes remains major technological challenges. New synthetic long read (SLR) technologies promise significant advances towards these goals, however, their applicability is limited by high sequencing requirements and the inability of current assembly paradigms to cope with combinations of short and long reads.'),
(80052, 2016, 'bio', 'An algorithm for computing the gene tree probability under the multispecies coalescent and its application in the inference of population tree', 'Wu, Yufeng', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.', 'United States', 6, 'Algorithms|Biological Evolution|Genetics, Population|Models, Genetic|Phylogeny|Probability', 'Journal Article', '10.1093/bioinformatics/btw261', '27307621', 'Gene tree represents the evolutionary history of gene lineages that originate from multiple related populations. Under the multispecies coalescent model, lineages may coalesce outside the species (population) boundary. Given a species tree (with branch lengths), the gene tree probability is the probability of observing a specific gene tree topology under the multispecies coalescent model. There are two existing algorithms for computing the exact gene tree probability. The first algorithm is due to Degnan and Salter, where they enumerate all the so-called coalescent histories for the given species tree and the gene tree topology. Their algorithm runs in exponential time in the number of gene lineages in general. The second algorithm is the STELLS algorithm (2012), which is usually faster but also runs in exponential time in almost all the cases.'),
(80053, 2016, 'bio', 'Read-based phasing of related individuals', 'Garg, Shilpa|Martin, Marcel|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany Max Planck Institute for Informatics, Saarbrücken, Germany Saarbrücken Graduate School of Computer Science, Saarland University, Saarbrücken, Germany.|Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-17121 Solna, Sweden.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany Max Planck Institute for Informatics, Saarbrücken, Germany.', 'Germany|Sweden|Germany', 16, 'Algorithms|Genotype|Haplotypes|Pedigree|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw276', '27307622', 'Read-based phasing deduces the haplotypes of an individual from sequencing reads that cover multiple variants, while genetic phasing takes only genotypes as input and applies the rules of Mendelian inheritance to infer haplotypes within a pedigree of individuals. Combining both into an approach that uses these two independent sources of information-reads and pedigree-has the potential to deliver results better than each individually.'),
(80054, 2016, 'bio', 'A convex optimization approach for identification of human tissue-specific interactomes', 'Mohammadi, Shahin|Grama, Ananth', 'Department of Computer Sciences, Purdue University, West Lafayette, IN 47907, USA.|Department of Computer Sciences, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States', 0, 'Humans|Organ Specificity', 'Journal Article', '10.1093/bioinformatics/btw245', '27307623', 'Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.'),
(80055, 2016, 'bio', 'Reconstructing the temporal progression of HIV-1 immune response pathways', 'Jain, Siddhartha|Arrais, Joel|Venkatachari, Narasimhan J|Ayyavoo, Velpandi|Bar-Joseph, Ziv', 'Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computer Science, University of Coimbra, Coimbra, Portugal.|Department of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.|Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|Portugal|United States|United States|United States', 6, 'Disease Progression|Gene Expression Profiling|HIV-1|Humans|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw254', '27307624', 'Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.'),
(80056, 2016, 'bio', 'Finding correct protein-protein docking models using ProQDock', 'Basu, Sankar|Wallner, Björn', 'Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping SE-581 83, Sweden.|Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping SE-581 83, Sweden.', 'Sweden|Sweden', 16, 'Models, Molecular|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw257', '27307625', 'Protein-protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models.'),
(80057, 2016, 'bio', 'An integer programming framework for inferring disease complexes from network data', 'Mazza, Arnon|Klockmeier, Konrad|Wanker, Erich|Sharan, Roded', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Max Delbrück Center for Molecular Medicine, Berlin, Germany.|Max Delbrück Center for Molecular Medicine, Berlin, Germany.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Germany|Germany|Israel', 7, 'Algorithms|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw263', '27307626', 'Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.'),
(80058, 2016, 'bio', 'Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction', 'Tabei, Yasuo|Yamanishi, Yoshihiro|Kotera, Masaaki', 'PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, 332-0012, Japan The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.|Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, Fukuoka, 812-8582, Japan Institute for Advanced Study, Kyushu University, 6-10-1, Hakozaki, Higashi-Ku, Fukuoka, Fukuoka, 812-8581, Japan The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.|School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-Ku, Tokyo, 152-8550, Japan.', 'Japan|Japan|Japan', 5, 'Algorithms|Catalysis|Genomics|Metabolic Networks and Pathways|Metabolome', 'Journal Article', '10.1093/bioinformatics/btw260', '27307627', 'Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes.'),
(80059, 2016, 'bio', 'Fast metabolite identification with Input Output Kernel Regression', 'Brouard, Céline|Shen, Huibin|Dührkop, Kai|dAlché-Buc, Florence|Böcker, Sebastian|Rousu, Juho', 'Department of Computer Science, Aalto University, Espoo, Finland Helsinki Institute for Information Technology, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland Helsinki Institute for Information Technology, Espoo, Finland.|Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany.|LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay, Paris, France.|Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany.|Department of Computer Science, Aalto University, Espoo, Finland Helsinki Institute for Information Technology, Espoo, Finland.', 'Finland|Finland|Germany|France|Germany|Finland', 30, 'Algorithms|Computational Biology|Databases, Chemical|Machine Learning|Metabolomics|Molecular Structure|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw246', '27307628', 'An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space.'),
(80060, 2016, 'bio', 'Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks', 'Intosalmi, Jukka|Nousiainen, Kari|Ahlfors, Helena|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University, Aalto, FI-00076, Finland.|Department of Computer Science, Aalto University, Aalto, FI-00076, Finland.|Lymphocyte Signalling and Development, The Babraham Institute, Cambridgeshire CB22 3AT, UK.|Department of Computer Science, Aalto University, Aalto, FI-00076, Finland.', 'Finland|Finland|United Kingdom|Finland', 4, 'Algorithms|Cell Differentiation|Gene Expression Regulation|Gene Regulatory Networks|Sequence Analysis, RNA|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw274', '27307629', 'Mechanistic models based on ordinary differential equations provide powerful and accurate means to describe the dynamics of molecular machinery which orchestrates gene regulation. When combined with appropriate statistical techniques, mechanistic models can be calibrated using experimental data and, in many cases, also the model structure can be inferred from time-course measurements. However, existing mechanistic models are limited in the sense that they rely on the assumption of static network structure and cannot be applied when transient phenomena affect, or rewire, the network structure. In the context of gene regulatory network inference, network rewiring results from the net impact of possible unobserved transient phenomena such as changes in signaling pathway activities or epigenome, which are generally difficult, but important, to account for.'),
(80061, 2016, 'bio', 'Linear effects models of signaling pathways from combinatorial perturbation data', 'Szczurek, Ewa|Beerenwinkel, Niko', 'Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics.', 'Poland|Switzerland', 4, 'Linear Models|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw268', '27307630', 'Perturbations constitute the central means to study signaling pathways. Interrupting components of the pathway and analyzing observed effects of those interruptions can give insight into unknown connections within the signaling pathway itself, as well as the link from the pathway to the effects. Different pathway components may have different individual contributions to the measured perturbation effects, such as gene expression changes. Those effects will be observed in combination when the pathway components are perturbed. Extant approaches focus either on the reconstruction of pathway structure or on resolving how the pathway components control the downstream effects.'),
(80062, 2016, 'bio', 'ISMB 2016 Proceedings Papers Committee', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw296', '27307631', '-'),
(80063, 2016, 'bio', 'Structure-based prediction of transcription factor binding specificity using an integrative energy function', 'Farrel, Alvin|Murphy, Jonathan|Guo, Jun-Tao', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States|United States', 10, 'Animals|Binding Sites|Computational Biology|DNA-Binding Proteins|Gene Expression Regulation|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw264', '27307632', 'Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and xcfx80 interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals.'),
(80064, 2016, 'bio', 'Ensemble-based evaluation for protein structure models', 'Jamroz, Michal|Kolinski, Andrzej|Kihara, Daisuke', 'Department of Chemistry, University of Warsaw, Warsaw, 02-093, Poland.|Department of Chemistry, University of Warsaw, Warsaw, 02-093, Poland.|Department of Biological Sciences Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.', 'Poland|Poland|United States', 0, 'Magnetic Resonance Spectroscopy|Models, Molecular|Molecular Dynamics Simulation|Protein Conformation|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btw262', '27307633', 'Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein.'),
(80065, 2016, 'bio', 'Faster and more accurate graphical model identification of tandem mass spectra using trellises', 'Wang, Shengjie|Halloran, John T|Bilmes, Jeff A|Noble, William S', 'Department of Computer Science and Engineering.|Department of Electrical Engineering.|Department of Computer Science and Engineering Department of Electrical Engineering.|Department of Computer Science and Engineering Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Bayes Theorem|Databases, Protein|Peptides|Proteins|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw269', '27307634', 'Tandem mass spectrometry (MS/MS) is the dominant high throughput technology for identifying and quantifying proteins in complex biological samples. Analysis of the tens of thousands of fragmentation spectra produced by an MS/MS experiment begins by assigning to each observed spectrum the peptide that is hypothesized to be responsible for generating the spectrum. This assignment is typically done by searching each spectrum against a database of peptides. To our knowledge, all existing MS/MS search engines compute scores individually between a given observed spectrum and each possible candidate peptide from the database. In this work, we use a trellis, a data structure capable of jointly representing a large set of candidate peptides, to avoid redundantly recomputing common sub-computations among different candidates. We show how trellises may be used to significantly speed up existing scoring algorithms, and we theoretically quantify the expected speedup afforded by trellises. Furthermore, we demonstrate that compact trellis representations of whole sets of peptides enables efficient discriminative learning of a dynamic Bayesian network for spectrum identification, leading to greatly improved spectrum identification accuracy.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80066, 2016, 'bio', 'CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction', 'Cui, Xuefeng|Lu, Zhiwu|Wang, Sheng|Jing-Yan Wang, Jim|Gao, Xin', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.|Beijing Key Laboratory of Big Data Management and Analysis Methods, School of Information, Renmin University of China, Beijing 100872, China.|Toyota Technological Institute at Chicago, 6045 Kenwood Avenue, Chicago, IL 60637, USA Department of Human Genetics, University of Chicago, E. 58th St, Chicago, IL 60637, USA.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|China|United States|Saudi Arabia|Saudi Arabia', 16, 'Algorithms|Amino Acid Sequence|Computational Biology|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw271', '27307635', 'Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information.'),
(80067, 2016, 'bio', 'DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences', 'Meng, Fanchi|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada Department of Computer Science, Virginia Commonwealth University, Richmond, 23284, U.S.A.', 'Canada|Canada', 29, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Humans|Protein Structure, Secondary|Proteins|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btw280', '27307636', 'Disordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build high-throughput computational predictors of these regions from protein sequences. To date, there are no computational methods that directly predict DFLs and they can be found only indirectly by filtering predicted flexible residues with predictions of disorder.'),
(80068, 2016, 'bio', 'RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data', 'Orenstein, Yaron|Wang, Yuhao|Berger, Bonnie', 'Computer Science and Artificial Intelligence Laboratory.|Computer Science and Artificial Intelligence Laboratory.|Computer Science and Artificial Intelligence Laboratory Math Department, MIT, Cambridge, MA, USA.', 'United States|United States|United States', 21, 'Algorithms|Protein Binding|Proteins|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw259', '27307637', 'Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset.'),
(80069, 2016, 'bio', 'RNAiFold2T: Constraint Programming design of thermo-IRES switches', 'Garcia-Martin, Juan Antonio|Dotu, Ivan|Fernandez-Chamorro, Javier|Lozano, Gloria|Ramajo, Jorge|Martinez-Salas, Encarnacion|Clote, Peter', 'Biology Department, Boston College, Chestnut Hill, MA 02467, USA.|Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra. Dr. Aiguader 88, Barcelona, Spain.|Centro de Biologia Molecular Severo Ochoa, Consejo Superior de Investigaciones Cientificas-Universidad Autonoma de Madrid, 28049 Madrid, Spain.|Centro de Biologia Molecular Severo Ochoa, Consejo Superior de Investigaciones Cientificas-Universidad Autonoma de Madrid, 28049 Madrid, Spain.|Centro de Biologia Molecular Severo Ochoa, Consejo Superior de Investigaciones Cientificas-Universidad Autonoma de Madrid, 28049 Madrid, Spain.|Centro de Biologia Molecular Severo Ochoa, Consejo Superior de Investigaciones Cientificas-Universidad Autonoma de Madrid, 28049 Madrid, Spain.|Biology Department, Boston College, Chestnut Hill, MA 02467, USA.', 'United States|Spain|Spain|Spain|Spain|Spain|United States', 4, 'Algorithms|Base Sequence|Internal Ribosome Entry Sites|Nucleic Acid Conformation|RNA|RNA Folding|Software', 'Journal Article', '10.1093/bioinformatics/btw265', '27307638', 'RNA thermometers (RNATs) are cis-regulatory elements that change secondary structure upon temperature shift. Often involved in the regulation of heat shock, cold shock and virulence genes, RNATs constitute an interesting potential resource in synthetic biology, where engineered RNATs could prove to be useful tools in biosensors and conditional gene regulation.'),
(80070, 2016, 'bio', 'SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing', 'Tsuchiya, Mariko|Amano, Kojiro|Abe, Masaya|Seki, Misato|Hase, Sumitaka|Sato, Kengo|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama 161-0031, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 1, 'Algorithms|Cluster Analysis|Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|RNA, Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw273', '27307639', 'Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs.'),
(80071, 2016, 'bio', 'Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction', 'He, Dan|Kuhn, David|Parida, Laxmi', 'IBM T.J. Watson Research, Yorktown Heights, NY, USA.|USDA-ARS Subtropical Horticultural Research Station, Miami, FL, USA.|IBM T.J. Watson Research, Yorktown Heights, NY, USA.', 'United States|United States|United States', 21, 'Algorithms|Animals|Genotype|Humans|Linear Models|Machine Learning|Models, Genetic|Phenotype|Plants|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btw249', '27307640', 'Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits.'),
(80072, 2016, 'bio', 'A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data', 'Cui, Xiaodong|Meng, Jia|Zhang, Shaowu|Chen, Yidong|Huang, Yufei', 'Department of Electrical and Computer Engineering, University of Texas at San Antonio, TX 78249, USA.|Department of Biological Science, Xian Jiaotong-Liverpool University, Suzhou 215123, China.|College of Automation, Northwestern Polytechnical University, Xian 710072, China.|Greehey Childrens Cancer Research Institute Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, TX 78229, USA.|Department of Electrical and Computer Engineering, University of Texas at San Antonio, TX 78249, USA Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, TX 78229, USA.', 'United States|China|China|United States|United States', 30, 'Algorithms|Base Sequence|Methylation|Models, Biological|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw281', '27307641', 'N(6)-methyl-adenosine (m(6)A) is the most prevalent mRNA methylation but precise prediction of its mRNA location is important for understanding its function. A recent sequencing technology, known as Methylated RNA Immunoprecipitation Sequencing technology (MeRIP-seq), has been developed for transcriptome-wide profiling of m(6)A. We previously developed a peak calling algorithm called exomePeak. However, exomePeak over-simplifies data characteristics and ignores the reads variances among replicates or reads dependency across a site region. To further improve the performance, new model is needed to address these important issues of MeRIP-seq data.'),
(80073, 2016, 'bio', 'PHOCOS: inferring multi-feature phenotypic crosstalk networks', 'Deng, Yue|Altschuler, Steven J|Wu, Lani F', 'Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.|Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.|Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA 94158, USA.', 'United States|United States|United States', 0, 'Biomarkers|Humans|Machine Learning|Microscopy|Neutrophils|Phenotype|Receptor Cross-Talk|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw251', '27307643', 'Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a single phenotypic feature (e.g. abundance) of a biomarker. These approaches are insufficient for analyzing perturbation data that can contain information about multiple features (e.g. abundance, activity or localization) of each biomarker.'),
(80074, 2016, 'bio', 'Classifying and segmenting microscopy images with deep multiple instance learning', 'Kraus, Oren Z|Ba, Jimmy Lei|Frey, Brendan J', 'Department of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 2E4, Canada The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 2E4, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 2E4, Canada The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, Canada.', 'Canada|Canada|Canada', 143, 'Algorithms|Humans|Image Interpretation, Computer-Assisted|Machine Learning|Microscopy|Neural Networks (Computer)|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw252', '27307644', 'High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations.'),
(80075, 2016, 'bio', 'BioASF: a framework for automatically generating executable pathway models specified in BioPAX', 'Haydarlou, Reza|Jacobsen, Annika|Bonzanni, Nicola|Feenstra, K Anton|Abeln, Sanne|Heringa, Jaap', 'Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands NKI-AVL, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Computer Simulation|Gene Regulatory Networks|Humans|Models, Biological|Programming Languages|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btw250', '27307645', 'Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular, this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model.'),
(80076, 2016, 'bio', 'DeepMeSH: deep semantic representation for improving large-scale MeSH indexing', 'Peng, Shengwen|You, Ronghui|Wang, Hongning|Zhai, Chengxiang|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.|Department of Computer Science, University of Virginia, Charlottesville 22904-4740, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA.|Bioinformatics Center, Kyoto University, Institute for Chemical Research, Uji 611-0011, Japan Department of Computer Science, Aalto University, Finland.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China Centre for Computational System Biology, Fudan University, Shanghai 200433, China.', 'China|China|United States|United States|Finland|China', 0, 'Abstracting and Indexing as Topic|Data Mining|MEDLINE|Medical Subject Headings|National Library of Medicine (U.S.)|Semantics|Software|United States', 'Journal Article', '10.1093/bioinformatics/btw294', '27307646', 'Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.'),
(80077, 2016, 'bio', 'What time is it? Deep learning approaches for circadian rhythms', 'Agostinelli, Forest|Ceglia, Nicholas|Shahbaba, Babak|Sassone-Corsi, Paolo|Baldi, Pierre', 'Department of Computer Science.|Department of Computer Science.|Department of Statistics.|Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.|Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.', 'United States|United States|United States|United States|United States', 23, 'Animals|Circadian Clocks|Circadian Rhythm|Computational Biology|Machine Learning|Mice|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw243', '27307647', 'Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken.'),
(80078, 2016, 'bio', 'Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells', 'Schissler, A Grant|Li, Qike|Chen, James L|Kenost, Colleen|Achour, Ikbel|Billheimer, D Dean|Li, Haiquan|Piegorsch, Walter W|Lussier, Yves A', 'Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Division of Bioinformatics, Departments of Biomedical Informatics Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Graduate Interdisciplinary Program in Statistics BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.|Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA The University of Arizona Cancer Center, Tucson, AZ 85719, USA Institute for Genomics and Systems Biology, The University of Chicago, Chicago, IL, 60637, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Drug Resistance, Neoplasm|Gene Expression Profiling|Humans|Male|Neoplastic Cells, Circulating|Prostatic Neoplasms|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw248', '27307648', 'As omics biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.'),
(80079, 2016, 'bio', 'Jumping across biomedical contexts using compressive data fusion', 'Zitnik, Marinka|Zupan, Blaz', 'Department of Computer Science, Stanford University, CA 94305, USA Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia 1000.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia 1000 Department of Molecular and Human Genetics, Baylor College of Medicine, TX 77030, USA.', 'Slovenia|Slovenia', 0, 'Algorithms|Computational Biology|Data Compression|Disease|Gene Ontology|Humans|Semantics', 'Journal Article', '10.1093/bioinformatics/btw247', '27307649', 'The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects-such as a gene and a disease-can be related in different ways, for example, directly via gene-disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling.'),
(80080, 2016, 'bio', 'ARGON: fast, whole-genome simulation of the discrete time Wright-fisher process', 'Palamara, Pier Francesco', 'Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.', 'United States', 0, 'Animals|Computer Simulation|Gene Conversion|Genome|Humans|Models, Theoretical|Mutation', 'Journal Article', '10.1093/bioinformatics/btw355', '27312410', 'Simulation under the coalescent model is ubiquitous in the analysis of genetic data. The rapid growth of real data sets from multiple human populations led to increasing interest in simulating very large sample sizes at whole-chromosome scales. When the sample size is large, the coalescent model becomes an increasingly inaccurate approximation of the discrete time Wright-Fisher model (DTWF). Analytical and computational treatment of the DTWF, however, is generally harder.'),
(80081, 2016, 'bio', 'MultiQC: summarize analysis results for multiple tools and samples in a single report', 'Ewels, Philip|Magnusson, Måns|Lundin, Sverker|Käller, Max', 'Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden.|Department of Molecular Medicine and Surgery, Science for Life Laboratory, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.|Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden.|Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 554, 'Computational Biology|High-Throughput Nucleotide Sequencing|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btw354', '27312411', 'Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone, batch effects and outlier samples can easily be missed in the early stages of analysis.'),
(80082, 2016, 'bio', 'RADIS: analysis of RAD-seq data for interspecific phylogeny', 'Cruaud, Astrid|Gautier, Mathieu|Rossi, Jean-Pierre|Rasplus, Jean-Yves|Gouzy, Jérôme', 'INRA, UMR1062 CBGP, F-34988 Montferrier-sur-Lez, France.|INRA, UMR1062 CBGP, F-34988 Montferrier-sur-Lez, France, IBC, F-34095 Montpellier, France.|INRA, UMR1062 CBGP, F-34988 Montferrier-sur-Lez, France.|INRA, UMR1062 CBGP, F-34988 Montferrier-sur-Lez, France.|INRA, UMR441 LIPM, F31326 Castanet Tolosan, France CNRS, UMR2594 LIPM, F31326 Castanet Tolosan, France.', 'France|France|France|France|France', 7, 'Phylogeny|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw352', '27312412', 'In an attempt to make the processing of RAD-seq data easier and allow rapid and automated exploration of parameters/data for phylogenetic inference, we introduce the perl pipeline RADIS Users of RADIS can let their raw Illumina data be processed up to phylogenetic tree inference, or stop (and restart) the process at some point. Different values for key parameters can be explored in a single analysis (e.g. loci building, sample/loci selection), making possible a thorough exploration of data. RADIS relies on Stacks for demultiplexing of data, removing PCR duplicates and building individual and catalog loci. Scripts have been specifically written for trimming of reads and loci/sample selection. Finally, RAxML is used for phylogenetic inferences, though other software may be utilized.'),
(80083, 2016, 'bio', '@MInter: automated text-mining of microbial interactions', 'Lim, Kun Ming Kenneth|Li, Chenhao|Chng, Kern Rei|Nagarajan, Niranjan', 'Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Computational Biology Program, Faculty of Science.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Department of Computer Science, National University of Singapore, Singapore, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Department of Computer Science, National University of Singapore, Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 17, 'Data Mining|Electronic Data Processing|Microbial Interactions|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw357', '27312413', 'Microbial consortia are frequently defined by numerous interactions within the community that are key to understanding their function. While microbial interactions have been extensively studied experimentally, information regarding them is dispersed in the scientific literature. As manual collation is an infeasible option, automated data processing tools are needed to make this information easily accessible.'),
(80084, 2016, 'bio', 'Pseudotime estimation: deconfounding single cell time series', 'Reid, John E|Wernisch, Lorenz', 'MRC Biostatistics Unit, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom', 0, 'Bayes Theorem|Cell Line|Gene Expression|Humans|Male|Models, Statistical|Prostatic Neoplasms|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btw372', '27318198', 'Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression.'),
(80085, 2016, 'bio', 'TADtool: visual parameter identification for TAD-calling algorithms', 'Kruse, Kai|Hug, Clemens B|Hernández-Rodríguez, Benjamín|Vaquerizas, Juan M', 'Max Planck Institute for Molecular Biomedicine, Münster 48149, Germany.|Max Planck Institute for Molecular Biomedicine, Münster 48149, Germany.|Max Planck Institute for Molecular Biomedicine, Münster 48149, Germany.|Max Planck Institute for Molecular Biomedicine, Münster 48149, Germany.', 'Germany|Germany|Germany|Germany', 21, 'Algorithms|Animals|Genome|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw368', '27318199', 'Eukaryotic genomes are hierarchically organized into topologically associating domains (TADs). The computational identification of these domains and their associated properties critically depends on the choice of suitable parameters of TAD-calling algorithms. To reduce the element of trial-and-error in parameter selection, we have developed TADtool: an interactive plot to find robust TAD-calling parameters with immediate visual feedback. TADtool allows the direct export of TADs called with a chosen set of parameters for two of the most common TAD calling algorithms: directionality and insulation index. It can be used as an intuitive, standalone application or as a Python package for maximum flexibility.'),
(80086, 2016, 'bio', 'Genome puzzle master (GPM): an integrated pipeline for building and editing pseudomolecules from fragmented sequences', 'Zhang, Jianwei|Kudrna, Dave|Mu, Ting|Li, Weiming|Copetti, Dario|Yu, Yeisoo|Goicoechea, Jose Luis|Lei, Yang|Wing, Rod A', 'National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.|Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA International Rice Research Institute, Genetic Resource Center, Los Baños, Laguna, Philippines.|Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.|Arizona Genomics Institute and BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA International Rice Research Institute, Genetic Resource Center, Los Baños, Laguna, Philippines.', 'China|United States|China|China|Philippines|United States|United States|China|Philippines', 8, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw370', '27318200', 'Next generation sequencing technologies have revolutionized our ability to rapidly and affordably generate vast quantities of sequence data. Once generated, raw sequences are assembled into contigs or scaffolds. However, these assemblies are mostly fragmented and inaccurate at the whole genome scale, largely due to the inability to integrate additional informative datasets (e.g. physical, optical and genetic maps). To address this problem, we developed a semi-automated software tool-Genome Puzzle Master (GPM)-that enables the integration of additional genomic signposts to edit and build new-gen-assemblies that result in high-quality annotation-ready pseudomolecules.'),
(80087, 2016, 'bio', 'PhenoScanner: a database of human genotype-phenotype associations', 'Staley, James R|Blackshaw, James|Kamat, Mihir A|Ellis, Steve|Surendran, Praveen|Sun, Benjamin B|Paul, Dirk S|Freitag, Daniel|Burgess, Stephen|Danesh, John|Young, Robin|Butterworth, Adam S', 'Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK NIHR Blood and Transplant Research Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK Robertson Centre for Biostatistics, University of Glasgow, Glasgow G12 8QQ, UK.|Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK NIHR Blood and Transplant Research Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Genetic Association Studies|Genetic Variation|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw373', '27318201', 'PhenoScanner is a curated database of publicly available results from large-scale genetic association studies. This tool aims to facilitate phenome scans, the cross-referencing of genetic variants with many phenotypes, to help aid understanding of disease pathways and biology. The database currently contains over 350 million association results and over 10 million unique genetic variants, mostly single nucleotide polymorphisms. It is accompanied by a web-based tool that queries the database for associations with user-specified variants, providing results according to the same effect and non-effect alleles for each input variant. The tool provides the option of searching for trait associations with proxies of the input variants, calculated using the European samples from 1000 Genomes and Hapmap.'),
(80088, 2016, 'bio', 'AgIn: measuring the landscape of CpG methylation of individual repetitive elements', 'Suzuki, Yuta|Korlach, Jonas|Turner, Stephen W|Tsukahara, Tatsuya|Taniguchi, Junko|Qu, Wei|Ichikawa, Kazuki|Yoshimura, Jun|Yurino, Hideaki|Takahashi, Yuji|Mitsui, Jun|Ishiura, Hiroyuki|Tsuji, Shoji|Takeda, Hiroyuki|Morishita, Shinichi', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Pacific Biosciences, Menlo Park, CA 94025, USA.|Pacific Biosciences, Menlo Park, CA 94025, USA.|Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.|Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan.|Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan.|Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan.|Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan.|Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8583, Japan.', 'Japan|United States|United States|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|CpG Islands|DNA Methylation|Genome, Human|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw360', '27318202', 'Determining the methylation state of regions with high copy numbers is challenging for second-generation sequencing, because the read length is insufficient to map reads uniquely, especially when repetitive regions are long and nearly identical to each other. Single-molecule real-time (SMRT) sequencing is a promising method for observing such regions, because it is not vulnerable to GC bias, it produces long read lengths, and its kinetic information is sensitive to DNA modifications.'),
(80089, 2016, 'bio', 'The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants', 'Al-Numair, Nouf S|Lopes, Luis|Syrris, Petros|Monserrat, Lorenzo|Elliott, Perry|Martin, Andrew C R', 'Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.|Institute of Cardiovascular Science, UCL, London, UK.|Institute of Cardiovascular Science, UCL, London, UK.|Complejo Hospitalario Universitario de A Coruxc3xb1a, Insituto de Investigacixc3xb3n Biomxc3xa9dica, Coruxc3xb1a, Spain.|Institute of Cardiovascular Science, UCL, London, UK.|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|Spain|United Kingdom|United Kingdom', 2, 'Cardiomyopathies|Cluster Analysis|Humans|Mutation|Myosin Heavy Chains|Ventricular Myosins', 'Journal Article', '10.1093/bioinformatics/btw362', '27318203', 'High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7).'),
(80090, 2016, 'bio', 'Assemblytics: a web analytics tool for the detection of variants from an assembly', 'Nattestad, Maria|Schatz, Michael C', 'Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA and.|Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA and Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States', 66, 'Genetic Variation|Genome|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw369', '27318204', 'Assemblytics is a web app for detecting and analyzing variants from a de novo genome assembly aligned to a reference genome. It incorporates a unique anchor filtering approach to increase robustness to repetitive elements, and identifies six classes of variants based on their distinct alignment signatures. Assemblytics can be applied both to comparing aberrant genomes, such as human cancers, to a reference, or to identify differences between related species. Multiple interactive visualizations enable in-depth explorations of the genomic distributions of variants.'),
(80091, 2016, 'bio', 'NegGOA: negative GO annotations selection using ontology structure', 'Fu, Guangyuan|Wang, Jun|Yang, Bo|Yu, Guoxian', 'College of Computer and Information Science, Southwest University, Chongqing 400715, China.|College of Computer and Information Science, Southwest University, Chongqing 400715, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.|College of Computer and Information Science, Southwest University, Chongqing 400715, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.', 'China|China|China|China', 18, 'Algorithms|Animals|Computational Biology|Gene Ontology|Humans|Mice|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btw366', '27318205', 'Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space.'),
(80092, 2016, 'bio', 'STRUM: structure-based prediction of protein stability changes upon single-point mutation', 'Quan, Lijun|Lv, Qiang|Zhang, Yang', 'School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.|School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu, China.|Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA Department of Biological Chemistry, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.', 'China|China|United States', 0, 'Algorithms|Humans|Models, Molecular|Point Mutation|Protein Stability|Proteins|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btw361', '27318206', 'Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability, but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling.'),
(80093, 2016, 'bio', 'SELAM: simulation of epistasis and local adaptation during admixture with mate choice', 'Corbett-Detig, Russell|Jones, Matt', 'Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA.|Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA.', 'United States|United States', 4, 'Animals|Epistasis, Genetic|Humans|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw365', '27318207', 'SELAM is a forward time population genetic simulation program that provides a flexible framework for simulating admixture between any number of ancestral populations. The program can be used to simulate complex demographic and selection models, including dioecious or monoecious populations, autosomal or sex chromosomes, local adaptation, dominance, epistasis, and mate choice.'),
(80094, 2016, 'bio', 'Statistical modeling of isoform splicing dynamics from RNA-seq time series data', 'Huang, Yuanhua|Sanguinetti, Guido', 'School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.|School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh EH9 3BF, UK.', 'United Kingdom|United Kingdom', 5, 'Animals|Gene Expression Profiling|Humans|Models, Statistical|Protein Isoforms|RNA|RNA Splicing|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw364', '27318208', 'Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80095, 2016, 'bio', 'New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins', 'Fabris, Fabio|Freitas, Alex A', 'School of Computing, University of Kent, CT2 7NF Canterbury, Kent, UK.|School of Computing, University of Kent, CT2 7NF Canterbury, Kent, UK.', 'United Kingdom|United Kingdom', 0, 'Aging|Algorithms|Animals|Genome|Mice|Phenotype|Proteins', 'Journal Article', '10.1093/bioinformatics/btw363', '27318209', 'The incidence of ageing-related diseases has been constantly increasing in the last decades, raising the need for creating effective methods to analyze ageing-related protein data. These methods should have high predictive accuracy and be easily interpretable by ageing experts. To enable this, one needs interpretable classification models (supervised machine learning) and features with rich biological meaning. In this paper we propose two interpretable feature types based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compare them with traditional feature types in hierarchical classification (a more challenging classification task regarding predictive performance) and binary classification (a classification task producing easier to interpret classification models). As far as we know, this work is the first to: (i) explore the potential of the KEGG pathway data in the hierarchical classification setting, (i) use the graph structure of KEGG pathways to create a feature type that quantifies the influence of a current protein on another specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the classification models induced using KEGG features.'),
(80096, 2016, 'bio', 'compendiumdb: an R package for retrieval and storage of functional genomics data', 'Nandal, Umesh K|van Kampen, Antoine H C|Moerland, Perry D', 'Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Biosystems Data Analysis Group, University of Amsterdam, Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Computational Biology|Gene Expression|Genome, Human|Genomics|Humans|Software|User-Computer Interface|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw335', '27323392', 'Currently, the Gene Expression Omnibus (GEO) contains public data of over 1 million samples from more than 40 000 microarray-based functional genomics experiments. This provides a rich source of information for novel biological discoveries. However, unlocking this potential often requires retrieving and storing a large number of expression profiles from a wide range of different studies and platforms. The compendiumdb R package provides an environment for downloading functional genomics data from GEO, parsing the information into a local or remote database and interacting with the database using dedicated R functions, thus enabling seamless integration with other tools available in R/Bioconductor.'),
(80097, 2016, 'bio', 'A simple model predicts UGT-mediated metabolism', 'Dang, Na Le|Hughes, Tyler B|Krishnamurthy, Varun|Swamidass, S Joshua', 'Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660xe2x80x89S. Euclid Ave, St. Louis, MO 63110, USA.|Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660xe2x80x89S. Euclid Ave, St. Louis, MO 63110, USA.|Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660xe2x80x89S. Euclid Ave, St. Louis, MO 63110, USA.|Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660xe2x80x89S. Euclid Ave, St. Louis, MO 63110, USA.', 'United States|United States|United States|United States', 0, 'Drug Interactions|Glucuronosyltransferase|Humans|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btw350', '27324196', 'Uridine diphosphate glucunosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. This paper describes a computational method for predicting sites of UGT-mediated metabolism on drug-like molecules.'),
(80098, 2016, 'bio', 'Integrated gene set analysis for microRNA studies', 'Garcia-Garcia, Francisco|Panadero, Joaquin|Dopazo, Joaquin|Montaner, David', 'Computational Genomics Department, Centro de Investigacion Principe Felipe (CIPF), Valencia, Spain.|Genometra S.L., Valencia, Spain.|Computational Genomics Department, Centro de Investigacion Principe Felipe (CIPF), Valencia, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain Functional Genomics Node, (INB) at CIPF, Valencia, Spain.|Computational Genomics Department, Centro de Investigacion Principe Felipe (CIPF), Valencia, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Computational Biology|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Neoplasms|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btw334', '27324197', 'Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.'),
(80099, 2016, 'bio', 'RTCR: a pipeline for complete and accurate recovery of T cell repertoires from high throughput sequencing data', 'Gerritsen, Bram|Pandit, Aridaman|Andeweg, Arno C|de Boer, Rob J', 'Theoretical Biology and Bioinformatics, Utrecht University, 3584CH the Netherlands.|Theoretical Biology and Bioinformatics, Utrecht University, 3584CH the Netherlands.|Department of Viroscience, Rotterdam, Erasmus MC, 3000CA, the Netherlands.|Theoretical Biology and Bioinformatics, Utrecht University, 3584CH the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|Receptors, Antigen, T-Cell|T-Lymphocytes', 'Journal Article', '10.1093/bioinformatics/btw339', '27324198', 'High Throughput Sequencing (HTS) has enabled researchers to probe the human T cell receptor (TCR) repertoire, which consists of many rare sequences. Distinguishing between true but rare TCR sequences and variants generated by polymerase chain reaction (PCR) and sequencing errors remains a formidable challenge. The conventional approach to handle errors is to remove low quality reads, and/or rare TCR sequences. Such filtering discards a large number of true and often rare TCR sequences. However, accurate identification and quantification of rare TCR sequences is essential for repertoire diversity estimation.'),
(80100, 2016, 'bio', 'ACE: adaptive cluster expansion for maximum entropy graphical model inference', 'Barton, J P|De Leonardis, E|Coucke, A|Cocco, S', 'Departments of Chemical Engineering and Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, Cambridge, MA 02139, USA.|Laboratoire de Physique Statistique de LEcole Normale Supérieure, CNRS, Ecole Normale Supérieure & Université P.&M. Curie, Paris, France Computational and Quantitative Biology, UPMC, UMR 7238, Sorbonne Université, Paris, France.|Computational and Quantitative Biology, UPMC, UMR 7238, Sorbonne Université, Paris, France Laboratoire de Physique Théorique de LEcole Normale Supérieure, CNRS, Ecole Normale Supérieure & Université P.&M. Curie, Paris, France.|Laboratoire de Physique Statistique de LEcole Normale Supérieure, CNRS, Ecole Normale Supérieure & Université P.&M. Curie, Paris, France.', 'United States|France|France|France', 34, 'Algorithms|Entropy|Likelihood Functions|Models, Theoretical|Normal Distribution', 'Journal Article', '10.1093/bioinformatics/btw328', '27329863', 'Graphical models are often employed to interpret patterns of correlations observed in data through a network of interactions between the variables. Recently, Ising/Potts models, also known as Markov random fields, have been productively applied to diverse problems in biology, including the prediction of structural contacts from protein sequence data and the description of neural activity patterns. However, inference of such models is a challenging computational problem that cannot be solved exactly. Here, we describe the adaptive cluster expansion (ACE) method to quickly and accurately infer Ising or Potts models based on correlation data. ACE avoids overfitting by constructing a sparse network of interactions sufficient to reproduce the observed correlation data within the statistical error expected due to finite sampling. When convergence of the ACE algorithm is slow, we combine it with a Boltzmann Machine Learning algorithm (BML). We illustrate this method on a variety of biological and artificial datasets and compare it to state-of-the-art approximate methods such as Gaussian and pseudo-likelihood inference.'),
(80101, 2016, 'bio', 'CAGEd-oPOSSUM: motif enrichment analysis from CAGE-derived TSSs', 'Arenillas, David J|Forrest, Alistair R R|Kawaji, Hideya|Lassmann, Timo|FANTOM Consortium|Wasserman, Wyeth W|Mathelier, Anthony', 'Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC V5Z 4H4, Canada.|Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan.|Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan.|Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan Telethon Kids Institute, The University of Western Australia, Subiaco, WA 6008, Australia.|Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan Telethon Kids Institute, The University of Western Australia, Subiaco, WA 6008, Australia.|Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC V5Z 4H4, Canada.|Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC V5Z 4H4, Canada Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway.', 'Canada|Japan|Japan|Australia|Australia|Canada|Canada', 5, 'Binding Sites|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Nucleotide Motifs|Regulatory Sequences, Nucleic Acid|Software|Transcription Factors|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btw337', '27334471', 'With the emergence of large-scale Cap Analysis of Gene Expression (CAGE) datasets from individual labs and the FANTOM consortium, one can now analyze the cis-regulatory regions associated with gene transcription at an unprecedented level of refinement. By coupling transcription factor binding site (TFBS) enrichment analysis with CAGE-derived genomic regions, CAGEd-oPOSSUM can identify TFs that act as key regulators of genes involved in specific mammalian cell and tissue types. The webtool allows for the analysis of CAGE-derived transcription start sites (TSSs) either provided by the user or selected from xe2x88xbc1300 mammalian samples from the FANTOM5 project with pre-computed TFBS predicted with JASPAR TF binding profiles. The tool helps power insights into the regulation of genes through the study of the specific usage of TSSs within specific cell types and/or under specific conditions.'),
(80102, 2016, 'bio', 'Revealing aperiodic aspects of solenoid proteins from sequence information', 'Hrabe, Thomas|Jaroszewski, Lukasz|Godzik, Adam', 'Department of Bioinformatics and Systems Biology, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.|Department of Bioinformatics and Systems Biology, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.|Department of Bioinformatics and Systems Biology, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Proteins|Repetitive Sequences, Amino Acid|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btw319', '27334472', 'Repeat proteins, which contain multiple repeats of short sequence motifs, form a large but seldom-studied group of proteins. Methods focusing on the analysis of 3D structures of such proteins identified many subtle effects in length distribution of individual motifs that are important for their functions. However, similar analysis was yet not applied to the vast majority of repeat proteins with unknown 3D structures, mostly because of the extreme diversity of the underlying motifs and the resulting difficulty to detect those.'),
(80103, 2016, 'bio', 'iPTM-mLys: identifying multiple lysine PTM sites and their different types', 'Qiu, Wang-Ren|Sun, Bi-Qian|Xiao, Xuan|Xu, Zhao-Chun|Chou, Kuo-Chen', 'Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China, Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia MO, USA, Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China, Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China.|Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.', 'China|China|China|China|China', 0, 'Algorithms|Amino Acids|Animals|Humans|Lysine|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1093/bioinformatics/btw380', '27334473', 'Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as acetylation, crotonylation, methylation and succinylation. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none?'),
(80104, 2016, 'bio', 'Vecuum: identification and filtration of false somatic variants caused by recombinant vector contamination', 'Kim, Junho|Maeng, Ju Heon|Lim, Jae Seok|Son, Hyeonju|Lee, Junehawk|Lee, Jeong Ho|Kim, Sangwoo', 'Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, South Korea.|Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, South Korea.|Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, South Korea.|Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, South Korea.|Department of Convergence Technology Research, Korea Institute of Science and Technology Information, Daejeon 34141, South Korea.|Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, South Korea.|Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, South Korea.', '-|-|-|-|-|-|-', 4, 'Alleles|Genetic Vectors|High-Throughput Nucleotide Sequencing|Mutation|Recombination, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btw383', '27334474', 'Advances in sequencing technologies have remarkably lowered the detection limit of somatic variants to a low frequency. However, calling mutations at this range is still confounded by many factors including environmental contamination. Vector contamination is a continuously occurring issue and is especially problematic since vector inserts are hardly distinguishable from the sample sequences. Such inserts, which may harbor polymorphisms and engineered functional mutations, can result in calling false variants at corresponding sites. Numerous vector-screening methods have been developed, but none could handle contamination from inserts because they are focusing on vector backbone sequences alone.'),
(80105, 2016, 'bio', 'PEP_scaffolder: using (homologous) proteins to scaffold genomes', 'Zhu, Bai-Han|Song, Ying-Nan|Xue, Wei|Xu, Gui-Cai|Xiao, Jun|Sun, Ming-Yuan|Sun, Xiao-Wen|Li, Jiong-Tang', 'College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China College of Marine Science, Zhejiang Ocean University, Zhoushan 316022, China.|College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.|Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing 100141, China.', 'China|China|China|China|China|China|China|China', 5, 'Algorithms|Animals|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Homology|Software', 'Journal Article', '10.1093/bioinformatics/btw378', '27334475', 'Recovering the gene structures is one of the important goals of genome assembly. In low-quality assemblies, and even some high-quality assemblies, certain gene regions are still incomplete, thus, novel scaffolding approaches are required to complete gene regions.'),
(80106, 2016, 'bio', 'SERAPHIM: studying environmental rasters and phylogenetically informed movements', 'Dellicour, Simon|Rose, Rebecca|Faria, Nuno R|Lemey, Philippe|Pybus, Oliver G', 'Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven-University of Leuven, Minderbroedersstaat 10, Leuven 3000, Belgium.|BioInfoExperts LLC, Norfolk, Virginia, USA, 23518.|Department of Zoology, University of Oxford, Oxford OX1 3PS, UK.|Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven-University of Leuven, Minderbroedersstaat 10, Leuven 3000, Belgium.|Department of Zoology, University of Oxford, Oxford OX1 3PS, UK.', 'Belgium|United Kingdom|United Kingdom|Belgium|United Kingdom', 0, 'Computational Biology|Environment|Phylogeny|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btw384', '27334476', 'SERAPHIM (Studying Environmental Rasters and PHylogenetically Informed Movements) is a suite of computational methods developed to study phylogenetic reconstructions of spatial movement in an environmental context. SERAPHIM extracts the spatio-temporal information contained in estimated phylogenetic trees and uses this information to calculate summary statistics of spatial spread and to visualize dispersal history. Most importantly, SERAPHIM enables users to study the impact of customized environmental variables on the spread of the study organism. Specifically, given an environmental raster, SERAPHIM computes environmental weights for each phylogeny branch, which represent the degree to which the environmental variable impedes (or facilitates) lineage movement. Correlations between movement duration and these environmental weights are then assessed, and the statistical significances of these correlations are evaluated using null distributions generated by a randomization procedure. SERAPHIM can be applied to any phylogeny whose nodes are annotated with spatial and temporal information. At present, such phylogenies are most often found in the field of emerging infectious diseases, but will become increasingly common in other biological disciplines as population genomic data grows.'),
(80107, 2016, 'bio', 'GORpipe: a query tool for working with sequence data based on a Genomic Ordered Relational (GOR) architecture', 'Guðbjartsson, Hákon|Georgsson, Guðmundur Fr|Guðjónsson, Sigurjón A|Valdimarsson, Ragnar Þór|Sigurðsson, Jóhann H|Stefánsson, Sigmar K|Másson, Gísli|Magnússon, Gísli|Pálmason, Vilmundur|Stefánsson, Kári', 'WuXiNextCode Genomics, Sturlugata 8, 101 Reykjavík, Iceland.|deCODE Genetics, Sturlugata 8, 101 Reykjavík, Iceland.|deCODE Genetics, Sturlugata 8, 101 Reykjavík, Iceland.|deCODE Genetics, Sturlugata 8, 101 Reykjavík, Iceland.|WuXiNextCode Genomics, Sturlugata 8, 101 Reykjavík, Iceland.|WuXiNextCode Genomics, Sturlugata 8, 101 Reykjavík, Iceland.|deCODE Genetics, Sturlugata 8, 101 Reykjavík, Iceland.|WuXiNextCode Genomics, Sturlugata 8, 101 Reykjavík, Iceland.|WuXiNextCode Genomics, Sturlugata 8, 101 Reykjavík, Iceland.|deCODE Genetics, Sturlugata 8, 101 Reykjavík, Iceland.', 'Iceland|Iceland|Iceland|Iceland|Iceland|Iceland|Iceland|Iceland|Iceland|Iceland', 0, 'Genome|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw199', '27339714', 'Our aim was to create a general-purpose relational data format and analysis tools to provide an efficient and coherent framework for working with large volumes of DNA sequence data.'),
(80108, 2016, 'bio', 'Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes', 'Hsu, Jacob Shujui|Kwan, Johnny S H|Pan, Zhicheng|Garcia-Barcelo, Maria-Mercxc3xa8|Sham, Pak Chung|Li, Miaoxin', 'Department of Psychiatry.|Department of Psychiatry.|Department of Psychiatry.|Department of Surgery.|Department of Psychiatry Centre for Genomics Science, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.|Department of Psychiatry Centre for Genomics Science, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.', '-|-|-|-|Hong Kong|Hong Kong', 5, 'Area Under Curve|Databases, Genetic|Genes, Dominant|Genes, Recessive|Genetic Variation|Humans|Mutation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw381', '27354691', 'Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information.'),
(80109, 2016, 'bio', 'Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization', 'Wen, Ping-Ping|Shi, Shao-Ping|Xu, Hao-Dong|Wang, Li-Na|Qiu, Jian-Ding', 'Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China.|Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China.|Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China.|Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China.|Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China Department of Materials and Chemical Engineering, Pingxiang University, Pingxiang 337055, China.', 'China|China|China|China|China', 36, 'Animals|Computer Simulation|Humans|Lysine|Methylation|Protein Processing, Post-Translational|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btw377', '27354692', 'As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation.'),
(80110, 2016, 'bio', 'A probabilistic approach for collective similarity-based drug-drug interaction prediction', 'Sridhar, Dhanya|Fakhraei, Shobeir|Getoor, Lise', 'Computer Science Department, University of California Santa Cruz, Santa Cruz, CA 95050, USA.|Computer Science Department, University of California Santa Cruz, Santa Cruz, CA 95050, USA Computer Science Department, University of Maryland, College Park, MD 20740, USA.|Computer Science Department, University of California Santa Cruz, Santa Cruz, CA 95050, USA.', 'United States|United States|United States', 0, 'Drug Interactions|Humans|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw342', '27354693', 'As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions (DDIs) can guide in vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their associated targets, such methods must effectively fuse multiple sources of information and perform inference over the network of drugs.'),
(80111, 2016, 'bio', 'Evaluating the molecule-based prediction of clinical drug responses in cancer', 'Ding, Zijian|Zu, Songpeng|Gu, Jin', 'MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.', 'China|China|China', 27, 'Antineoplastic Agents, Alkylating|Computational Biology|Dose-Response Relationship, Drug|Genomics|Humans|MicroRNAs|Neoplasms|Predictive Value of Tests|RNA, Messenger|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btw344', '27354694', 'Molecule-based prediction of drug response is one major task of precision oncology. Recently, large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), provide the opportunity to evaluate the predictive utility of molecular data for clinical drug responses in multiple cancer types.'),
(80112, 2016, 'bio', 'ABAEnrichment: an R package to test for gene set expression enrichment in the adult and developing human brain', 'Grote, Steffi|Prxc3xbcfer, Kay|Kelso, Janet|Dannemann, Michael', 'Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany Medical Faculty, University of Leipzig, Leipzig 04103, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Adult|Brain|Gene Expression|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw392', '27354695', 'We present ABAEnrichment, an R package that tests for expression enrichment in specific brain regions at different developmental stages using expression information gathered from multiple regions of the adult and developing human brain, together with ontologically organized structural information about the brain, both provided by the Allen Brain Atlas. We validate ABAEnrichment by successfully recovering the origin of gene sets identified in specific brain cell-types and developmental stages.'),
(80113, 2016, 'bio', 'pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC', 'Jia, Jianhua|Zhang, Liuxia|Liu, Zi|Xiao, Xuan|Chou, Kuo-Chen', 'Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China Gordon Life Science Institute, Boston, MA 02478, USA.|Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.|Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China Gordon Life Science Institute, Boston, MA 02478, USA.|Gordon Life Science Institute, Boston, MA 02478, USA Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.', 'China|China|China|China|China', 144, 'Algorithms|Amino Acids|Proteins|Sequence Analysis, Protein|Software|Sumoylation', 'Journal Article', '10.1093/bioinformatics/btw387', '27354696', 'Sumoylation is a post-translational modification (PTM) process, in which small ubiquitin-related modifier (SUMO) is attaching by covalent bonds to substrate protein. It is critical to many different biological processes such as replicating genome, expressing gene, localizing and stabilizing proteins, unfortunately, it is also involved with many major disorders including Alzheimers and Parkinsons diseases. Therefore, for both basic research and drug development, it is important to identify the sumoylation sites in proteins.'),
(80114, 2016, 'bio', 'Cyclo-lib: a database of computational molecular dynamics simulations of cyclodextrins', 'Mixcoha, Edgar|Rosende, Roberto|Garcia-Fandino, Rebeca|Pixc3xb1eiro, xc3x81ngel', 'Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain CONACYT - Instituto Nacional de Psiquiatrxc3xada Ramxc3xb3n de la Fuente Muxc3xb1iz, 14370 Tlalpan, Distrito Federal, Mexico.|Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.|Centro Singular de Investigacixc3xb3n en Quxc3xadmica Biolxc3xb3xica e Materiais Moleculares (CIQUS) and departamento de Quxc3xadmica Orgxc3xa1nica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.|Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.', 'Spain|Spain|Spain|Spain', 4, 'Cyclodextrins|Databases, Factual|Hydrophobic and Hydrophilic Interactions|Molecular Dynamics Simulation|Water', 'Journal Article', '10.1093/bioinformatics/btw289', '27354697', 'Cyclodextrins (CDs) are amongst the most versatile/multi-functional molecules used in molecular research and chemical applications. They are natural cyclic oligosaccharides typically employed to encapsulate hydrophobic groups in their central cavity. This allows solubilizing, protecting or reducing the toxicity of a large variety of different molecules including drugs, dyes and surfactant agents. In spite of their great potential, atomic level information of these molecules, which is key for their function, is really scarce. Computational Molecular Dynamics (MD) simulations have the potential to efficiently fill this gap, providing structural-dynamic information at atomic level in time scales ranging from ps to xcexbcs.'),
(80115, 2016, 'bio', 'Structural distinctions of fast and slow bacterial luciferases revealed by phylogenetic analysis', 'Deeva, Anna A|Temlyakova, Evgenia A|Sorokin, Anatoly A|Nemtseva, Elena V|Kratasyuk, Valentina A', 'Laboratory of Bioluminescent Biotechnologies, Siberian Federal University, Krasnoyarsk, Russian Federation.|Mechanism of Cell Genome Functioning Laboratory, Institute of Cell Biophysics RAS, Moscow Region, Pushchino, Russian Federation.|Mechanism of Cell Genome Functioning Laboratory, Institute of Cell Biophysics RAS, Moscow Region, Pushchino, Russian Federation Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation.|Laboratory of Bioluminescent Biotechnologies, Siberian Federal University, Krasnoyarsk, Russian Federation Laboratory of Photobiology, Institute of Biophysics SB RAS, Krasnoyarsk, Russian Federation.|Laboratory of Bioluminescent Biotechnologies, Siberian Federal University, Krasnoyarsk, Russian Federation Laboratory of Photobiology, Institute of Biophysics SB RAS, Krasnoyarsk, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russian Federation', 7, 'Kinetics|Luciferases, Bacterial|Models, Molecular|Photobacterium|Phylogeny|Vibrio', 'Journal Article', '10.1093/bioinformatics/btw386', '27354698', 'Bacterial luciferases are heterodimeric enzymes that catalyze a chemical reaction, so called bioluminescence, which causes light emission in bacteria. Bioluminescence is vastly used as a reporter system in research tools and commercial developments. However, the details of the mechanisms that stabilize and transform the reaction intermediates as well as differences in the enzymatic kinetics amongst different bacterial luciferases remain to be elucidated.'),
(80116, 2016, 'bio', 'Conpair: concordance and contamination estimator for matched tumor-normal pairs', 'Bergmann, Ewa A|Chen, Bo-Juen|Arora, Kanika|Vacic, Vladimir|Zody, Michael C', 'New York Genome Center, New York, NY 10013, USA.|New York Genome Center, New York, NY 10013, USA.|New York Genome Center, New York, NY 10013, USA.|New York Genome Center, New York, NY 10013, USA.|New York Genome Center, New York, NY 10013, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|DNA, Neoplasm|High-Throughput Nucleotide Sequencing|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw389', '27354699', 'Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a solution, we developed Conpair, a tool for detection of sample swaps and cross-individual contamination in whole-genome and whole-exome tumor-normal sequencing experiments.'),
(80117, 2016, 'bio', 'GeneCodeq: quality score compression and improved genotyping using a Bayesian framework', 'Greenfield, Daniel L|Stegle, Oliver|Rrustemi, Alban', 'PetaGene, Ideaspace, 3 Charles Babbage Rd, Cambridge CB3 0GT, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SQ, UK.|PetaGene, Ideaspace, 3 Charles Babbage Rd, Cambridge CB3 0GT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 13, 'Bayes Theorem|Data Compression|Genotype|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1093/bioinformatics/btw385', '27354700', 'The exponential reduction in cost of genome sequencing has resulted in a rapid growth of genomic data. Most of the entropy of short read data lies not in the sequence of read bases themselves but in their Quality Scores-the confidence measurement that each base has been sequenced correctly. Lossless compression methods are now close to their theoretical limits and hence there is a need for lossy methods that further reduce the complexity of these data without impacting downstream analyses.'),
(80118, 2016, 'bio', 'CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets', 'Dao, David|Fraser, Adam N|Hung, Jane|Ljosa, Vebjorn|Singh, Shantanu|Carpenter, Anne E', 'Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA Department of Informatics, Technical University of Munich, Munich, Bavaria 80333, Germany.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.', 'Germany|United States|United States|United States|United States|United States', 43, 'Animals|Datasets as Topic|Humans|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btw390', '27354701', 'CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).'),
(80119, 2016, 'bio', 'AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms', 'Ravindranath, Pradeep Anand|Sanner, Michel F', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.', 'United States|United States', 17, 'Binding Sites|Hydrogen Bonding|Ligands|Models, Molecular|Protein Binding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw367', '27354702', 'The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms.'),
(80120, 2016, 'bio', 'Local versus global biological network alignment', 'Meng, Lei|Striegel, Aaron|Milenkovixc4x87, Tijana', 'Department of Computer Science and Engineering ECK Institute of Global Health and Interdisciplinary Center for Network Science and Applications.|Department of Computer Science and Engineering Wireless Institute, University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Computer Science and Engineering ECK Institute of Global Health and Interdisciplinary Center for Network Science and Applications.', 'United States|United States|United States', 26, 'Algorithms|Proteins|Sequence Alignment|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btw348', '27357169', 'Network alignment (NA) aims to find regions of similarities between species molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories.'),
(80121, 2016, 'bio', 'Network-based pathway enrichment analysis with incomplete network information', 'Ma, Jing|Shojaie, Ali|Michailidis, George', 'Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, PA 19104, USA.|Department of Biostatistics, University of Washington, Seattle, WA 98915, USA.|Department of Statistics, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States', 23, 'Cell Communication|Computational Biology|Databases, Factual|Protein Interaction Maps', 'Journal Article', '10.1093/bioinformatics/btw410', '27357170', 'Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific.'),
(80122, 2016, 'bio', 'Estimating and testing high-dimensional mediation effects in epigenetic studies', 'Zhang, Haixiang|Zheng, Yinan|Zhang, Zhou|Gao, Tao|Joyce, Brian|Yoon, Grace|Zhang, Wei|Schwartz, Joel|Just, Allan|Colicino, Elena|Vokonas, Pantel|Zhao, Lihui|Lv, Jinchi|Baccarelli, Andrea|Hou, Lifang|Liu, Lei', 'Center for Applied Mathematics, Tianjin University, Tianjin 300072, China.|Department of Preventive Medicine.|Department of Preventive Medicine.|Department of Preventive Medicine.|Department of Preventive Medicine.|Department of Statistics, Northwestern University, Chicago, IL 60611, USA.|Department of Preventive Medicine.|Department of Environmental Health, Harvard University, Boston, MA 02115, USA.|Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Environmental Health, Harvard University, Boston, MA 02115, USA.|Veterans Affairs Boston Healthcare System and Boston University School of Medicine, VA Normative Aging Study, Boston, MA 02118, USA.|Department of Preventive Medicine.|Data Sciences and Operations Department, University of Southern California, Los Angeles, CA 90089, USA.|Department of Environmental Health, Harvard University, Boston, MA 02115, USA.|Department of Preventive Medicine.|Department of Preventive Medicine.', 'China|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 27, 'Aging|DNA Methylation|Epigenomics|Humans|Monte Carlo Method|Smoking', 'Journal Article', '10.1093/bioinformatics/btw351', '27357171', 'High-dimensional DNA methylation markers may mediate pathways linking environmental exposures with health outcomes. However, there is a lack of analytical methods to identify significant mediators for high-dimensional mediation analysis.'),
(80123, 2016, 'bio', 'Accounting for pairwise distance restraints in FFT-based protein-protein docking', 'Xia, Bing|Vajda, Sandor|Kozakov, Dima', 'Department of Biomedical Engineering.|Department of Biomedical Engineering Department of Chemistry, Boston University, Boston, MA 02215, USA.|Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.', 'United States|United States|United States', 13, 'Animals|Fourier Analysis|Humans|Molecular Docking Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw306', '27357172', 'ClusPro is a heavily used protein-protein docking server based on the fast Fourier transform (FFT) correlation approach. While FFT enables global docking, accounting for pairwise distance restraints using penalty terms in the scoring function is computationally expensive. We use a different approach and directly select low energy solutions that also satisfy the given restraints. As expected, accounting for restraints generally improves the rank of near native predictions, while retaining or even improving the numerical efficiency of FFT based docking.'),
(80124, 2016, 'bio', '4DGenome: a comprehensive database of chromatin interactions', 'Teng, Li|He, Bing|Wang, Jiahui|Tan, Kai', 'Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.|Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.|Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.|Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw375', '27371723', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80125, 2016, 'bio', 'IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics', 'Rosikiewicz, Marta|Robinson-Rechavi, Marc', 'Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.|Department of Applied Mathematics and Statistics Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btw374', '27373894', '-'),
(80126, 2016, 'bio', 'AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology', 'Balsa-Canto, Eva|Henriques, David|Gábor, Attila|Banga, Julio R', 'Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain.|Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain.|Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain.|Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain.', 'Spain|Spain|Spain|Spain', 52, 'Algorithms|Animals|Humans|Metabolic Engineering|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw411', '27378288', 'Many problems of interest in dynamic modeling and control of biological systems can be posed as non-linear optimization problems subject to algebraic and dynamic constraints. In the context of modeling, this is the case of, e.g. parameter estimation, optimal experimental design and dynamic flux balance analysis. In the context of control, model-based metabolic engineering or drug dose optimization problems can be formulated as (multi-objective) optimal control problems. Finding a solution to those problems is a very challenging task which requires advanced numerical methods.'),
(80127, 2016, 'bio', 'w4CSeq: software and web application to analyze 4C-seq data', 'Cai, Mingyang|Gao, Fan|Lu, Wange|Wang, Kai', 'Department of Preventive Medicine Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research Zilkha Neurogenetic Institute.|Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research Zilkha Neurogenetic Institute.|Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research Department of Stem Cell Biology and Regenerative Medicine.|Zilkha Neurogenetic Institute Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.', 'United States|United States|United States|United States', 7, 'Animals|Chromosomes|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw408', '27378289', 'Circularized Chromosome Conformation Capture followed by deep sequencing (4C-Seq) is a powerful technique to identify genome-wide partners interacting with a pre-specified genomic locus. Here, we present a computational and statistical approach to analyze 4C-Seq data generated from both enzyme digestion and sonication fragmentation-based methods. We implemented a command line software tool and a web interface called w4CSeq, which takes in the raw 4C sequencing data (FASTQ files) as input, performs automated statistical analysis and presents results in a user-friendly manner. Besides providing users with the list of candidate interacting sites/regions, w4CSeq generates figures showing genome-wide distribution of interacting regions, and sketches the enrichment of key features such as TSSs, TTSs, CpG sites and DNA replication timing around 4C sites.'),
(80128, 2016, 'bio', 'TwoPhaseInd: an R package for estimating gene-treatment interactions and discovering predictive markers in randomized clinical trials', 'Wang, Xiaoyu|Dai, James Y', 'Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Biostatistics, University of Washington, Seattle, WA, USA.', 'United States|United States', 1, 'Biomarkers|Genome|Humans|Logistic Models|Randomized Controlled Trials as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btw391', '27378290', 'In randomized clinical trials, identifying baseline genetic or genomic markers for predicting subgroup treatment effects is of rising interest. Outcome-dependent sampling is often employed for measuring markers. The R package TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup treatment effects and gene-treatment interactions, exploiting the gene-treatment independence dictated by randomization, including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model. For rare failure events subject to censoring, we have proposed efficient augmented case-only designs, a variation of the case-cohort design, to estimate genetic associations and subgroup treatment effects in a Cox regression model. The R package is computationally scalable to genome-wide studies, as illustrated by an example from Womens Health Initiative.'),
(80129, 2016, 'bio', 'SwiSpot: modeling riboswitches by spotting out switching sequences', 'Barsacchi, Marco|Novoa, Eva Maria|Kellis, Manolis|Bechini, Alessio', 'Department of Information Engineering, University of Pisa, Largo L. Lazzarino, Pisa, IT 56122, Italy.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142 USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142 USA.|Department of Information Engineering, University of Pisa, Largo L. Lazzarino, Pisa, IT 56122, Italy.', 'Italy|United States|United States|Italy', 0, 'Bacteria|Ligands|Models, Genetic|Riboswitch|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw401', '27378291', 'Riboswitches are cis-regulatory elements in mRNA, mostly found in Bacteria, which exhibit two main secondary structure conformations. Although one of them prevents the gene from being expressed, the other conformation allows its expression, and this switching process is typically driven by the presence of a specific ligand. Although there are a handful of known riboswitches, our knowledge in this field has been greatly limited due to our inability to identify their alternate structures from their sequences. Indeed, current methods are not able to predict the presence of the two functionally distinct conformations just from the knowledge of the plain RNA nucleotide sequence. Whether this would be possible, for which cases, and what prediction accuracy can be achieved, are currently open questions.'),
(80130, 2016, 'bio', 'ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes', 'Weinmaier, Thomas|Platzer, Alexander|Frank, Jeroen|Hellinger, Hans-Jörg|Tischler, Patrick|Rattei, Thomas', 'Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.|Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.|Department of Microbiology, Radboud University Nijmegen, Nijmegen, The Netherlands.|Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.|Department of Genome Oriented Bioinformatics, Technische Universität München, Freising, Germany.|Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria Department of Genome Oriented Bioinformatics, Technische Universität München, Freising, Germany.', 'Austria|Austria|Netherlands|Austria|Germany|Austria', 3, 'Algorithms|Genome|Prokaryotic Cells|Software', 'Journal Article', '10.1093/bioinformatics/btw393', '27378292', 'The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs.'),
(80131, 2016, 'bio', 'MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins', 'Kara, Altan|Vickers, Martin|Swain, Martin|Whitworth, David E|Fernandez-Fuentes, Narcis', 'Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Prokaryotic Cells|Proteins|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw403', '27378293', 'Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood.'),
(80132, 2016, 'bio', 'ChAsE: chromatin analysis and exploration tool', 'Younesy, Hamid|Nielsen, Cydney B|Lorincz, Matthew C|Jones, Steven J M|Karimi, Mohammad M|Möller, Torsten', 'Graphics Usability and Visualization Lab, Simon Fraser University, Burnaby, Canada Canadas Michael Smith Genome Sciences Centre, Vancouver, Canada Biomedical Research Centre, University of British Columbia, Vancouver, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, Canada Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada.|Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, Canada Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.|Biomedical Research Centre, University of British Columbia, Vancouver, Canada Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada.|Graphics Usability and Visualization Lab, Simon Fraser University, Burnaby, Canada Visualization and Data Analysis Lab, Faculty of Computer Science, University of Vienna, Wien, Austria.', 'Canada|Canada|Canada|Canada|Canada|Austria', 15, 'Animals|Chromatin|Cluster Analysis|Humans|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw382', '27378294', ': We present ChAsE, a cross-platform desktop application developed for interactive visualization, exploration and clustering of epigenomic data such as ChIP-seq experiments. ChAsE is designed and developed in close collaboration with several groups of biologists and bioinformaticians with a focus on usability and interactivity. Data can be analyzed through k-means clustering, specifying presence or absence of signal in epigenetic data and performing set operations between clusters. Results can be explored in an interactive heat map and profile plot interface and exported for downstream analysis or as high quality figures suitable for publications.'),
(80133, 2016, 'bio', 'Unbiased classification of spatial strategies in the Barnes maze', 'Illouz, Tomer|Madar, Ravit|Clague, Charlotte|Griffioen, Kathleen J|Louzoun, Yoram|Okun, Eitan', 'The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.|The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel The Mina and Everard Goodman Faculty of Life Sciences.|The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.|Department of Biology and Chemistry, Liberty University, Lynchburg, VA 24515, USA.|The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel Department of Mathematics, Bar Ilan University, Ramat Gan 5290002, Israel.|The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel The Mina and Everard Goodman Faculty of Life Sciences.', 'Israel|Israel|Israel|United States|Israel|Israel', 14, 'Algorithms|Animals|Maze Learning|Memory|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw376', '27378295', 'Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze.'),
(80134, 2016, 'bio', 'Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees', 'Yamada, Kazunori D|Tomii, Kentaro|Katoh, Kazutaka', 'Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.', 'Japan|Japan|Japan', 113, 'Algorithms|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw412', '27378296', 'Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones.'),
(80135, 2016, 'bio', 'The SMAL web server: global multiple network alignment from pairwise alignments', 'Dohrmann, Jakob|Singh, Rahul', 'Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA.|Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA Center for Discovery and Innovation in Parasitic Diseases, University of California, San Diego, CA 92093, USA.', 'United States|United States', 2, 'Algorithms|Computational Biology|Protein Interaction Maps|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw402', '27378297', 'Alignments of protein-protein interaction networks (PPIN) can be used to predict protein function, study conserved aspects of the interactome, and to establish evolutionary correspondences. Within this problem context, determining multiple network alignments (MNA) is a significant challenge that involves high computational complexity. A limited number of public MNA implementations are available currently and the majority of the pairwise network alignment (PNA) algorithms do not have MNA counterparts. Furthermore, current MNA algorithms do not allow choosing a specific PPIN relative to which an MNA could be constructed. Also, once an MNA is obtained, it cannot easily be modified, such as through addition of a new network, without expensive re-computation of the entire MNA.'),
(80136, 2016, 'bio', 'Performance of protein-structure predictions with the physics-based UNRES force field in CASP11', 'Krupa, Pawexc5x82|Mozolewska, Magdalena A|Wixc5x9bniewska, Marta|Yin, Yanping|He, Yi|Sieradzan, Adam K|Ganzynkowicz, Robert|Lipska, Agnieszka G|Karczyxc5x84ska, Agnieszka|xc5x9alusarz, Magdalena|xc5x9alusarz, Rafaxc5x82|Giexc5x82doxc5x84, Artur|Czaplewski, Cezary|Jagiexc5x82a, Dawid|Zaborowski, Bartxc5x82omiej|Scheraga, Harold A|Liwo, Adam', 'Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.|Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.|Faculty of Chemistry, University of Gdaxc5x84sk, Wita Stwosza 63, Gdaxc5x84sk 80-308, Poland.', 'Poland|Poland|Poland|United States|United States|Poland|Poland|Poland|Poland|Poland|Poland|Poland|Poland|Poland|Poland|United States|Pol', 27, 'Animals|Humans|Models, Molecular|Protein Conformation|Protein Structure, Secondary|Proteins|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btw404', '27378298', 'Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the Cxcexb1RMSD was 3.8 xc3x85 for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes.'),
(80137, 2016, 'bio', 'Icarus: visualizer for de novo assembly evaluation', 'Mikheenko, Alla|Valin, Gleb|Prjibelski, Andrey|Saveliev, Vladislav|Gurevich, Alexey', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia, 199034.|Department of Mathematics and Information Technology, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia, 194021.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia, 199034.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia, 199034.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia, 199034.', '-|-|-|-|-', 25, 'Genome|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw379', '27378299', ': Data visualization plays an increasingly important role in NGS data analysis. With advances in both sequencing and computational technologies, it has become a new bottleneck in genomics studies. Indeed, evaluation of de novo genome assemblies is one of the areas that can benefit from the visualization. However, even though multiple quality assessment methods are now available, existing visualization tools are hardly suitable for this purpose. Here, we present Icarus-a novel genome visualizer for accurate assessment and analysis of genomic draft assemblies, which is based on the tool QUAST. Icarus can be used in studies where a related reference genome is available, as well as for non-model organisms. The tool is available online and as a standalone application.'),
(80138, 2016, 'bio', 'An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data', 'Huang, Gongyi|Wang, Shaoli|Wang, Xueqin|You, Na', 'School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.|School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics, Shanghai 200433, China.|School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China South China Center for Statistical Science, Sun Yat-sen University, Guangzhou 510275, China Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.|School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China South China Center for Statistical Science, Sun Yat-sen University, Guangzhou 510275, China.', 'China|China|China|China', 0, 'Animals|Bayes Theorem|Genotype|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw409', '27378300', 'The development of next generation sequencing technology provides an efficient and powerful approach to rare variant detection. To identify genetic variations, the essential question is how to quantity the sequencing error rate in the data. Because of the advantage of easy implementation and the ability to integrate data from different sources, the empirical Bayes method is popularly employed to estimate the sequencing error rate for SNP detection.'),
(80139, 2016, 'bio', 'Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals', 'Hu, Xiuzhen|Dong, Qiwen|Yang, Jianyi|Zhang, Yang', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA Institute for Data Science and Engineering, East China Normal University, Shanghai 200062, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA School of Mathematical Sciences, Nankai University, Tianjin 300071, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA.', 'China|China|China|United States', 0, 'Algorithms|Binding Sites|Databases, Protein|Ligands|Metals|Models, Molecular|Proteins', 'Journal Article', '10.1093/bioinformatics/btw396', '27378301', 'More than half of proteins require binding of metal and acid radical ions for their structure and function. Identification of the ion-binding locations is important for understanding the biological functions of proteins. Due to the small size and high versatility of the metal and acid radical ions, however, computational prediction of their binding sites remains difficult.'),
(80140, 2016, 'bio', 'Heat*seq: an interactive web tool for high-throughput sequencing experiment comparison with public data', 'Devailly, Guillaume|Mantsoki, Anna|Joshi, Anagha', 'Department of Developmental Biology, The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.|Department of Developmental Biology, The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.|Department of Developmental Biology, The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 5, 'Animals|Chromatin Immunoprecipitation|Drosophila|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw407', '27378302', 'Better protocols and decreasing costs have made high-throughput sequencing experiments now accessible even to small experimental laboratories. However, comparing one or few experiments generated by an individual lab to the vast amount of relevant data freely available in the public domain might be limited due to lack of bioinformatics expertise. Though several tools, including genome browsers, allow such comparison at a single gene level, they do not provide a genome-wide view. We developed Heat*seq, a web-tool that allows genome scale comparison of high throughput experiments chromatin immuno-precipitation followed by sequencing, RNA-sequencing and Cap Analysis of Gene Expression) provided by a user, to the data in the public domain. Heat*seq currently contains over 12 000 experiments across diverse tissues and cell types in human, mouse and drosophila. Heat*seq displays interactive correlation heatmaps, with an ability to dynamically subset datasets to contextualize user experiments. High quality figures and tables are produced and can be downloaded in multiple formats.'),
(80141, 2016, 'bio', 'deBGA: read alignment with de Bruijn graph-based seed and extension', 'Liu, Bo|Guo, Hongzhe|Brudno, Michael|Wang, Yadong', 'Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Department of Computer Science, University of Toronto, ON M5S 3G4, Canada Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 1L7, Canada Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|Canada|China', 21, 'Algorithms|Animals|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw371', '27378303', 'As high-throughput sequencing (HTS) technology becomes ubiquitous and the volume of data continues to rise, HTS read alignment is becoming increasingly rate-limiting, which keeps pressing the development of novel read alignment approaches. Moreover, promising novel applications of HTS technology require aligning reads to multiple genomes instead of a single reference, however, it is still not viable for the state-of-the-art aligners to align large numbers of reads to multiple genomes.'),
(80142, 2016, 'bio', 'MetaCycle: an integrated R package to evaluate periodicity in large scale data', 'Wu, Gang|Anafi, Ron C|Hughes, Michael E|Kornacker, Karl|Hogenesch, John B', 'Department of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and Therapeutics.|Division of Sleep Medicine Center for Sleep and Circadian Neurobiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.|Department of Biology, University of Missouri-St. Louis, St. Louis, MO 63121, USA.|Emeritus Professor of Sensory Biophysics, the Ohio State University, Columbus, OH 43210, USA.|Department of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and Therapeutics.', 'United States|United States|United States|United States|United States', 92, 'Algorithms|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw405', '27378304', 'Detecting periodicity in large scale data remains a challenge. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here, we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to conveniently evaluate periodicity in time-series data. MetaCycle has two functions, meta2d and meta3d, designed to analyze two-dimensional and three-dimensional time-series datasets, respectively. Meta2d implements N-version programming concepts using a suite of algorithms and integrating their results.'),
(80143, 2016, 'bio', 'SIFORM: shared informative factor models for integration of multi-platform bioinformatic data', 'An, Xuebei|Hu, Jianhua|Do, Kim-Anh', 'Department of Biostatistics, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States', 1, 'Computational Biology|Models, Theoretical|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btw295', '27381342', 'High-dimensional omic data derived from different technological platforms have been extensively used to facilitate comprehensive understanding of disease mechanisms and to determine personalized health treatments. Numerous studies have integrated multi-platform omic data, however, few have efficiently and simultaneously addressed the problems that arise from high dimensionality and complex correlations.'),
(80144, 2016, 'bio', 'samExploreR: exploring reproducibility and robustness of RNA-seq results based on SAM files', 'Stupnikov, Alexey|Tripathi, Shailesh|de Matos Simoes, Ricardo|McArt, Darragh|Salto-Tellez, Manuel|Glazko, Galina|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, Faculty of Medicine Health and Life Sciences, Queens University Belfast, BT9 7AE Belfast, UK.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queens University Belfast, BT9 7AE Belfast, UK School of Mathematics and Physics, Queens University Belfast, BT7 1NN Belfast, UK.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queens University Belfast, BT9 7AE Belfast, UK.|Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queens University Belfast, BT9 7AE Belfast, UK.|Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queens University Belfast, BT9 7AE Belfast, UK.|Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Department of Biomedical Computer Science and Mechatronics, UMIT, Hall in Tirol, Austria College of Computer and Control Engineering, Nankai University, Tianjin, P.R. China.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queens University Belfast, BT9 7AE Belfast, UK Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland Institute of Biosciences and Medical Technology, 33720 Tampere, Finland.', 'United Kingdom|United Kingdom|United Kingdom|Ireland|Ireland|United Kingdom|Austria|Finland', 3, 'RNA|Reproducibility of Results|Research Design|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw475', '27402900', 'Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to the two counter acting goals of (i) a cost efficient and (ii) an optimal experimental design leading to a compromise, e.g. in the sequencing depth of experiments.'),
(80145, 2016, 'bio', 'A subpopulation model to analyze heterogeneous cell differentiation dynamics', 'Chan, Yat Hin|Intosalmi, Jukka|Rautio, Sini|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University, 00076 Aalto, Finland.|Department of Computer Science, Aalto University, 00076 Aalto, Finland.|Department of Computer Science, Aalto University, 00076 Aalto, Finland.|Department of Computer Science, Aalto University, 00076 Aalto, Finland Turku Centre for Biotechnology, University of Turku and Åbo Akademi, 20521 Turku, Finland.', 'Finland|Finland|Finland|Finland', 2, 'Cell Differentiation|Humans|Models, Theoretical|Sequence Analysis, RNA|Th17 Cells', 'Journal Article', '10.1093/bioinformatics/btw395', '27402901', 'Cell differentiation is steered by extracellular signals that activate a cell type specific transcriptional program. Molecular mechanisms that drive the differentiation can be analyzed by combining mathematical modeling with population average data. For standard mathematical models, the population average data is informative only if the measurements come from a homogeneous cell culture. In practice, however, the differentiation efficiencies are always imperfect. Consequently, cell cultures are inherently mixtures of several cell types, which have different molecular mechanisms and exhibit quantitatively different dynamics. There is an urgent need for data-driven mathematical modeling approaches that can detect possible heterogeneity and, further, recover the molecular mechanisms from heterogeneous data.'),
(80146, 2016, 'bio', 'New quality measure for SNP array based CNV detection', 'Macé, A|Tuke, M A|Beckmann, J S|Lin, L|Jacquemont, S|Weedon, M N|Reymond, A|Kutalik, Z', 'Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland Department of Computational Biology, University of Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Division of Cardiology, Geneva University Hospital, Geneva, Switzerland.|Service de Génétique Médicale, Centre Universitaire Hospitalier Vaudois, Lausanne, Switzerland.|Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.|Center for Integrative Genomics, University for Lausanne, Lausanne, Switzerland.|Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|United Kingdom|Switzerland|Switzerland|Switzerland|United Kingdom|Switzerland|Switzerland', 7, 'DNA Copy Number Variations|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw477', '27402902', 'Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software.'),
(80147, 2016, 'bio', 'MIMEAnTo: profiling functional RNA in mutational interference mapping experiments', 'Smith, Maureen R|Smyth, Redmond P|Marquet, Roland|von Kleist, Max', 'Systems Pharmacology & Disease Control, Department of Mathematics and Computer Science, Freie Universitxc3xa4t Berlin, Berlin, Germany.|Architecture et Rxc3xa9activitxc3xa9 de lARN, Institut de Biologie Molxc3xa9culaire et Cellulaire du Centre National de la Recherche Scientifique, Universitxc3xa9 de Strasbourg, Strasbourg, France.|Architecture et Rxc3xa9activitxc3xa9 de lARN, Institut de Biologie Molxc3xa9culaire et Cellulaire du Centre National de la Recherche Scientifique, Universitxc3xa9 de Strasbourg, Strasbourg, France.|Systems Pharmacology & Disease Control, Department of Mathematics and Computer Science, Freie Universitxc3xa4t Berlin, Berlin, Germany.', 'Germany|France|France|Germany', 2, 'Animals|High-Throughput Nucleotide Sequencing|Humans|Mutation|Programming Languages|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw479', '27402903', 'The mutational interference mapping experiment (MIME) is a powerful method that, coupled to a bioinformatics analysis pipeline, allows the identification of domains and structures in RNA that are important for its function. In MIME, target RNAs are randomly mutated, selected by function, physically separated and sequenced using next-generation sequencing (NGS). Quantitative effects of each mutation at each position in the RNA can be recovered with statistical certainty using the herein developed user-friendly, cross-platform software MIMEAnTo (MIME Analysis Tool).'),
(80148, 2016, 'bio', 'Towards the knowledge-based design of universal influenza epitope ensemble vaccines', 'Sheikh, Qamar M|Gatherer, Derek|Reche, Pedro A|Flower, Darren R', 'School of Life & Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK.|Division of Biomedical & Life Sciences, Faculty of Health & Medicine, Lancaster University, Lancaster LA1 4YW, UK.|Facultad de Medicina, Departamento de Microbiologia I, Universidad Complutense de Madrid, Madrid, Spain.|School of Life & Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK.', 'United Kingdom|United Kingdom|Spain|United Kingdom', 25, 'CD4-Positive T-Lymphocytes|Computer Simulation|Epitopes, T-Lymphocyte|Humans|Immunogenetics|Influenza A virus|Influenza Vaccines|Influenza, Human', 'Journal Article', '10.1093/bioinformatics/btw399', '27402904', 'Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes.'),
(80149, 2016, 'bio', 'PyPanda: a Python package for gene regulatory network reconstruction', 'van IJzendoorn, David G P|Glass, Kimberly|Quackenbush, John|Kuijjer, Marieke L', 'Department of Pathology, Leiden University Medical Center, 2300RC Leiden, The Netherlands.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.', 'Netherlands|United States|United States|United States', 3, 'Animals|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btw422', '27402905', 'PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of omics data. PANDA was originally coded in Cxe2x80x82++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the Cxe2x80x82++xe2x80x82version and includes additional features for network analysis.'),
(80150, 2016, 'bio', 'CRISPR-DO for genome-wide CRISPR design and optimization', 'Ma, Jian|Köster, Johannes|Qin, Qian|Hu, Shengen|Li, Wei|Chen, Chenhao|Cao, Qingyi|Wang, Jinzeng|Mei, Shenglin|Liu, Qi|Xu, Han|Liu, Xiaole Shirley', 'School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|School of Life Science and Technology, Tongji University, Shanghai 200092, China Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.', 'China|United States|China|China|United States|United States|China|China|China|China|United States|China', 20, 'Animals|Clustered Regularly Interspaced Short Palindromic Repeats|Computational Biology|DNA|Exons|Gene Editing|Genome|Humans|Mice|RNA, Guide', 'Journal Article', '10.1093/bioinformatics/btw476', '27402906', 'Despite the growing popularity in using CRISPR/Cas9 technology for genome editing and gene knockout, its performance still relies on well-designed single guide RNAs (sgRNA). In this study, we propose a web application for the Design and Optimization (CRISPR-DO) of guide sequences that target both coding and non-coding regions in spCas9 CRISPR system across human, mouse, zebrafish, fly and worm genomes. CRISPR-DO uses a computational sequence model to predict sgRNA efficiency, and employs a specificity scoring function to evaluate the potential of off-target effect. It also provides information on functional conservation of target sequences, as well as the overlaps with exons, putative regulatory sequences and single-nucleotide polymorphisms (SNPs). The web application has a user-friendly genome-browser interface to facilitate the selection of the best target DNA sequences for experimental design.'),
(80151, 2016, 'bio', 'BioNetGen 22: advances in rule-based modeling', 'Harris, Leonard A|Hogg, Justin S|Tapia, José-Juan|Sekar, John A P|Gupta, Sanjana|Korsunsky, Ilya|Arora, Arshi|Barua, Dipak|Sheehan, Robert P|Faeder, James R', 'Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 65, 'Biochemistry|Humans|Models, Theoretical|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw469', '27402907', ': BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80152, 2016, 'bio', 'SYNBADm: a tool for optimization-based automated design of synthetic gene circuits', 'Otero-Muras, Irene|Henriques, David|Banga, Julio R', 'BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain.|BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain.|BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208, Vigo, Spain.', 'Spain|Spain|Spain', 16, 'Gene Regulatory Networks|Genes, Synthetic|Software|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/btw415', '27402908', 'The design of de novo circuits with predefined performance specifications is a challenging problem in Synthetic Biology. Computational models and tools have proved to be crucial for a successful wet lab implementation. Natural gene circuits are complex, subject to evolutionary tradeoffs and playing multiple roles. However, most synthetic designs implemented to date are simple and perform a single task. As the field progresses, advanced computational tools are needed in order to handle greater levels of circuit complexity in a more flexible way and considering multiple design criteria.'),
(80153, 2016, 'bio', 'MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ', 'Legland, David|Arganda-Carreras, Ignacio|Andrey, Philippe', 'UR1268 Biopolymers, Interactions and Assemblies, INRA, Nantes, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, Cedex, RD10, 78026, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, Cedex, RD10, 78026, France.', 'France|France|France', 203, 'Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Software', 'Journal Article', '10.1093/bioinformatics/btw413', '27412086', 'Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images.'),
(80154, 2016, 'bio', 'sBWT: memory efficient implementation of the hardware-acceleration-friendly Schindler transform for the fast biological sequence mapping', 'Chang, Chia-Hua|Chou, Min-Te|Wu, Yi-Chung|Hong, Ting-Wei|Li, Yun-Lung|Yang, Chia-Hsiang|Hung, Jui-Hung', 'Institute of Bioinformatics and Systems Biology.|Institute of Bioinformatics and Systems Biology.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.|Institute of Bioinformatics and Systems Biology.|Institute of Bioinformatics and Systems Biology.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.|Institute of Bioinformatics and Systems Biology.', '-|-|-|-|-|-|-', 5, 'Abstracting and Indexing as Topic|Algorithms|Genome|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw419', '27412087', 'The Full-text index in Minute space (FM-index) derived from the Burrows-Wheeler transform (BWT) is broadly used for fast string matching in large genomes or a huge set of sequencing reads. Several graphic processing unit (GPU) accelerated aligners based on the FM-index have been proposed recently, however, the construction of the index is still handled by central processing unit (CPU), only parallelized in data level (e.g. by performing blockwise suffix sorting in GPU), or not scalable for large genomes.'),
(80155, 2016, 'bio', 'cisASE: a likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data', 'Liu, Zhi|Gui, Tuantuan|Wang, Zhen|Li, Hong|Fu, Yunhe|Dong, Xiao|Li, Yixue', 'Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China School of Life Science and Technology, Shanghai Jiaotong University, Shanghai 200240, China Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai 201203, China and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200438, China.', 'China|China|China|China|China|United States|China', 3, 'Alleles|DNA Copy Number Variations|Humans|Likelihood Functions|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw416', '27412088', 'Allele-specific expression (ASE) is a useful way to identify cis-acting regulatory variation, which provides opportunities to develop new therapeutic strategies that activate beneficial alleles or silence mutated alleles at specific loci. However, multiple problems hinder the identification of ASE in next-generation sequencing (NGS) data.'),
(80156, 2016, 'bio', 'MEMHDX: an interactive tool to expedite the statistical validation and visualization of large HDX-MS datasets', 'Hourdel, Vxc3xa9ronique|Volant, Stevenn|OBrien, Darragh P|Chenal, Alexandre|Chamot-Rooke, Julia|Dillies, Marie-Agnxc3xa8s|Brier, Sxc3xa9bastien', 'Department of Chemistry and Structural Biology, Institut Pasteur, UMR CNRS, Paris 3528, France.|Hub Bioinformatique Et Biostatistique, Institut Pasteur, C3bi, USR 3756 IP CNRS Paris 3528, France.|Department of Chemistry and Structural Biology, Institut Pasteur, UMR CNRS, Paris 3528, France.|Department of Chemistry and Structural Biology, Institut Pasteur, UMR CNRS, Paris 3528, France.|Department of Chemistry and Structural Biology, Institut Pasteur, UMR CNRS, Paris 3528, France.|Hub Bioinformatique Et Biostatistique, Institut Pasteur, C3bi, USR 3756 IP CNRS Paris 3528, France.|Department of Chemistry and Structural Biology, Institut Pasteur, UMR CNRS, Paris 3528, France.', 'France|France|France|France|France|France|France', 23, 'Datasets as Topic|Deuterium|Deuterium Exchange Measurement|Hydrogen|Mass Spectrometry|Software', 'Journal Article', '10.1093/bioinformatics/btw420', '27412089', 'With the continued improvement of requisite mass spectrometers and UHPLC systems, Hydrogen/Deuterium eXchange Mass Spectrometry (HDX-MS) workflows are rapidly evolving towards the investigation of more challenging biological systems, including large protein complexes and membrane proteins. The analysis of such extensive systems results in very large HDX-MS datasets for which specific analysis tools are required to speed up data validation and interpretation.'),
(80157, 2016, 'bio', 'BioPartsDB: a synthetic biology workflow web-application for education and research', 'Stracquadanio, Giovanni|Yang, Kun|Boeke, Jef D|Bader, Joel S', 'Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA.|Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY 10016, USA.|Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States', 3, 'Research|Software|Synthetic Biology|Workflow', 'Journal Article', '10.1093/bioinformatics/btw394', '27412090', 'Synthetic biology has become a widely used technology, and expanding applications in research, education and industry require progress tracking for team-based DNA synthesis projects. Although some vendors are beginning to supply multi-kilobase sequence-verified constructs, synthesis workflows starting with short oligos remain important for cost savings and pedagogical benefit. We developed BioPartsDB as an open source, extendable workflow management system for synthetic biology projects with entry points for oligos and larger DNA constructs and ending with sequence-verified clones.'),
(80158, 2016, 'bio', 'VisualGraphX: interactive graph visualization within Galaxy', 'Schäfer, Richard A|Voß, Björn', 'Institute of Biochemical Engineering, Computational Biology, University of Stuttgart, Allmandring, 31, 70569 Stuttgart, Germany.|Institute of Biochemical Engineering, Computational Biology, University of Stuttgart, Allmandring, 31, 70569 Stuttgart, Germany.', 'Germany|Germany', 0, 'Image Processing, Computer-Assisted|Internet', 'Journal Article', '10.1093/bioinformatics/btw414', '27412091', 'We developed VisualGraphX, a web-based, interactive visualization tool for large-scale graphs. Current graph visualization tools that follow the rich-internet paradigm lack an interactive and scalable visualization of graph-based data. VisualGraphX aims to provide a universal graph visualization tool that empowers the users to efficiently explore the data for themselves at a large scale. It is available as a visualization plugin for the Galaxy platform, such that VisualGraphX can be integrated into custom analysis pipelines.'),
(80159, 2016, 'bio', 'LightAssembler: fast and memory-efficient assembly algorithm for high-throughput sequencing reads', 'El-Metwally, Sara|Zakaria, Magdi|Hamza, Taher', 'Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.|Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.|Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.', 'Egypt|Egypt|Egypt', 5, 'Algorithms|Animals|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw470', '27412092', 'The deluge of current sequenced data has exceeded Moores Law, more than doubling every 2 years since the next-generation sequencing (NGS) technologies were invented. Accordingly, we will able to generate more and more data with high speed at fixed cost, but lack the computational resources to store, process and analyze it. With error prone high throughput NGS reads and genomic repeats, the assembly graph contains massive amount of redundant nodes and branching edges. Most assembly pipelines require this large graph to reside in memory to start their workflows, which is intractable for mammalian genomes. Resource-efficient genome assemblers combine both the power of advanced computing techniques and innovative data structures to encode the assembly graph efficiently in a computer memory.'),
(80160, 2016, 'bio', 'LAMPLINK: detection of statistically significant SNP combinations from GWAS data', 'Terada, Aika|Yamada, Ryo|Tsuda, Koji|Sese, Jun', 'PRESTO, Japan Science and Technology Agency, Saitama 332-0012, Japan.|Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507 Japan.|Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.|Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.', 'Japan|Japan|Japan|Japan', 8, 'Animals|Genome|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw418', '27412093', 'One of the major issues in genome-wide association studies is to solve the missing heritability problem. While considering epistatic interactions among multiple SNPs may contribute to solving this problem, existing software cannot detect statistically significant high-order interactions. We propose software named LAMPLINK, which employs a cutting-edge method to enumerate statistically significant SNP combinations from genome-wide case-control data. LAMPLINK is implemented as a set of additional functions to PLINK, and hence existing procedures with PLINK can be applicable. Applied to the 1000 Genomes Project data, LAMPLINK detected a combination of five SNPs that are statistically significantly accumulated in the Japanese population.'),
(80161, 2016, 'bio', 'Estimating evolutionary rates using time-structured data: a general comparison of phylogenetic methods', 'Duchêne, Sebastián|Geoghegan, Jemma L|Holmes, Edward C|Ho, Simon Y W', 'Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.|Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.|Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.', 'Australia|Australia|Australia|Australia', 13, 'Bacteria|Bayes Theorem|Models, Genetic|Phylogeny|Reproducibility of Results|Viruses', 'Journal Article', '10.1093/bioinformatics/btw421', '27412094', 'In rapidly evolving pathogens, including viruses and some bacteria, genetic change can accumulate over short time-frames. Accordingly, their sampling times can be used to calibrate molecular clocks, allowing estimation of evolutionary rates. Methods for estimating rates from time-structured data vary in how they treat phylogenetic uncertainty and rate variation among lineages. We compiled 81 virus data sets and estimated nucleotide substitution rates using root-to-tip regression, least-squares dating and Bayesian inference.'),
(80162, 2016, 'bio', 'SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena', 'Tohsato, Yukako|Ho, Kenneth H L|Kyoda, Koji|Onami, Shuichi', 'Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.|Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.|Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.|Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.', 'Japan|Japan|Japan|Japan', 6, 'Animals|Biological Phenomena|Databases, Factual|Humans|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btw417', '27412095', 'Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.'),
(80163, 2016, 'bio', 'MSAViewer: interactive JavaScript visualization of multiple sequence alignments', 'Yachdav, Guy|Wilzbach, Sebastian|Rauscher, Benedikt|Sheridan, Robert|Sillitoe, Ian|Procter, James|Lewis, Suzanna E|Rost, Burkhard|Goldberg, Tatyana', 'Bioinformatik - I12, TUM, Garching, 85748, Germany.|Bioinformatik - I12, TUM, Garching, 85748, Germany.|Bioinformatik - I12, TUM, Garching, 85748, Germany.|Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|Institute of Structure and Molecular Biology, University College London, London, UK.|Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK.|Lawrence Berkeley National Laboratory, Berkeley, USA.|Bioinformatik - I12, TUM, Garching, 85748, Germany.|Bioinformatik - I12, TUM, Garching, 85748, Germany.', 'Germany|Germany|Germany|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Germany|Germany', 59, 'Programming Languages|Sequence Alignment|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btw474', '27412096', 'The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is web ready: written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components.'),
(80164, 2016, 'bio', 'Online interactive analysis of protein structure ensembles with Bio3D-web', 'Skjærven, Lars|Jariwala, Shashank|Yao, Xin-Qiu|Grant, Barry J', 'Department of Biomedicine, University of Bergen, Bergen, Norway.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.', 'Norway|United States|United States|United States', 0, 'Data Interpretation, Statistical|Programming Languages|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw482', '27423893', 'Bio3D-web is an online application for analyzing the sequence, structure and conformational heterogeneity of protein families. Major functionality is provided for identifying protein structure sets for analysis, their alignment and refined structure superposition, sequence and structure conservation analysis, mapping and clustering of conformations and the quantitative comparison of their predicted structural dynamics.'),
(80165, 2016, 'bio', 'ntHash: recursive nucleotide hashing', 'Mohamadi, Hamid|Chu, Justin|Vandervalk, Benjamin P|Birol, Inanc', 'Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Animals|Humans|Nucleotides|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw397', '27423894', 'Hashing has been widely used for indexing, querying and rapid similarity search in many bioinformatics applications, including sequence alignment, genome and transcriptome assembly, k-mer counting and error correction. Hence, expediting hashing operations would have a substantial impact in the field, making bioinformatics applications faster and more efficient.'),
(80166, 2016, 'bio', 'TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization', 'Kou, Qiang|Xun, Likun|Liu, Xiaowen', 'Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.', 'India|India|India', 59, 'Animals|Humans|Protein Processing, Post-Translational|Proteome|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw398', '27423895', 'Top-down mass spectrometry enables the observation of whole complex proteoforms in biological samples and provides crucial information complementary to bottom-up mass spectrometry. Because of the complexity of top-down mass spectra and proteoforms, it is a challenging problem to efficiently interpret top-down tandem mass spectra in high-throughput proteome-level proteomics studies. We present TopPIC, a tool that efficiently identifies and characterizes complex proteoforms with unknown primary structure alterations, such as amino acid mutations and post-translational modifications, by searching top-down tandem mass spectra against a protein database.'),
(80167, 2016, 'bio', 'LEVER: software tools for segmentation, tracking and lineaging of proliferating cells', 'Winter, Mark|Mankowski, Walter|Wait, Eric|Temple, Sally|Cohen, Andrew R', 'Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.|Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.|Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.|Neural Stem Cell Institute, Rensselaer, NY 12144, USA.|Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Cell Lineage|Cell Proliferation|Humans|Image Processing, Computer-Assisted|Microscopy|Microscopy, Phase-Contrast|Software', 'Journal Article', '10.1093/bioinformatics/btw406', '27423896', 'The analysis of time-lapse images showing cells dividing to produce clones of related cells is an important application in biological microscopy. Imaging at the temporal resolution required to establish accurate tracking for vertebrate stem or cancer cells often requires the use of transmitted light or phase-contrast microscopy. Processing these images requires automated segmentation, tracking and lineaging algorithms. There is also a need for any errors in the automated processing to be easily identified and quickly corrected. We have developed LEVER, an open source software tool that combines the automated image analysis for phase-contrast microscopy movies with an easy-to-use interface for validating the results and correcting any errors.'),
(80168, 2016, 'bio', 'PReFerSim: fast simulation of demography and selection under the Poisson Random Field model', 'Ortega-Del Vecchyo, Diego|Marsden, Clare D|Lohmueller, Kirk E', 'Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.|Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA.|Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States', 0, 'Computer Simulation|Consanguinity|Demography|Genetics, Population|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw478', '27436562', 'The Poisson Random Field (PRF) model has become an important tool in population genetics to study weakly deleterious genetic variation under complicated demographic scenarios. Currently, there are no freely available software applications that allow simulation of genetic variation data under this model. Here we present PReFerSim, an ANSI C program that performs forward simulations under the PRF model. PReFerSim models changes in population size, arbitrary amounts of inbreeding, dominance and distributions of selective effects. Users can track summaries of genetic variation over time and output trajectories of selected alleles.'),
(80169, 2016, 'bio', 'Variance adaptive shrinkage (vash): flexible empirical Bayes estimation of variances', 'Lu, Mengyin|Stephens, Matthew', 'Department of Statistics, University of Chicago, Chicago, 60637, USA.|Department of Statistics, University of Chicago, Chicago, 60637, USA.', 'United States|United States', 2, 'Animals|Bayes Theorem|Gene Expression Profiling|Genome|Genomics|Humans', 'Journal Article', '10.1093/bioinformatics/btw483', '27436563', 'Genomic studies often involve estimation of variances of thousands of genes (or other genomic units) from just a few measurements on each. For example, variance estimation is an important step in gene expression analyses aimed at identifying differentially expressed genes. A common approach to this problem is to use an Empirical Bayes (EB) method that assumes the variances among genes follow an inverse-gamma distribution. This distributional assumption is relatively inflexible, for example, it may not capture outlying genes whose variances are considerably bigger than usual. Here we describe a more flexible EB method, capable of capturing a much wider range of distributions. Indeed, the main assumption is that the distribution of the variances is unimodal (or, as an alternative, that the distribution of the precisions is unimodal). We argue that the unimodal assumption provides an attractive compromise between flexibility, computational tractability and statistical efficiency.'),
(80170, 2016, 'bio', 'plasmidSPAdes: assembling plasmids from whole genome sequencing data', 'Antipov, Dmitry|Hartwick, Nolan|Shen, Max|Raiko, Mikhail|Lapidus, Alla|Pevzner, Pavel A', 'Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Bioinformatics and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Genome, Bacterial|Plasmids|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw493', '27466620', 'Plasmids are stably maintained extra-chromosomal genetic elements that replicate independently from the host cells chromosomes. Although plasmids harbor biomedically important genes, (such as genes involved in virulence and antibiotics resistance), there is a shortage of specialized software tools for extracting and assembling plasmid data from whole genome sequencing projects.'),
(80171, 2016, 'bio', 'Simultaneous gene finding in multiple genomes', 'Kxc3xb6nig, Stefanie|Romoth, Lars W|Gerischer, Lizzy|Stanke, Mario', 'Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, 17487, Germany.|Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, 17487, Germany.|Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, 17487, Germany.|Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, 17487, Germany.', 'Germany|Germany|Germany|Germany', 22, 'Animals|Drosophila melanogaster|Exons|Genome|Humans|Mice|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btw494', '27466621', 'As the tree of life is populated with sequenced genomes ever more densely, the new challenge is the accurate and consistent annotation of entire clades of genomes. We address this problem with a new approach to comparative gene finding that takes a multiple genome alignment of closely related species and simultaneously predicts the location and structure of protein-coding genes in all input genomes, thereby exploiting negative selection and sequence conservation. The model prefers potential gene structures in the different genomes that are in agreement with each other, or-if not-where the exon gains and losses are plausible given the species tree. We formulate the multi-species gene finding problem as a binary labeling problem on a graph. The resulting optimization problem is NP hard, but can be efficiently approximated using a subgradient-based dual decomposition approach.'),
(80172, 2016, 'bio', 'Excess positional mutual information predicts both local and allosteric mutations affecting beta lactamase drug resistance', 'Cortina, George A|Kasson, Peter M', 'Departments of Biomedical Engineering and Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.|Departments of Biomedical Engineering and Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States', 0, 'Anti-Bacterial Agents|Cefotaxime|Escherichia coli|Humans|Mutation|beta-Lactam Resistance', 'Journal Article', '10.1093/bioinformatics/btw492', '27466622', 'Bacterial resistance to antibiotics, particularly plasmid-encoded resistance to beta lactam drugs, poses an increasing threat to human health. Point mutations to beta-lactamase enzymes can greatly alter the level of resistance conferred, but predicting the effects of such mutations has been challenging due to the large combinatorial space involved and the subtle relationships of distant residues to catalytic function. Therefore we desire an information-theoretic metric to sensitively and robustly detect both local and distant residues that affect substrate conformation and catalytic activity.'),
(80173, 2016, 'bio', 'NCMine: Core-peripheral based functional module detection using near-clique mining', 'Tadaka, Shu|Kinoshita, Kengo', 'Graduate School of Information Sciences, Tohoku University, Sendai, Japan.|Graduate School of Information Sciences, Tohoku University, Sendai, Japan.', 'Japan|Japan', 0, 'Algorithms|Animals|Cluster Analysis|Humans|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1093/bioinformatics/btw488', '27466623', 'The identification of functional modules from protein-protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with core and peripheral proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level.'),
(80174, 2016, 'bio', 'WALT: fast and accurate read mapping for bisulfite sequencing', 'Chen, Haifeng|Smith, Andrew D|Chen, Ting', 'Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|United States|United States', 16, 'DNA Methylation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btw490', '27466624', 'Whole-genome bisulfite sequencing (WGBS) has emerged as the gold-standard technique in genome-scale studies of DNA methylation. Mapping reads from WGBS requires unique considerations that make the process more time-consuming than in other sequencing applications. Typical WGBS data sets contain several hundred million reads, adding to this analysis challenge. We present the WALT tool for mapping WGBS reads. WALT uses a strategy of hashing periodic spaced seeds, which leads to significant speedup compared with the most efficient methods currently available. Although many existing WGBS mappers slow down with read length, WALT improves in speed. Importantly, these speed gains do not sacrifice accuracy.'),
(80175, 2016, 'bio', 'Joint sparse canonical correlation analysis for detecting differential imaging genetics modules', 'Fang, Jian|Lin, Dongdong|Schulz, S Charles|Xu, Zongben|Calhoun, Vince D|Wang, Yu-Ping', 'Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.|The Mind Research Network, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, USA.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, ShaanXi 710049, China.|The Mind Research Network, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.', 'United States|Mexico|United States|China|Mexico|United States', 32, 'Brain|Brain Mapping|Genetic Variation|Humans|Magnetic Resonance Imaging|Polymorphism, Single Nucleotide|Schizophrenia', 'Journal Article', '10.1093/bioinformatics/btw485', '27466625', 'Imaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns.'),
(80176, 2016, 'bio', 'Drug drug interaction extraction from biomedical literature using syntax convolutional neural network', 'Zhao, Zhehuan|Yang, Zhihao|Luo, Ling|Lin, Hongfei|Wang, Jian', 'College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.', 'China|China|China|China|China', 67, 'Data Mining|Drug Interactions|Humans|Neural Networks (Computer)|Programming Languages|Publications', 'Journal Article', '10.1093/bioinformatics/btw486', '27466626', 'Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.'),
(80177, 2016, 'bio', 'Motif comparison based on similarity of binding affinity profiles', 'Lambert, Samuel A|Albu, Mihai|Hughes, Timothy R|Najafabadi, Hamed S', 'Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.|Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada.|Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.', 'Canada|Canada|Canada|Canada', 5, 'Binding Sites|Protein Binding|RNA-Binding Proteins|Sequence Alignment|Sequence Analysis, Protein|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw489', '27466627', 'Measuring motif similarity is essential for identifying functionally related transcription factors (TFs) and RNA-binding proteins, and for annotating de novo motifs. Here, we describe Motif Similarity Based on Affinity of Targets (MoSBAT), an approach for measuring the similarity of motifs by computing their affinity profiles across a large number of random sequences. We show that MoSBAT successfully associates de novo ChIP-seq motifs with their respective TFs, accurately identifies motifs that are obtained from the same TF in different in vitro assays, and quantitatively reflects the similarity of in vitro binding preferences for pairs of TFs.'),
(80178, 2016, 'bio', 'Functional classification of CATH superfamilies: a domain-based approach for protein function annotation', 'Das, Sayoni|Lee, David|Sillitoe, Ian|Dawson, Natalie L|Lees, Jonathan G|Orengo, Christine A', 'McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.|McGill University and Génome Québec Innovation Centre, Montreal, QC H3A 0G1, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/btw473', '27477482', '-'),
(80179, 2016, 'bio', 'NET-GE: a web-server for NETwork-based human gene enrichment', 'Bovo, Samuele|Di Lena, Pietro|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.|DISI, University of Bologna, Bologna, Italy.|Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.|BCA, University of Padova, Padova, Italy.|Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.', 'Italy|Italy|Italy|Italy|Italy', 9, 'Databases, Factual|Genes|Humans|Internet|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw508', '27485441', 'Gene enrichment is a requisite for the interpretation of biological complexity related to specific molecular pathways and biological processes. Furthermore, when interpreting NGS data and human variations, including those related to pathologies, gene enrichment allows the inclusion of other genes that in the human interactome space may also play important key roles in the emergency of the phenotype. Here, we describe NET-GE, a web server for associating biological processes and pathways to sets of human proteins involved in the same phenotype RESULTS: NET-GE is based on protein-protein interaction networks, following the notion that for a set of proteins, the context of their specific interactions can better define their function and the processes they can be related to in the biological complexity of the cell. Our method is suited to extract statistically validated enriched terms from Gene Ontology, KEGG and REACTOME annotation databases. Furthermore, NET-GE is effective even when the number of input proteins is small.'),
(80180, 2016, 'bio', 'MetaCoMET: a web platform for discovery and visualization of the core microbiome', 'Wang, Yi|Xu, Ling|Gu, Yong Q|Coleman-Derr, Devin', 'USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA.|USDA-ARS, Western Regional Research Center, Crop Improvement and Genetics Research Unit, Albany, CA 94710, USA.|Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA.', 'United States|United States|United States|United States', 33, 'Internet|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btw507', '27485442', 'A key component of the analysis of microbiome datasets is the identification of OTUs shared between multiple experimental conditions, commonly referred to as the core microbiome.'),
(80181, 2016, 'bio', 'An efficient method to estimate the optimum regularization parameter in RLDA', 'Bakir, Daniyar|James, Alex Pappachen|Zollanvari, Amin', 'Department of Electrical and Electronics Engineering, Nazarbayev University, Astana, 010000, Kazakhstan.|Department of Electrical and Electronics Engineering, Nazarbayev University, Astana, 010000, Kazakhstan.|Department of Electrical and Electronics Engineering, Nazarbayev University, Astana, 010000, Kazakhstan.', 'Kazakhstan|Kazakhstan|Kazakhstan', 5, 'Algorithms|Animals|Biomarkers|Biometry|Discriminant Analysis|Genomics|Humans|Normal Distribution|Sample Size', 'Journal Article', '10.1093/bioinformatics/btw506', '27485443', 'The biomarker discovery process in high-throughput genomic profiles has presented the statistical learning community with a challenging problem, namely learning when the number of variables is comparable or exceeding the sample size. In these settings, many classical techniques including linear discriminant analysis (LDA) falter. Poor performance of LDA is attributed to the ill-conditioned nature of sample covariance matrix when the dimension and sample size are comparable. To alleviate this problem, regularized LDA (RLDA) has been classically proposed in which the sample covariance matrix is replaced by its ridge estimate. However, the performance of RLDA depends heavily on the regularization parameter used in the ridge estimate of sample covariance matrix.'),
(80182, 2016, 'bio', 'Predicting G protein-coupled receptor downstream signaling by tissue expression', 'Hao, Yun|Tatonetti, Nicholas P', 'Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, NY, 10032, USA.|Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, NY, 10032, USA.', 'United States|United States', 5, 'Datasets as Topic|Gene Expression|Humans|Organ Specificity|Receptors, G-Protein-Coupled|Signal Transduction|beta-Arrestins', 'Journal Article', '10.1093/bioinformatics/btw510', '27485444', 'G protein-coupled receptors (GPCRs) are central to how cells respond to their environment and a major class of pharmacological targets. However, comprehensive knowledge of which pathways are activated and deactivated by these essential sensors is largely unknown. To better understand the mechanism of GPCR signaling system, we integrated five independent genome-wide expression datasets, representing 275 human tissues and cell lines, with protein-protein interactions and functional pathway data.'),
(80183, 2016, 'bio', 'NRGC: a novel referential genome compression algorithm', 'Saha, Subrata|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.', 'United States|United States', 9, 'Algorithms|Animals|Data Compression|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw505', '27485445', 'Next-generation sequencing techniques produce millions to billions of short reads. The procedure is not only very cost effective but also can be done in laboratory environment. The state-of-the-art sequence assemblers then construct the whole genomic sequence from these reads. Current cutting edge computing technology makes it possible to build genomic sequences from the billions of reads within a minimal cost and time. As a consequence, we see an explosion of biological sequences in recent times. In turn, the cost of storing the sequences in physical memory or transmitting them over the internet is becoming a major bottleneck for research and future medical applications. Data compression techniques are one of the most important remedies in this context. We are in need of suitable data compression algorithms that can exploit the inherent structure of biological sequences. Although standard data compression algorithms are prevalent, they are not suitable to compress biological sequencing data effectively. In this article, we propose a novel referential genome compression algorithm (NRGC) to effectively and efficiently compress the genomic sequences.'),
(80184, 2016, 'bio', 'HiPub: translating PubMed and PMC texts to networks for knowledge discovery', 'Lee, Kyubum|Shin, Wonho|Kim, Byounggun|Lee, Sunwon|Choi, Yonghwa|Kim, Sunkyu|Jeon, Minji|Tan, Aik Choon|Kang, Jaewoo', 'Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.|Department of Computer Science and Engineering, Korea University, Seoul, Korea Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Algorithms|Computational Biology|Data Curation|Databases, Factual|Genes|Humans|Pattern Recognition, Automated|Pharmaceutical Preparations|Proteins|PubMed|Search Engine', 'Journal Article', '10.1093/bioinformatics/btw511', '27485446', 'We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations.'),
(80185, 2016, 'bio', 'ReactPRED: a tool to predict and analyze biochemical reactions', 'Sivakumar, Tadi Venkata|Giri, Varun|Park, Jin Hwan|Kim, Tae Yong|Bhaduri, Anirban', 'Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-Do 443803, Korea.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-Do 443803, Korea.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India.', 'India|India|-|-|India', 12, 'Algorithms|Biochemistry|Computer Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btw491', '27485447', 'Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same.'),
(80186, 2016, 'bio', 'PcircRNA_finder: a software for circRNA prediction in plants', 'Chen, Li|Yu, Yongyi|Zhang, Xinchen|Liu, Chen|Ye, Chuyu|Fan, Longjiang', 'Institute of Crop Sciences & Institute of Bioinformatics.|Institute of Crop Sciences & Institute of Bioinformatics.|Institute of Crop Sciences & Institute of Bioinformatics.|Institute of Crop Sciences & Institute of Bioinformatics.|Institute of Crop Sciences & Institute of Bioinformatics.|Institute of Crop Sciences & Institute of Bioinformatics.', '-|-|-|-|-|-', 28, 'Algorithms|Arabidopsis|Oryza|RNA|RNA, Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw496', '27493192', 'Recent studies reveal an important role of non-coding circular RNA (circRNA) in the control of cellular processes. Because of differences in the organization of plant and mammal genomes, the sensitivity and accuracy of circRNA prediction programs using algorithms developed for animals and humans perform poorly for plants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80187, 2016, 'bio', 'FARAO: the flexible all-round annotation organizer', 'Hammarén, Rickard|Pal, Chandan|Bengtsson-Palme, Johan', 'Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46, Gothenburg, Sweden.|Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46, Gothenburg, Sweden.|Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46, Gothenburg, Sweden.', 'Sweden|Sweden|Sweden', 3, 'Computational Biology|Genome|Genomics|Metagenomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw499', '27493193', 'With decreasing costs of generating DNA sequence data, genome and metagenome projects have become accessible to a wider scientific community. However, to extract meaningful information and visualize the data remain challenging. We here introduce FARAO, a highly scalable software for organization, visualization and integration of annotation and read coverage data that can also combine output data from several bioinformatics tools. The capabilities of FARAO can greatly aid analyses of genomic and metagenomic datasets.'),
(80188, 2016, 'bio', 'Detection of differentially methylated regions in whole genome bisulfite sequencing data using local Getis-Ord statistics', 'Wen, Yalu|Chen, Fushun|Zhang, Qingzheng|Zhuang, Yan|Li, Zhiguang', 'Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, Dalian 116044, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, Dalian 116044, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, Dalian 116044, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, Dalian 116044, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, Dalian 116044, China.', 'China|China|China|China|China', 9, 'Animals|DNA Methylation|Genome|Humans|Mice|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btw497', '27493194', 'DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing is a widely used technique to obtain genome-wide DNA methylation profiles, and one of the key tasks of analyzing bisulfite sequencing data is to detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed to detect differentially methylated single CpG site (DMC) between samples, methods for direct DMR detection, especially for complex study designs, are largely limited.'),
(80189, 2016, 'bio', 'genipe: an automated genome-wide imputation pipeline with automatic reporting and statistical tools', 'Lemieux Perreault, Louis-Philippe|Legault, Marc-André|Asselin, Géraldine|Dubé, Marie-Pierre', 'Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montreal Heart Institute Research Center, Montréal, Canada H1T 1C8.|Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montreal Heart Institute Research Center, Montréal, Canada H1T 1C8.|Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montreal Heart Institute Research Center, Montréal, Canada H1T 1C8.|Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montreal Heart Institute Research Center, Montréal, Canada H1T 1C8.', 'Canada|Canada|Canada|Canada', 0, 'Computational Biology|Electronic Data Processing|Genome|Genomics|Genotype|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw487', '27497439', 'Genotype imputation is now commonly performed following genome-wide genotyping experiments. Imputation increases the density of analyzed genotypes in the dataset, enabling fine-mapping across the genome. However, the process of imputation using the most recent publicly available reference datasets can require considerable computation power and the management of hundreds of large intermediate files. We have developed genipe, a complete genome-wide imputation pipeline which includes automatic reporting, imputed data indexing and management, and a suite of statistical tests for imputed data commonly used in genetic epidemiology (Sequence Kernel Association Test, Cox proportional hazards for survival analysis, and linear mixed models for repeated measurements in longitudinal studies).'),
(80190, 2016, 'bio', 'scphaser: haplotype inference using single-cell RNA-seq data', 'Edsgxc3xa4rd, Daniel|Reinius, Bjxc3xb6rn|Sandberg, Rickard', 'Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden.|Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden.|Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden Ludwig Institute for Cancer Research, Box 240, 171 77 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 0, 'Alleles|Animals|Genetic Variation|Haplotypes|Humans|Mice|RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btw484', '27497440', 'Determination of haplotypes is important for modelling the phenotypic consequences of genetic variation in diploid organisms, including cis-regulatory control and compound heterozygosity. We realized that single-cell RNA-seq (scRNA-seq) data are well suited for phasing genetic variants, since both transcriptional bursts and technical bottlenecks cause pronounced allelic fluctuations in individual single cells. Here we present scphaser, an R package that phases alleles at heterozygous variants to reconstruct haplotypes within transcribed regions of the genome using scRNA-seq data. The devised method efficiently and accurately reconstructed the known haplotype forxe2x80x89xe2x89xa593% of phasable genes in both human and mouse. It also enables phasing of rare and de novo variants and variants far apart within genes, which is hard to attain with population-based computational inference.'),
(80191, 2016, 'bio', 'Metrics for rapid quality control in RNA structure probing experiments', 'Choudhary, Krishna|Shih, Nathan P|Deng, Fei|Ledda, Mirko|Li, Bo|Aviran, Sharon', 'Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Center for RNA Systems Biology, University of California at Berkeley, Berkeley, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Models, Statistical|Quality Control|RNA|Reproducibility of Results|Sequence Analysis, RNA|Signal-To-Noise Ratio', 'Journal Article', '10.1093/bioinformatics/btw501', '27497441', 'The diverse functionalities of RNA can be attributed to its capacity to form complex and varied structures. The recent proliferation of new structure probing techniques coupled with high-throughput sequencing has helped RNA studies expand in both scope and depth. Despite differences in techniques, most experiments face similar challenges in reproducibility due to the stochastic nature of chemical probing and sequencing. As these protocols expand to transcriptome-wide studies, quality control becomes a more daunting task. General and efficient methodologies are needed to quantify variability and quality in the wide range of current and emerging structure probing experiments.'),
(80192, 2016, 'bio', 'A novel copy number variants kernel association test with application to autism spectrum disorders studies', 'Zhan, Xiang|Girirajan, Santhosh|Zhao, Ni|Wu, Michael C|Ghosh, Debashis', 'Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.|Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Department of Biostatistics and Informatics, University of Colorado, Aurora, CO 80045, USA.', 'United States|United States|United States|United States|United States', 10, 'Algorithms|Autism Spectrum Disorder|Computational Biology|DNA Copy Number Variations|Humans|Phenotype', 'Journal Article', '10.1093/bioinformatics/btw500', '27497442', 'Copy number variants (CNVs) have been implicated in a variety of neurodevelopmental disorders, including autism spectrum disorders, intellectual disability and schizophrenia. Recent advances in high-throughput genomic technologies have enabled rapid discovery of many genetic variants including CNVs. As a result, there is increasing interest in studying the role of CNVs in the etiology of many complex diseases. Despite the availability of an unprecedented wealth of CNV data, methods for testing association between CNVs and disease-related traits are still under-developed due to the low prevalence and complicated multi-scale features of CNVs.'),
(80193, 2016, 'bio', 'WebSTAR3D: a web server for RNA 3D structural alignment', 'Holzhauser, Erwin|Ge, Ping|Zhang, Shaojie', 'Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.|Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.|Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.', 'United States|United States|United States', 0, 'Internet|Nucleic Acid Conformation|RNA|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw502', '27497443', 'The WebSTAR3D web server is a user-friendly online interface for the alignment of RNA 3D structures. The website takes as input two files, each of which can be in either PDB or mmCIF format, containing the desired structures to align, via a PDB code or user upload. In return, the user is presented with a visualization of the aligned structures in Jmol or JSmol, along with the corresponding sequence alignment, and the option to download the nucleotide mapping of the structures and a PDB file containing the aligned, superimposed structures.'),
(80194, 2016, 'bio', 'Rule-based modeling with Virtual Cell', 'Schaff, James C|Vasilescu, Dan|Moraru, Ion I|Loew, Leslie M|Blinov, Michael L', 'R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.|R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.|R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.|R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.|R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA.', 'United States|United States|United States|United States|United States', 0, 'Cell Physiological Phenomena|Computational Biology|Computer Simulation|Gene Regulatory Networks|Models, Biological|Models, Theoretical|Programming Languages|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btw353', '27497444', 'Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine.'),
(80195, 2016, 'bio', 'A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes', 'Wheeler, Nicole E|Barquist, Lars|Kingsley, Robert A|Gardner, Paul P', 'School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.|Institute for Molecular Infection Biology, University of Wuerzburg, Wuerzburg, Germany.|Institute of Food Research, Norwich Research Park, Norwich, UK.|School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.', 'New Zealand|Germany|United Kingdom|New Zealand', 0, 'Algorithms|Bacterial Proteins|Genome, Bacterial|Genomics|Markov Chains|Models, Theoretical|Proteome|Salmonella enterica|Software', 'Journal Article', '10.1093/bioinformatics/btw518', '27503221', 'Next generation sequencing technologies have provided us with a wealth of information on genetic variation, but predicting the functional significance of this variation is a difficult task. While many comparative genomics studies have focused on gene flux and large scale changes, relatively little attention has been paid to quantifying the effects of single nucleotide polymorphisms and indels on protein function, particularly in bacterial genomics.'),
(80196, 2016, 'bio', 'BiDiFuse: a FIJI plugin for fusing bi-directionally recorded microscopic image volumes', 'Detrez, Jan R|Vanderwinden, Jean-Marie|Barbier, Michaël|Verschuuren, Marlies|Nuydens, Rony|Langlois, Xavier|Timmermans, Jean-Pierre|De Vos, Winnok H', 'Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2020 Antwerp, Belgium.|Light Microscopy Facility & Laboratory of Neurophysiology, Faculty of Medicine, Free University of Brussels, 1070 Anderlecht, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2020 Antwerp, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2020 Antwerp, Belgium.|Department of Neuroscience, Janssen Research and Development, 2340 Beerse, Belgium.|Department of Neuroscience, Janssen Research and Development, 2340 Beerse, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2020 Antwerp, Belgium.|Laboratory of Cell Biology & Histology, Department of Veterinary Sciences, University of Antwerp, 2020 Antwerp, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 2, 'Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btw515', '27503222', 'Deep tissue imaging is increasingly used for non-destructive interrogation of intact organs and small model organisms. An intuitive approach to increase the imaging depth by almost a factor of 2 is to record a sample from two sides and fuse both image stacks. However, imperfect three-dimensional alignment of both stacks presents a computational challenge. We have developed a FIJI plugin, called BiDiFuse, which merges bi-directionally recorded image stacks via 3D rigid transformations. The method is broadly applicable, considering it is compatible with all optical sectioning microscopes and it does not rely on fiducial markers for image registration.'),
(80197, 2016, 'bio', 'Bayesian network feature finder (BANFF): an R package for gene network feature selection', 'Lan, Zhou|Zhao, Yize|Kang, Jian|Yu, Tianwei', 'Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.|Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Bayes Theorem|Computational Biology|Gene Regulatory Networks|Humans|Markov Chains|Software', 'Journal Article', '10.1093/bioinformatics/btw522', '27503223', 'Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand.'),
(80198, 2016, 'bio', 'PathScore: a web tool for identifying altered pathways in cancer data', 'Gaffney, Stephen G|Townsend, Jeffrey P', 'Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06511, USA.|Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06511, USA.', 'United States|United States', 1, 'Algorithms|Computational Biology|Humans|Internet|Metabolic Networks and Pathways|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw512', '27503224', 'PathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.'),
(80199, 2016, 'bio', 'RNAcommender: genome-wide recommendation of RNA-protein interactions', 'Corrado, Gianluca|Tebaldi, Toma|Costa, Fabrizio|Frasconi, Paolo|Passerini, Andrea', 'Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy.|Centre for Integrative Biology, University of Trento, Trento 38123, Italy.|Department of Computer Science, Albert-Ludwigs-Universitaet Freiburg, Freiburg 79110, Germany.|Dipartimento di Ingegneria dellInformazione, University of Florence, Florence 50139, Italy.|Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy.', 'Italy|Italy|Germany|Italy|Italy', 0, 'Humans|Protein Binding|RNA|RNA-Binding Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw517', '27503225', 'Information about RNA-protein interactions is a vital pre-requisite to tackle the dissection of RNA regulatory processes. Despite the recent advances of the experimental techniques, the currently available RNA interactome involves a small portion of the known RNA binding proteins. The importance of determining RNA-protein interactions, coupled with the scarcity of the available information, calls for in silico prediction of such interactions.'),
(80200, 2016, 'bio', 'shinyGEO: a web-based application for analyzing gene expression omnibus datasets', 'Dumas, Jasmine|Gargano, Michael A|Dancik, Garrett M', 'College of Computing and Digital Media, DePaul University, Chicago, IL, USA.|Department of Mathematics and Computer Science, Eastern Connecticut State University, Willimantic, CT, USA.|Department of Mathematics and Computer Science, Eastern Connecticut State University, Willimantic, CT, USA.', 'United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Internet|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw519', '27503226', 'The Gene Expression Omnibus (GEO) is a public repository of gene expression data. Although GEO has its own tool, GEO2R, for data analysis, evaluation of single genes is not straightforward and survival analysis in specific GEO datasets is not possible without bioinformatics expertise. We describe a web application, shinyGEO, that allows a user to download gene expression data sets directly from GEO in order to perform differential expression and survival analysis for a gene of interest. In addition, shinyGEO supports customized graphics, sample selection, data export and R code generation so that all analyses are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.'),
(80201, 2016, 'bio', 'SRinversion: a tool for detecting short inversions by splitting and re-aligning poorly mapped and unmapped sequencing reads', 'Chen, Ruoyan|Lau, Yu Lung|Zhang, Yan|Yang, Wanling', 'Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.|Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.|Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.|Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 3, 'Algorithms|Chromosome Inversion|Genome, Human|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw516', '27503227', 'Rapid development in sequencing technologies has dramatically improved our ability to detect genetic variants in human genome. However, current methods have variable sensitivities in detecting different types of genetic variants. One type of such genetic variants that is especially hard to detect is inversions. Analysis of public databases showed that few short inversions have been reported so far. Unlike reads that contain small insertions or deletions, which will be considered through gap alignment, reads carrying short inversions often have poor mapping quality or are unmapped, thus are often not further considered. As a result, the majority of short inversions might have been overlooked and require special algorithms for their detection.'),
(80202, 2016, 'bio', 'PRODIGY: a web server for predicting the binding affinity of protein-protein complexes', 'Xue, Li C|Rodrigues, João Pglm|Kastritis, Panagiotis L|Bonvin, Alexandre Mjj|Vangone, Anna', 'Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Internet|Protein Binding|Protein Conformation|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btw514', '27503228', 'Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface.'),
(80203, 2016, 'bio', 'DTMiner: identification of potential disease targets through biomedical literature mining', 'Xu, Dong|Zhang, Meizhuo|Xie, Yanping|Wang, Fan|Chen, Ming|Zhu, Kenny Q|Wei, Jia', 'Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.|R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.|Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.|R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.|Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.|R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Biomedical Research|Data Mining|Disease|Gene Library|Humans|Periodicals as Topic', 'Journal Article', '10.1093/bioinformatics/btw503', '27506226', 'Biomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy.'),
(80204, 2016, 'bio', 'Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates', 'Cogill, S|Wang, L', 'Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.|Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.', 'United States|United States', 18, 'Algorithms|Autism Spectrum Disorder|Brain|Gene Expression|Humans|Risk Factors|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw498', '27506227', 'Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that are time-consuming and expensive to validate. Prioritization methods can identify high-confidence candidates. Previous ASD gene prioritization methods have focused on a priori knowledge, which excludes genes with little functional annotation or no protein product such as long non-coding RNAs (lncRNAs).'),
(80205, 2016, 'bio', 'NVT: a fast and simple tool for the assessment of RNA-seq normalization strategies', 'Eder, Thomas|Grebien, Florian|Rattei, Thomas', 'Ludwig Boltzmann Institute for Cancer Research, Vienna, 1090, Austria.|Ludwig Boltzmann Institute for Cancer Research, Vienna, 1090, Austria.|CUBE Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, 1090, Austria.', 'Austria|Austria|Austria', 0, 'Computational Biology|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw521', '27515738', 'Measuring differential gene expression is a common task in the analysis of RNA-Seq data. To identify differentially expressed genes between two samples, it is crucial to normalize the datasets. While multiple normalization methods are available, all of them are based on certain assumptions that may or may not be suitable for the type of data they are applied on. Researchers therefore need to select an adequate normalization strategy for each RNA-Seq experiment. This selection includes exploration of different normalization methods as well as their comparison. Methods that agree with each other most likely represent realistic assumptions under the particular experimental conditions.'),
(80206, 2016, 'bio', 'LCA*: an entropy-based measure for taxonomic assignment within assembled metagenomes', 'Hanson, Niels W|Konwar, Kishori M|Hallam, Steven J', 'Graduate Program in Bioinformatics University of British Columbia, Vancouver, Canada.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.|Graduate Program in Bioinformatics University of British Columbia, Vancouver, Canada.', 'Canada|United States|Canada', 9, 'Algorithms|Entropy|Metagenome|Models, Statistical|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btw400', '27515739', 'A perennial problem in the analysis of environmental sequence information is the assignment of reads or assembled sequences, e.g. contigs or scaffolds, to discrete taxonomic bins. In the absence of reference genomes for most environmental microorganisms, the use of intrinsic nucleotide patterns and phylogenetic anchors can improve assembly-dependent binning needed for more accurate taxonomic and functional annotation in communities of microorganisms, and assist in identifying mobile genetic elements or lateral gene transfer events.'),
(80207, 2016, 'bio', 'ChemTreeMap: an interactive map of biochemical similarity in molecular datasets', 'Lu, Jing|Carlson, Heather A', 'Department of Computational Medicine and Bioinformatics.|Department of Computational Medicine and Bioinformatics.', '-|-', 0, 'Algorithms|Biochemistry|Computational Biology|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btw523', '27515740', 'What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into todays team-based biological research.'),
(80208, 2016, 'bio', 'Multivariate Welch t-test on distances', 'Alekseyenko, Alexander V', 'Departments of Public Health Sciences and Oral Health Sciences, Program for Human Microbiome Research, The Biomedical Informatics Center Medical University of South Carolina, 135 Cannon Street, MSC 200, Charleston, SC 29466, USA.', 'United States', 0, 'Animals|Computer Simulation|Gastrointestinal Tract|Humans|Mice|Microbiota|Models, Statistical|Psoriasis|Sample Size|Skin', 'Journal Article', '10.1093/bioinformatics/btw524', '27515741', 'Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances.'),
(80209, 2016, 'bio', 'EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types', 'Gao, Tianshun|He, Bing|Liu, Sheng|Zhu, Heng|Tan, Kai|Qian, Jiang', 'The Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Division of Oncology and Center for Childhood Cancer Research, Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|The Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.|Division of Oncology and Center for Childhood Cancer Research, Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|The Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.', 'United States|United States|United States|United States|United States|United States', 64, 'Databases, Nucleic Acid|Enhancer Elements, Genetic|Genomics|Humans|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/btw495', '27515742', 'Multiple high-throughput approaches have recently been developed and allowed the discovery of enhancers on a genome scale in a single experiment. However, the datasets generated from these approaches are not fully utilized by the research community due to technical challenges such as lack of consensus enhancer annotation and integrative analytic tools.'),
(80210, 2016, 'bio', 'Gene Slider: sequence logo interactive data-visualization for education and research', 'Waese, Jamie|Pasha, Asher|Wang, Ting Ting|van Weringh, Anna|Guttman, David S|Provart, Nicholas J', 'Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.|Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.|Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.|Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.|Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.|Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Binding Sites|Brassicaceae|Computational Biology|Conserved Sequence|Databases, Genetic|Internet|Position-Specific Scoring Matrices|Promoter Regions, Genetic|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw525', '27522081', 'Gene Slider helps visualize the conservation and entropy of orthologous DNA and protein sequences by presenting them as one long sequence logo that can be zoomed in and out of, from an overview of the entire sequence down to just a few residues at a time. A search function enables users to find motifs such as cis-elements in promoter regions by simply drawing a sequence logo representation of the desired motif as a query. In addition to displaying user-supplied FASTA files, our demonstration version of Gene Slider loads and displays a rich database of 90xc2xa0000+xe2x80x89conserved non-coding regions across the Brassicaceae indexed to the TAIR10 Col-0 Arabidopsis thaliana sequence. It also displays transcription factor binding sites, enabling easy identification of regions that are both conserved across multiple species and may contain transcription factor binding sites.'),
(80211, 2016, 'bio', 'oxBS-MLE: an efficient method to estimate 5-methylcytosine and 5-hydroxymethylcytosine in paired bisulfite and oxidative bisulfite treated DNA', 'Xu, Zongli|Taylor, Jack A|Leung, Yuet-Kin|Ho, Shuk-Mei|Niu, Liang', 'Epidemiology Branch.|Epidemiology Branch.|Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA.|Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA.|Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA.', 'United States|United States|United States|United States|United States', 12, '5-Methylcytosine|Computational Biology|CpG Islands|DNA|DNA Methylation|Models, Theoretical|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw527', '27522082', '5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are important epigenetic regulators of gene expression. 5mC and 5hmC levels can be computationally inferred at single base resolution using sequencing or array data from paired DNA samples that have undergone bisulfite and oxidative bisulfite conversion. Current estimation methods have been shown to produce irregular estimates of 5hmC level or are extremely computation intensive.'),
(80212, 2016, 'bio', 'SDEAP: a splice graph based differential transcript expression analysis tool for population data', 'Yang, Ei-Wen|Jiang, Tao', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.', 'United States|United States', 0, 'Algorithms|Alternative Splicing|Cell Cycle|Exons|Gene Expression Profiling|Humans|Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw513', '27522083', 'Differential transcript expression (DTE) analysis without predefined conditions is critical to biological studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools for population data, i.e. data without known biological conditions, have been published, these tools either assume binary conditions in the input population or require the number of conditions as a part of the input. Fixing the number of conditions to binary is unrealistic and may distort the results of a DTE analysis. Estimating the correct number of conditions in a population could also be challenging for a routine user. Moreover, the existing tools only provide differential usages of exons, which may be insufficient to interpret the patterns of alternative splicing across samples and restrains the applications of the tools from many biology studies.'),
(80213, 2016, 'bio', 'Extensive complementarity between gene function prediction methods', 'Vidulin, Vedrana|xc5xa0muc, Tomislav|Supek, Fran', 'Division of Electronics, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Bijenixc4x8dka cesta 54, Zagreb 10000, Croatia.|Division of Electronics, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Bijenixc4x8dka cesta 54, Zagreb 10000, Croatia.|Division of Electronics, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Bijenixc4x8dka cesta 54, Zagreb 10000, Croatia.', 'Croatia|Croatia|Croatia', 7, 'Algorithms|Computational Biology|Gene Ontology|Genome|Genomics|Machine Learning|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw532', '27522084', 'The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions.'),
(80214, 2016, 'bio', 'CGDM: collaborative genomic data model for molecular profiling data using NoSQL', 'Wang, Shicai|Mares, Mihaela A|Guo, Yi-Ke', 'Data Science Institute, Imperial College London, London, UK.|Data Science Institute, Imperial College London, London, UK.|Data Science Institute, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Cluster Analysis|Computational Biology|Databases, Genetic|Genomics|Humans|Information Storage and Retrieval|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw531', '27522085', 'High-throughput molecular profiling has greatly improved patient stratification and mechanistic understanding of diseases. With the increasing amount of data used in translational medicine studies in recent years, there is a need to improve the performance of data warehouses in terms of data retrieval and statistical processing. Both relational and Key Value models have been used for managing molecular profiling data. Key Value models such as SeqWare have been shown to be particularly advantageous in terms of query processing speed for large datasets. However, more improvement can be achieved, particularly through better indexing techniques of the Key Value models, taking advantage of the types of queries which are specific for the high-throughput molecular profiling data.'),
(80215, 2017, 'bio', 'SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA', 'Kockan, Can|Hach, Faraz|Sarrafi, Iman|Bell, Robert H|McConeghy, Brian|Beja, Kevin|Haegert, Anne|Wyatt, Alexander W|Volik, Stanislav V|Chi, Kim N|Collins, Colin C|Sahinalp, S Cenk', 'School of Computing Science.|School of Computing Science.|School of Computing Science.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|School of Computing Science.', '-|-|-|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|-', 24, 'DNA Mutational Analysis|DNA, Neoplasm|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Male|Neoplasms|Point Mutation|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btw536', '27531099', 'Successful development and application of precision oncology approaches require robust elucidation of the genomic landscape of a patients cancer and, ideally, the ability to monitor therapy-induced genomic changes in the tumour in an inexpensive and minimally invasive manner. Thanks to recent advances in sequencing technologies, liquid biopsy, the sampling of patients bodily fluids such as blood and urine, is considered as one of the most promising approaches to achieve this goal. In many cancer patients, and especially those with advanced metastatic disease, deep sequencing of circulating cell free DNA (cfDNA) obtained from patients blood yields a mixture of reads originating from the normal DNA and from multiple tumour subclones-called circulating tumour DNA or ctDNA. The ctDNA/cfDNA ratio as well as the proportion of ctDNA originating from specific tumour subclones depend on multiple factors, making comprehensive detection of mutations difficult, especially at early stages of cancer. Furthermore, sensitive and accurate detection of single nucleotide variants (SNVs) and indels from cfDNA is constrained by several factors such as the sequencing errors and PCR artifacts, and mapping errors related to repeat regions within the genome. In this article, we introduce SiNVICT, a computational method that increases the sensitivity and specificity of SNV and indel detection at very low variant allele frequencies. SiNVICT has the capability to handle multiple sequencing platforms with different error properties, it minimizes false positives resulting from mapping errors and other technology specific artifacts including strand bias and low base quality at read ends. SiNVICT also has the capability to perform time-series analysis, where samples from a patient sequenced at multiple time points are jointly examined to report locations of interest where there is a possibility that certain clones were wiped out by some treatment while some subclones gained selective advantage.'),
(80216, 2016, 'bio', 'Corpus domain effects on distributional semantic modeling of medical terms', 'Pakhomov, Serguei V S|Finley, Greg|McEwan, Reed|Wang, Yan|Melton, Genevieve B', 'College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA.|Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.|Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.|Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.|Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.', 'United States|United States|United States|United States|United States', 39, 'Data Mining|Humans|Natural Language Processing|Neural Networks (Computer)|PubMed|Reference Standards|Semantics|Software|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/btw529', '27531100', 'Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available, however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.'),
(80217, 2016, 'bio', 'A computational strategy to adjust for copy number in tumor Hi-C data', 'Wu, Hua-Jun|Michor, Franziska', 'Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.|Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.', 'United States|United States', 17, 'Computational Biology|DNA Copy Number Variations|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw540', '27531101', 'The Hi-C technology was designed to decode the three-dimensional conformation of the genome. Despite progress towards more and more accurate contact maps, several systematic biases have been demonstrated to affect the resulting data matrix. Here we report a new source of bias that can arise in tumor Hi-C data, which is related to the copy number of genomic DNA. To address this bias, we designed a chromosome-adjusted iterative correction method called caICB. Our caICB correction method leads to significant improvements when compared with the original iterative correction in terms of eliminating copy number bias.'),
(80218, 2017, 'bio', 'iRSpot-EL: identify recombination spots with an ensemble learning approach', 'Liu, Bin|Wang, Shanyi|Long, Ren|Chou, Kuo-Chen', 'School of Computer Science and Technology.|School of Computer Science and Technology.|School of Computer Science and Technology.|Gordon Life Science Institute, Belmont, MA 02478, USA.', 'United States|United States|United States|United States', 233, 'Chromosomes, Fungal|Genomics|Meiosis|Recombination, Genetic|Sequence Analysis, DNA|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw539', '27531102', 'Coexisting in a DNA system, meiosis and recombination are two indispensible aspects for cell reproduction and growth. With the avalanche of genome sequences emerging in the post-genomic age, it is an urgent challenge to acquire the information of DNA recombination spots because it can timely provide very useful insights into the mechanism of meiotic recombination and the process of genome evolution.'),
(80219, 2016, 'bio', 'H-PoP and H-PoPG: heuristic partitioning algorithms for single individual haplotyping of polyploids', 'Xie, Minzhu|Wu, Qiong|Wang, Jianxin|Jiang, Tao', 'Key Laboratory of Internet of Things Technologies and Application, College of Physics and Information Science, Hunan Normal University, Changsha 410081, China.|State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.|School of Information Science and Engineering, Central South University, Changsha 410083, China.|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.', 'China|China|China|United States', 12, 'Algorithms|Haplotypes|Heuristics|High-Throughput Nucleotide Sequencing|Models, Genetic|Plants|Polyploidy|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw537', '27531103', 'Some economically important plants including wheat and cotton have more than two copies of each chromosome. With the decreasing cost and increasing read length of next-generation sequencing technologies, reconstructing the multiple haplotypes of a polyploid genome from its sequence reads becomes practical. However, the computational challenge in polyploid haplotyping is much greater than that in diploid haplotyping, and there are few related methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80220, 2016, 'bio', 'DOMINO: development of informative molecular markers for phylogenetic and genome-wide population genetic studies in non-model organisms', 'Frías-López, Cristina|Sánchez-Herrero, José F|Guirao-Rico, Sara|Mora, Elisa|Arnedo, Miquel A|Sánchez-Gracia, Alejandro|Rozas, Julio', 'Departament de Genètica, Microbiologia i Estadística, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.|Departament de Genètica, Microbiologia i Estadística, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.|Departament de Genètica, Microbiologia i Estadística, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.|Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona 08028, Spain.|Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona 08028, Spain.|Departament de Genètica, Microbiologia i Estadística, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.|Departament de Genètica, Microbiologia i Estadística, and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 2, 'Computational Biology|Genetic Markers|Genetics, Population|Genomics|Genotype|High-Throughput Nucleotide Sequencing|Phylogeny|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw534', '27531104', 'The development of molecular markers is one of the most important challenges in phylogenetic and genome wide population genetics studies, especially in studies with non-model organisms. A highly promising approach for obtaining suitable markers is the utilization of genomic partitioning strategies for the simultaneous discovery and genotyping of a large number of markers. Unfortunately, not all markers obtained from these strategies provide enough information for solving multiple evolutionary questions at a reasonable taxonomic resolution.'),
(80221, 2016, 'bio', 'ImmQuant: a user-friendly tool for inferring immune cell-type composition from gene-expression data', 'Frishberg, Amit|Brodt, Avital|Steuerman, Yael|Gat-Viks, Irit', 'Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.', 'Israel|Israel|Israel|Israel', 24, 'Algorithms|Animals|Humans|Immune System|Mice|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw535', '27531105', ': The composition of immune-cell subsets is key to the understanding of major diseases and pathologies. Computational deconvolution methods enable researchers to investigate immune cell quantities in complex tissues based on transcriptome data. Here we present ImmQuant, a software tool allowing immunologists to upload transcription profiles of multiple tissue samples, apply deconvolution methodology to predict differences in cell-type quantities between the samples, and then inspect the inferred cell-type alterations using convenient visualization tools. ImmQuant builds on the DCQ deconvolution algorithm and allows a user-friendly utilization of this method by non-bioinformatician researchers. Specifically, it enables investigation of hundreds of immune cell subsets in mouse tissues, as well as a few dozen cell types in human samples.'),
(80222, 2016, 'bio', 'Selective mutation accumulation: a computational model of the paternal age effect', 'Whelan, Eoin C|Nwala, Alexander C|Osgood, Christopher|Olariu, Stephan', 'Department of Biology, Old Dominion University, Norfolk, VA, USA.|Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.|Department of Biology, Old Dominion University, Norfolk, VA, USA.|Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.', 'United States|United States|United States|United States', 1, 'Adult Germline Stem Cells|Computer Simulation|Humans|Male|Markov Chains|Mutation|Mutation Accumulation|Paternal Age|Selection, Genetic|Spermatogonia|Stem Cell Niche|Testis', 'Journal Article', '10.1093/bioinformatics/btw528', '27531106', 'As the mean age of parenthood grows, the effect of parental age on genetic disease and child health becomes ever more important. A number of autosomal dominant disorders show a dramatic paternal age effect due to selfish mutations: substitutions that grant spermatogonial stem cells (SSCs) a selective advantage in the testes of the father, but have a deleterious effect in offspring. In this paper we present a computational technique to model the SSC niche in order to examine the phenomenon and draw conclusions across different genes and disorders.'),
(80223, 2016, 'bio', 'CSAM: Compressed SAM format', 'Cánovas, Rodrigo|Moffat, Alistair|Turpin, Andrew', 'L.I.R.M.M. and Institut Biologie Computationnelle, Université de Montpellier, Montpellier Cedex 5, CNRS F-34392, France.|Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia.|Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia.', 'France|Australia|Australia', 5, 'Data Compression|Genome|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw543', '27540265', 'Next generation sequencing machines produce vast amounts of genomic data. For the data to be useful, it is essential that it can be stored and manipulated efficiently. This work responds to the combined challenge of compressing genomic data, while providing fast access to regions of interest, without necessitating decompression of whole files.'),
(80224, 2016, 'bio', 'Higher classification sensitivity of short metagenomic reads with CLARK-S', 'Ounit, Rachid|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.', 'United States|United States', 40, 'Algorithms|Ecosystem|Humans|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btw542', '27540266', 'The growing number of metagenomic studies in medicine and environmental sciences is creating increasing demands on the computational infrastructure designed to analyze these very large datasets. Often, the construction of ultra-fast and precise taxonomic classifiers can compromise on their sensitivity (i.e. the number of reads correctly classified). Here we introduce CLARK-S, a new software tool that can classify short reads with high precision, high sensitivity and high speed.'),
(80225, 2016, 'bio', 'ReadXplorer 2-detailed read mapping analysis and visualization from one single source', 'Hilker, Rolf|Stadermann, Kai Bernd|Schwengers, Oliver|Anisiforov, Evgeny|Jaenicke, Sebastian|Weisshaar, Bernd|Zimmermann, Tobias|Goesmann, Alexander', 'Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.|Faculty of Biology, Chair of Genome Research, Bielefeld University, Bielefeld 33615, Germany.|Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.|Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.|Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.|Faculty of Biology, Chair of Genome Research, Bielefeld University, Bielefeld 33615, Germany.|Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.|Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 40, 'Arabidopsis|Computational Biology|Gene Expression|Genome|Genomic Structural Variation|RNA, Plant|Sequence Analysis, RNA|Software|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btw541', '27540267', 'The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement.'),
(80226, 2016, 'bio', 'BatchQC: interactive software for evaluating sample and batch effects in genomic data', 'Manimaran, Solaiappan|Selby, Heather Marie|Okrah, Kwame|Ruberman, Claire|Leek, Jeffrey T|Quackenbush, John|Haibe-Kains, Benjamin|Bravo, Hector Corrada|Johnson, W Evan', 'Department of Biostatistics, Boston University, Boston, MA.|Bioinformatics Program, Boston University, Boston, MA.|gRED Oncology Biostatistics, Genentech, South San Francisco, CA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA.|Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Ontario, Canada.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD.|Department of Biostatistics, Boston University, Boston, MA.', '-|-|-|-|-|-|Canada|-|-', 18, 'Computational Biology|Genome|Genomics|Humans|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw538', '27540268', 'Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for -omics data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit.'),
(80227, 2016, 'bio', 'Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes', 'Liu, Yiyi|Zhao, Hongyu', 'Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.|Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.', 'United States|United States', 9, 'Computational Biology|Drug Synergism|Gene Expression|Humans|Logistic Models|Molecular Structure|Neoplasms|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw509', '27540269', 'Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies.'),
(80228, 2016, 'bio', 'A new correlation clustering method for cancer mutation analysis', 'Hou, Jack P|Emad, Amin|Puleo, Gregory J|Ma, Jian|Milenkovic, Olgica', 'Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computational Biology|DNA Mutational Analysis|Female|Gene Regulatory Networks|Glioblastoma|Humans|Mutation', 'Journal Article', '10.1093/bioinformatics/btw546', '27540270', 'Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer.'),
(80229, 2016, 'bio', 'DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments', 'Vrahatis, Aristidis G|Balomenos, Panos|Tsakalidis, Athanasios K|Bezerianos, Anastasios', 'Department of Computer Engineering and Informatics.|Department of Computer Engineering and Informatics.|Department of Computer Engineering and Informatics.|Department of Medicine, University of Patras, Patras, 26500, GR.', '-|-|-|-', 1, 'Humans|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw544', '27542770', 'DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases.'),
(80230, 2016, 'bio', 'MSIdV: a versatile tool to visualize biological indices from mass spectrometry imaging data', 'Hayakawa, Eisuke|Fujimura, Yoshinori|Miura, Daisuke', 'Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.|Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.|Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.', 'Japan|Japan|Japan', 5, 'Image Processing, Computer-Assisted|Mass Spectrometry|Software', 'Journal Article', '10.1093/bioinformatics/btw548', '27542771', 'Mass spectrometry imaging (MSI) visualizes the simultaneous lateral distribution of multiple compounds on sample surface. However, it is still difficult to visualize biological indices such as energy charge index from multiple compounds because of the lack of publicly available tools. Here we present MSIdV, a visualization tool for biological indices calculated from mass spectrometry imaging data, which can effectively scan a series of mass spectra and process, calculate and visualize user-defined index measures accurately with a number of signal processing features.'),
(80231, 2016, 'bio', 'STAMS: STRING-assisted module search for genome wide association studies and application to autism', 'Hillenmeyer, Sara|Davis, Lea K|Gamazon, Eric R|Cook, Edwin H|Cox, Nancy J|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.|Vanderbilt Genetics Institute.|Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.|Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.|Vanderbilt Genetics Institute.|Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Autistic Disorder|Computational Biology|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Phenotype', 'Journal Article', '10.1093/bioinformatics/btw530', '27542772', 'Analyzing genome wide association data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine genome wide association results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this article, we combine an algorithm for weighted-edge module searching and a probabilistic interaction network in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in six test cases.'),
(80232, 2016, 'bio', 'What time is it? Deep learning approaches for circadian rhythms', 'Agostinelli, Forest|Ceglia, Nicholas|Shahbaba, Babak|Sassone-Corsi, Paolo|Baldi, Pierre', 'Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.|Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.|Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.|Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.|Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw504', '27542773', '-'),
(80233, 2016, 'bio', 'Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks', 'Gao, Jianzhao|Yang, Yuedong|Zhou, Yaoqi', 'School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Peoples Republic of China.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD 4222, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD 4222, Australia.', 'China|Australia|Australia', 9, 'Amino Acids|Computational Biology|Neural Networks (Computer)|Protein Structure, Secondary|Proteins|Solvents', 'Journal Article', '10.1093/bioinformatics/btw549', '27551104', 'Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibility states. However, lacking the confidence score for predicted values has limited their applications. Here we investigated whether or not we can make a reasonable prediction of absolute errors for predicted backbone torsion angles, Cxcexb1-atom-based angles and torsion angles, solvent accessibility, contact numbers and half-sphere exposures by employing deep neural networks.'),
(80234, 2016, 'bio', 'Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank', 'Prlic, Andreas|Kalro, Tara|Bhattacharya, Roshni|Christie, Cole|Burley, Stephen K|Rose, Peter W', 'RCSB Protein Data Bank, University of California San Diego, San Diego Supercomputer Center, La Jolla, CA 92093, USA.|RCSB Protein Data Bank, University of California San Diego, San Diego Supercomputer Center, La Jolla, CA 92093, USA.|Bioinformatics and Medical Informatics, San Diego State University, San Diego, CA 92182, USA.|RCSB Protein Data Bank, University of California San Diego, San Diego Supercomputer Center, La Jolla, CA 92093, USA.|RCSB Protein Data Bank, University of California San Diego, San Diego Supercomputer Center, La Jolla, CA 92093, USA.|RCSB Protein Data Bank, University of California San Diego, San Diego Supercomputer Center, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States', 8, 'Amino Acid Sequence|Computational Biology|Computer Graphics|Databases, Protein|Genomics|Humans|Protein Conformation|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw547', '27551105', 'The Protein Data Bank (PDB) now contains more than 120,000 three-dimensional (3D) structures of biological macromolecules. To allow an interpretation of how PDB data relates to other publicly available annotations, we developed a novel data integration platform that maps 3D structural information across various datasets. This integration bridges from the human genome across protein sequence to 3D structure space. We developed novel software solutions for data management and visualization, while incorporating new libraries for web-based visualization using SVG graphics.'),
(80235, 2016, 'bio', 'PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets', 'Yu, Jinchao|Guerois, Raphaël', 'Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France.|Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France.', 'France|France', 5, 'Algorithms|Computational Biology|Molecular Docking Simulation|Protein Binding|Proteins', 'Journal Article', '10.1093/bioinformatics/btw533', '27551106', 'Protein-protein docking methods are of great importance for understanding interactomes at the structural level. It has become increasingly appealing to use not only experimental structures but also homology models of unbound subunits as input for docking simulations. So far we are missing a large scale assessment of the success of rigid-body free docking methods on homology models.'),
(80236, 2016, 'bio', 'Discoal: flexible coalescent simulations with selection', 'Kern, Andrew D|Schrider, Daniel R', 'Department of Genetics.|Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ 08554, USA.', 'United States|Jersey', 21, 'Alleles|Computational Biology|Computer Simulation|Gene Frequency|Genetics, Population|Models, Genetic|Mutation|Programming Languages|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btw556', '27559153', 'Here we describe discoal, a coalescent simulator able to generate population samples that include selective sweeps in a feature-rich, flexible manner. discoal can perform simulations conditioning on the fixation of an allele due to drift or either hard or soft sweeps-even those occurring a large genetic distance away from the simulated locus. discoal can simulate sweeps with recurrent mutation to the adaptive allele, recombination, and gene conversion, under non-equilibrium demographic histories and without specifying an allele frequency trajectory in advance.'),
(80237, 2017, 'bio', 'Cas-analyzer: an online tool for assessing genome editing results using NGS data', 'Park, Jeongbin|Lim, Kayeong|Kim, Jin-Soo|Bae, Sangsu', 'Department of Chemistry, Hanyang University, Seoul, South Korea.|Center for Genome Engineering, Institute for Basic Science, Seoul, South Korea.|Center for Genome Engineering, Institute for Basic Science, Seoul, South Korea.|Department of Chemistry, Hanyang University, Seoul, South Korea.', '-|-|-|-', 0, 'CRISPR-Cas Systems|Endonucleases|Gene Editing|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw561', '27559154', 'Genome editing with programmable nucleases has been widely adopted in research and medicine. Next generation sequencing (NGS) platforms are now widely used for measuring the frequencies of mutations induced by CRISPR-Cas9 and other programmable nucleases. Here, we present an online tool, Cas-Analyzer, a JavaScript-based implementation for NGS data analysis. Because Cas-Analyzer is completely used at a client-side web browser on-the-fly, there is no need to upload very large NGS datasets to a server, a time-consuming step in genome editing analysis. Currently, Cas-Analyzer supports various programmable nucleases, including single nucleases and paired nucleases.'),
(80238, 2016, 'bio', 'Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models', 'Saa, Pedro A|Nielsen, Lars K', 'Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Rds (Bldg 75), Australia.|Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Rds (Bldg 75), Australia.', 'Australia|Australia', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Programming, Linear|Software', 'Journal Article', '10.1093/bioinformatics/btw555', '27559155', 'Computation of steady-state flux solutions in large metabolic models is routinely performed using flux balance analysis based on a simple LP (Linear Programming) formulation. A minimal requirement for thermodynamic feasibility of the flux solution is the absence of internal loops, which are enforced using loopless constraints. The resulting loopless flux problem is a substantially harder MILP (Mixed Integer Linear Programming) problem, which is computationally expensive for large metabolic models.'),
(80239, 2016, 'bio', 'KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure', 'Li, Qiwei|Dahl, David B|Vannucci, Marina|Joo, Hyun|Tsai, Jerry W', 'Department of Statistics, Rice University, Houston, TX, USA.|Department of Statistics, Brigham Young University, Provo, UT, USA.|Department of Statistics, Rice University, Houston, TX, USA.|Department of Chemistry, University of the Pacific, Stockton, CA, USA.|Department of Chemistry, University of the Pacific, Stockton, CA, USA.', 'United States|United States|United States|United States|United States', 1, 'Algorithms|Amino Acids|Bayes Theorem|Computational Biology|Models, Molecular|Models, Statistical|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btw553', '27559156', 'By simplifying the many-bodied complexity of residue packing into patterns of simple pairwise secondary structure interactions between a single knob residue with a three-residue socket, the knob-socket construct allows a more direct incorporation of structural information into the prediction of residue contacts. By modeling the preferences between the amino acid composition of a socket and knob, we undertake an investigation of the knob-socket constructs ability to improve the prediction of residue contacts. The statistical model considers three priors and two posterior estimations to better understand how the input data affects predictions. This produces six implementations of KScons that are tested on three sets: PSICOV, CASP10 and CASP11. We compare against the current leading contact prediction methods.'),
(80240, 2016, 'bio', 'PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations', 'Soul, Jamie|Dunn, Sara L|Hardingham, Tim E|Boot-Handford, Ray P|Schwartz, Jean-Marc', 'Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.|Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.|Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.|Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.|Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Animals|Disease Models, Animal|Gene Expression|Humans|Phenotype|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btw545', '27559157', 'PhenomeScape is a Cytoscape app which provides easy access to the PhenomeExpress algorithm to interpret gene expression data. PhenomeExpress integrates protein interaction networks with known phenotype to gene associations to find active sub-networks enriched in differentially expressed genes. It also incorporates cross-species phenotypes and associations to include results from animal models of disease. With expression data imported into PhenomeScape, the user can quickly generate and visualise interactive sub-networks. PhenomeScape thus enables researchers to use prior knowledge of a disease to identify differentially regulated sub-networks and to generate an overview of altered biologically processes specific to that disease.'),
(80241, 2016, 'bio', 'AlmostSignificant: simplifying quality control of high-throughput sequencing data', 'Ward, Joseph|Cole, Christian|Febrer, Melanie|Barton, Geoffrey J', 'Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.|Molecular Medicine, School of Life Sciences, University of Dundee, Dundee, DD1 5RH, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Base Sequence|Computational Biology|Data Accuracy|High-Throughput Nucleotide Sequencing|Quality Control|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btw559', '27559158', 'The current generation of DNA sequencing technologies produce a large amount of data quickly. All of these data need to pass some form of quality control (QC) processing and checking before they can be used for any analysis. The large number of samples that are run through Illumina sequencing machines makes the process of QC an onerous and time-consuming task that requires multiple pieces of information from several sources.'),
(80242, 2016, 'bio', 'Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with VDJer', 'Mose, Lisle E|Selitsky, Sara R|Bixby, Lisa M|Marron, David L|Iglesia, Michael D|Serody, Jonathan S|Perou, Charles M|Vincent, Benjamin G|Parker, Joel S', 'Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.|Curriculum in Genetics and Molecular Biology.|Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.|Lineberger Comprehensive Cancer Center.', '-|-|-|-|-|-|-|-|-', 27, 'B-Lymphocytes|Computational Biology|Humans|Receptors, Antigen, B-Cell|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw526', '27559159', 'B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not.'),
(80243, 2017, 'bio', 'LoFtool: a gene intolerance score based on loss-of-function variants in 60 706 individuals', 'Fadista, Joxc3xa3o|Oskolkov, Nikolay|Hansson, Ola|Groop, Leif', 'Department of Epidemiology Research, Statens Serum Institut, 2300 Copenhagen S, Denmark.|Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Sweden and.|Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Sweden and.|Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Sweden and.', 'Denmark|Sweden|Sweden|Sweden', 17, 'Brain|Genetic Diseases, Inborn|Genome, Human|Genomics|Humans|Loss of Function Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btv602', '27563026', 'Depletion of loss-of-function (LoF) mutations may provide a rank of genic functional intolerance and consequently susceptibility to disease.'),
(80244, 2017, 'bio', 'Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition', 'Awazu, Akinori', 'Department of Mathematical and Life Sciences.', '-', 0, 'Animals|Base Sequence|Caenorhabditis elegans|Chromatin Assembly and Disassembly|DNA|Drosophila melanogaster|Humans|Models, Genetic|Nucleosomes|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btw562', '27563027', 'Nucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes.'),
(80245, 2017, 'bio', 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition', 'Zuo, Yongchun|Li, Yuan|Chen, Yingli|Li, Guangpeng|Yan, Zhenhe|Yang, Lei', 'The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China.|The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China.|School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China.|The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China.|The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.', 'China|China|China|China|China|China', 60, 'Algorithms|Internet|Proteins|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw564', '27565583', 'The reduced amino acids perform powerful ability for both simplifying protein complexity and identifying functional conserved regions. However, dealing with different protein problems may need different kinds of cluster methods. Encouraged by the success of pseudo-amino acid composition algorithm, we developed a freely available web server, called PseKRAAC (the pseudo K-tuple reduced amino acids composition). By implementing reduced amino acid alphabets, the protein complexity can be significantly simplified, which leads to decrease chance of overfitting, lower computational handicap and reduce information redundancy. PseKRAAC delivers more capability for protein research by incorporating three crucial parameters that describes protein composition. Users can easily generate many different modes of PseKRAAC tailored to their needs by selecting various reduced amino acids alphabets and other characteristic parameters. It is anticipated that the PseKRAAC web server will become a very useful tool in computational proteomics and protein sequence analysis.'),
(80246, 2017, 'bio', 'A likelihood ratio-based method to predict exact pedigrees for complex families from next-generation sequencing data', 'Heinrich, Verena|Kamphans, Tom|Mundlos, Stefan|Robinson, Peter N|Krawitz, Peter M', 'Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin.|Smart Algos, Berlin, Germany.|Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin.|Institute for Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin.|Institute for Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin.', '-|Germany|-|-|-', 0, 'Algorithms|Exome|Female|Genetic Linkage|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Male|Mutation|Pedigree|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw550', '27565584', 'Next generation sequencing technology considerably changed the way we screen for pathogenic mutations in rare Mendelian disorders. However, the identification of the disease-causing mutation amongst thousands of variants of partly unknown relevance is still challenging and efficient techniques that reduce the genomic search space play a decisive role. Often segregation- or linkage analysis are used to prioritize candidates, however, these approaches require correct information about the degree of relationship among the sequenced samples. For quality assurance an automated control of pedigree structures and sample assignment is therefore highly desirable in order to detect label mix-ups that might otherwise corrupt downstream analysis.'),
(80247, 2016, 'bio', 'Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types', 'Lin, Weizhong|Xu, Dong', 'nformation Engineering School, Jingdezhen Ceramic Institute, Jingdezhen 333406, China.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.', 'China|United States', 29, 'Algorithms|Amino Acids|Antimicrobial Cationic Peptides|Computational Biology|Internet|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btw560', '27565585', 'With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional types has led to many studies, and various predictors using machine learning have been developed. However, there is room for improvement, in particular, no predictor takes into account the lack of balance among different functional AMPs.'),
(80248, 2016, 'bio', 'A statistical method for the detection of variants from next-generation resequencing of DNA pools', 'Bansal, Vikas|Libiger, Ondrej', 'Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw520', '27578802', '-'),
(80249, 2016, 'bio', 'Virtual exploration of early stage atherosclerosis', 'Olivares, Andy L|González Ballester, Miguel A|Noailly, Jérôme', 'Institute for Bioengineering of Catalonia (IBEC), 08028 Barcelona, Spain.|Department of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain.|Institute for Bioengineering of Catalonia (IBEC), 08028 Barcelona, Spain.', 'Spain|Spain|Spain', 4, 'Atherosclerosis|Humans|Hydroxymethylglutaryl-CoA Reductase Inhibitors|Hypercholesterolemia|Lipoproteins, LDL|Models, Biological|Oxidation-Reduction|Software', 'Journal Article', '10.1093/bioinformatics/btw551', '27578803', 'Biological mechanisms contributing to atherogenesis are multiple and complex. The early stage of atherosclerosis (AS) is characterized by the accumulation of low-density lipoprotein (LDL) droplets, leading to the creation of foam cells (FC). To address the difficulty to explore the dynamics of interactions that controls this process, this study aimed to develop a model of agents and infer on the most influential cell- and molecule-related parameters.'),
(80250, 2017, 'bio', 'FATSLiM: a fast and robust software to analyze MD simulations of membranes', 'Buchoux, Sébastien', 'Laboratory of Enzyme and Cell Engineering (GEC - FRE3580 CNRS/UPJV/UTC), Université de Picardie Jules Verne, Amiens, France.', 'France', 33, 'Membrane Lipids|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btw563', '27578804', 'When studying biological membranes, Molecular Dynamics (MD) simulations reveal to be quite complementary to experimental techniques. Because the simulated systems keep increasing both in size and complexity, the analysis of MD trajectories need to be computationally efficient while being robust enough to perform analysis on membranes that may be curved or deformed due to their size and/or protein-lipid interactions. This work presents a new software named FATSLiM (Fast Analysis Toolbox for Simulations of Lipid Membranes) that can extract physical properties from MD simulations of membranes (with or without interacting proteins). Because it relies on the calculation of local normals, FATSLiM does not depend of the bilayer morphology and thus can handle with the same accuracy vesicles for instance. Thanks to an efficiency-driven development, it is also fast and consumes a rather low amount of memory.'),
(80251, 2016, 'bio', 'ECCB 2016: The 15th European Conference on Computational Biology', 'ECCB 2016 Local Steering Committee|Heringa, Jaap|Reinders, Marcel|Abeln, Sanne|de Ridder, Jeroen', 'Laboratory of Enzyme and Cell Engineering (GEC - FRE3580 CNRS/UPJV/UTC), Université de Picardie Jules Verne, Amiens, France.|Vrije Universiteit, Amsterdam, The Netherlands.|Delft University of Technology, Delft, The Netherlands.|Vrije Universiteit, Amsterdam, The Netherlands.|University Medical Centre Utrecht, The Netherlands.', 'France|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Editorial', '10.1093/bioinformatics/btw481', '27587653', '-'),
(80252, 2016, 'bio', 'Estimating real cell size distribution from cross-section microscopy imaging', 'Lenz, Michael|Roumans, Nadia J T|Vink, Roel G|van Baak, Marleen A|Mariman, Edwin C M|Arts, Ilja C W|de Kok, Theo M|Ertaylan, Gökhan', 'Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.|Department of Human Biology NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.|Department of Human Biology NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.|Department of Human Biology NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.|Department of Human Biology NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.|Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.|Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.|Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Adipose Tissue|Algorithms|Cell Size|Diet|Humans|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btw431', '27587655', 'Microscopy imaging is an essential tool for medical diagnosis and molecular biology. It is particularly useful for extracting information about disease states, tissue heterogeneity and cell specific parameters such as cell type or cell size from biological specimens. However, the information obtained from the images is likely to be subjected to sampling and observational bias with respect to the underlying cell size/type distributions.'),
(80253, 2016, 'bio', 'Higher order methylation features for clustering and prediction in epigenomic studies', 'Kapourani, Chantriolnt-Andreas|Sanguinetti, Guido', 'IANC, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.|IANC, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3JD, UK.', 'United Kingdom|United Kingdom', 11, 'CpG Islands|DNA Methylation|Epigenomics|Forecasting|Gene Expression|Genome|Machine Learning|Promoter Regions, Genetic', 'Journal Article', '10.1093/bioinformatics/btw432', '27587656', 'DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands.'),
(80254, 2016, 'bio', 'TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types', 'Aben, Nanne|Vis, Daniel J|Michaut, Magali|Wessels, Lodewyk F A', 'Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands, Faculty of EEMCS, Delft University of Technology, Delft 2628CD, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands, Faculty of EEMCS, Delft University of Technology, Delft 2628CD, The Netherlands Cancer Genomics Netherlands, Utrecht 3584CT, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Cell Line|DNA Damage|Drug Delivery Systems|Gene Dosage|Gene Expression|Gene Expression Profiling|Humans|Mutation|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw449', '27587657', 'Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways.');
