INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80255, 2016, 'bio', 'Complementary feature selection from alternative splicing events and gene expression for phenotype prediction', 'Labuzzetta, Charles J|Antonio, Margaret L|Watson, Patricia M|Wilson, Robert C|Laboissonniere, Lauren A|Trimarchi, Jeffrey M|Genc, Baris|Ozdinler, P Hande|Watson, Dennis K|Anderson, Paul E', 'Department of Mathematics, Iowa State University, Ames, IA 50011, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.|Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.|Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.|Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.|Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.|Department of Computer Science, College of Charleston, Charleston, SC 29424, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Alternative Splicing|Amyotrophic Lateral Sclerosis|Animals|Carcinoma, Non-Small-Cell Lung|Humans|Lung Neoplasms|Machine Learning|Mice, Transgenic|Phenotype|Pulmonary Disease, Chronic Obstructive', 'Journal Article', '10.1093/bioinformatics/btw430', '27587658', 'A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from alternative splicing, may provide a novel and complementary set of transcripts for phenotype prediction. In contrast to gene expression, the number of isoforms increases significantly due to numerous alternative splicing patterns, resulting in a prioritization problem for many machine learning algorithms. This study identifies the empirically optimal methods of transcript quantification, feature engineering and filtering steps using phenotype prediction accuracy as a metric. At the same time, the complementary nature of gene and isoform data is analyzed and the feasibility of identifying isoforms as biomarker candidates is examined.'),
(80256, 2016, 'bio', 'Combining dependent P-values with an empirical adaptation of Browns method', 'Poole, William|Gibbs, David L|Shmulevich, Ilya|Bernard, Brady|Knijnenburg, Theo A', 'Institute for Systems Biology, Seattle, WA 98109-5263, USA.|Institute for Systems Biology, Seattle, WA 98109-5263, USA.|Institute for Systems Biology, Seattle, WA 98109-5263, USA.|Institute for Systems Biology, Seattle, WA 98109-5263, USA.|Institute for Systems Biology, Seattle, WA 98109-5263, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|High-Throughput Screening Assays|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw438', '27587659', 'Combining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here, we discuss an empirical adaptation of Browns method (an extension of Fishers method) for combining dependent P-values which is appropriate for the large and correlated datasets found in high-throughput biology.'),
(80257, 2016, 'bio', 'Causality modeling for directed disease network', 'Bang, Sunjoo|Kim, Jae-Hoon|Shin, Hyunjung', 'Department of Industrial Engineering, Ajou University, Wonchun-Dong, Yeongtong-Gu, Suwon 443-749, South Korea.|Department of Industrial Engineering, Ajou University, Wonchun-Dong, Yeongtong-Gu, Suwon 443-749, South Korea.|Department of Industrial Engineering, Ajou University, Wonchun-Dong, Yeongtong-Gu, Suwon 443-749, South Korea.', '-|-|-', 7, 'Algorithms|Causality|Computational Biology|Databases, Factual|Gene Expression Profiling|History, Medieval|Humans|Information Storage and Retrieval|Knowledge Bases|Metabolic Networks and Pathways|Neural Networks (Computer)', 'Historical Article', '10.1093/bioinformatics/btw439', '27587660', 'Causality between two diseases is valuable information as subsidiary information for medicine which is intended for prevention, diagnostics and treatment. Conventional cohort-centric researches are able to obtain very objective results, however, they demands costly experimental expense and long period of time. Recently, data source to clarify causality has been diversified: available information includes gene, protein, metabolic pathway and clinical information. By taking full advantage of those pieces of diverse information, we may extract causalities between diseases, alternatively to cohort-centric researches.'),
(80258, 2016, 'bio', 'Simultaneous discovery of cancer subtypes and subtype features by molecular data integration', 'Le Van, Thanh|van Leeuwen, Matthijs|Carolina Fierro, Ana|De Maeyer, Dries|Van den Eynden, Jimmy|Verbeke, Lieven|De Raedt, Luc|Marchal, Kathleen|Nijssen, Siegfried', 'Department of Computer Science, KULeuven, Leuven, Belgium.|Leiden Institute for Advanced Computer Science, Universiteit Leiden, Leiden, The Netherlands.|Department of Information Technology, iMinds, Ghent University, Gent, Belgium, Bioinformatics Institute Ghent, 9052 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium.|Department of Information Technology, iMinds, Ghent University, Gent, Belgium, Bioinformatics Institute Ghent, 9052 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium.|Department of Medical Biochemisty and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.|Department of Information Technology, iMinds, Ghent University, Gent, Belgium, Bioinformatics Institute Ghent, 9052 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium.|Department of Computer Science, KULeuven, Leuven, Belgium.|Department of Information Technology, iMinds, Ghent University, Gent, Belgium, Bioinformatics Institute Ghent, 9052 Gent, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium Department of Genetics, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa.|Department of Computer Science, KULeuven, Leuven, Belgium, Leiden Institute for Advanced Computer Science, Universiteit Leiden, Leiden, The Netherlands.', 'Belgium|Netherlands|Belgium|Belgium|Sweden|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Breast Neoplasms|Carcinogenesis|Cluster Analysis|Genetic Association Studies|Humans|Mutation|Prognosis', 'Journal Article', '10.1093/bioinformatics/btw434', '27587661', 'Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates` mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering'),
(80259, 2016, 'bio', 'Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization', 'Ammad-Ud-Din, Muhammad|Khan, Suleiman A|Malani, Disha|Murumägi, Astrid|Kallioniemi, Olli|Aittokallio, Tero|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo 02150, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki 00290, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki 00290, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki 00290, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki 00290, Finland Science for Life Laboratory Department of Oncology and Pathology, Karolinska Institutet, Solna 17165, Sweden.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki 00290, Finland Department of Mathematics and Statistics, University of Turku, Turku 20014, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo 02150, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Bayes Theorem|Drug Delivery Systems|Drug Discovery|Genomics|Humans|Metabolic Networks and Pathways|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw433', '27587662', 'A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses.'),
(80260, 2016, 'bio', 'Mutual enrichment in aggregated ranked lists with applications to gene expression regulation', 'Cohn-Alperovich, Dalia|Rabner, Alona|Kifer, Ilona|Mandel-Gutfreund, Yael|Yakhini, Zohar', 'Computer Science Department, Technion - Israel Institute of Technology, Haifa 3200003, Israel, Microsoft Research and Development Center, Haifa and Herzeliya, Israel.|Department of Biology, Technion - Israel Institute of Technology, Haifa 3200003, Israel.|Microsoft Research and Development Center, Haifa and Herzeliya, Israel.|Department of Biology, Technion - Israel Institute of Technology, Haifa 3200003, Israel.|Computer Science Department, Technion - Israel Institute of Technology, Haifa 3200003, Israel, School of Computer Science, The Interdisciplinary Center, Herzeliya 4610101, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Computational Biology|Factor Analysis, Statistical|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|Models, Statistical|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw435', '27587663', 'It is often the case in biological measurement data that results are given as a ranked list of quantities-for example, differential expression (DE) of genes as inferred from microarrays or RNA-seq. Recent years brought considerable progress in statistical tools for enrichment analysis in ranked lists. Several tools are now available that allow users to break the fixed set paradigm in assessing statistical enrichment of sets of genes. Continuing with the example, these tools identify factors that may be associated with measured differential expression. A drawback of existing tools is their focus on identifying single factors associated with the observed or measured ranks, failing to address relationships between these factors. For example, a scenario in which genes targeted by multiple miRNAs play a central role in the DE signal but the effect of each single miRNA is too subtle to be detected, as shown in our results.'),
(80261, 2016, 'bio', 'The Network Library: a framework to rapidly integrate network biology resources', 'Summer, Georg|Kelder, Thomas|Radonjic, Marijana|van Bilsen, Marc|Wopereis, Suzan|Heymans, Stephane', 'CARIM, Maastricht University, Maastricht, The Netherlands TNO, Zeist, The Netherlands.|EdgeLeap B.V, Utrecht, The Netherlands.|EdgeLeap B.V, Utrecht, The Netherlands.|CARIM, Maastricht University, Maastricht, The Netherlands.|TNO, Zeist, The Netherlands.|CARIM, Maastricht University, Maastricht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Databases, Factual|Epistasis, Genetic|Heart Failure|Humans|Knowledge Bases|Software', 'Journal Article', '10.1093/bioinformatics/btw436', '27587664', 'Much of the biological knowledge accumulated over the last decades is stored in different databases governed by various organizations and institutes. Integrating and connecting these vast knowledge repositories is an extremely useful method to support life sciences research and help formulate novel hypotheses.'),
(80262, 2016, 'bio', 'GTRAC: fast retrieval from compressed collections of genomic variants', 'Tatwawadi, Kedar|Hernaez, Mikel|Ochoa, Idoia|Weissman, Tsachy', 'Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Data Compression|Databases, Genetic|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw437', '27587665', 'The dramatic decrease in the cost of sequencing has resulted in the generation of huge amounts of genomic data, as evidenced by projects such as the UK10K and the Million Veteran Project, with the number of sequenced genomes ranging in the order of 10 K to 1 M. Due to the large redundancies among genomic sequences of individuals from the same species, most of the medical research deals with the variants in the sequences as compared with a reference sequence, rather than with the complete genomic sequences. Consequently, millions of genomes represented as variants are stored in databases. These databases are constantly updated and queried to extract information such as the common variants among individuals or groups of individuals. Previous algorithms for compression of this type of databases lack efficient random access capabilities, rendering querying the database for particular variants and/or individuals extremely inefficient, to the point where compression is often relinquished altogether.'),
(80263, 2016, 'bio', 'PanTools: representation, storage and exploration of pan-genomic data', 'Sheikhizadeh, Siavash|Schranz, M Eric|Akdel, Mehmet|de Ridder, Dick|Smit, Sandra', 'Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.|Biosystematics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, The Netherlands.|Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 10, 'Algorithms|Arabidopsis|Computational Biology|Escherichia coli|Genome|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw455', '27587666', 'Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability.'),
(80264, 2016, 'bio', 'Information-optimal genome assembly via sparse read-overlap graphs', 'Shomorony, Ilan|Kim, Samuel H|Courtade, Thomas A|Tse, David N C', 'Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA.|Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA.|Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA.|Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA Department of Electrical Engineering, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States', 7, 'Algorithms|Computational Biology|Genome|Genome, Bacterial|Metagenomics|Models, Theoretical|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw450', '27587667', 'In the context of third-generation long-read sequencing technologies, read-overlap-based approaches are expected to play a central role in the assembly step. A fundamental challenge in assembling from a read-overlap graph is that the true sequence corresponds to a Hamiltonian path on the graph, and, under most formulations, the assembly problem becomes NP-hard, restricting practical approaches to heuristics. In this work, we avoid this seemingly fundamental barrier by first setting the computational complexity issue aside, and seeking an algorithm that targets information limits In particular, we consider a basic feasibility question: when does the set of reads contain enough information to allow unambiguous reconstruction of the true sequence?'),
(80265, 2016, 'bio', 'A program for verification of phylogenetic network models', 'Gunawan, Andreas D M|Lu, Bingxin|Zhang, Louxin', 'Department of Mathematics.|Department of Computer Science, National University of Singapore, Singapore 117417, Singapore.|Department of Mathematics.', '-|Singapore|-', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Genome|Models, Genetic|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw467', '27587668', 'Genetic material is transferred in a non-reproductive manner across species more frequently than commonly thought, particularly in the bacteria kingdom. On one hand, extant genomes are thus more properly considered as a fusion product of both reproductive and non-reproductive genetic transfers. This has motivated researchers to adopt phylogenetic networks to study genome evolution. On the other hand, a genes evolution is usually tree-like and has been studied for over half a century. Accordingly, the relationships between phylogenetic trees and networks are the basis for the reconstruction and verification of phylogenetic networks. One important problem in verifying a network model is determining whether or not certain existing phylogenetic trees are displayed in a phylogenetic network. This problem is formally called the tree containment problem. It is NP-complete even for binary phylogenetic networks.'),
(80266, 2016, 'bio', 'LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs', 'Äijö, Tarmo|Yue, Xiaojing|Rao, Anjana|Lähdesmäki, Harri', 'Center for Computational Biology, Simons Foundation, New York, NY 10010, USA Department of Computer Science, Aalto University School of Science, Aalto FI-00076, Finland.|La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA Department of Pharmacology and Moores Cancer Center, University of California, La Jolla, CA 92037, USA Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA.|Department of Computer Science, Aalto University School of Science, Aalto FI-00076, Finland.', 'Finland|United States|United States|Finland', 6, '5-Methylcytosine|Cytosine|DNA|DNA Methylation|DNA Repair|DNA-Binding Proteins|Forkhead Transcription Factors|Humans|Oxidation-Reduction|Research Design|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw468', '27587669', '5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem.'),
(80267, 2016, 'bio', 'Assemble CRISPRs from metagenomic sequencing data', 'Lei, Jikai|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 2, 'Bacteriophages|Clustered Regularly Interspaced Short Palindromic Repeats|Genetic Variation|Metagenomics|Prokaryotic Cells', 'Journal Article', '10.1093/bioinformatics/btw456', '27587670', 'Clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR-Cas) allows more specific and efficient gene editing than all previous genetic engineering systems. These exciting discoveries stem from the finding of the CRISPR system being an adaptive immune system that protects the prokaryotes against exogenous genetic elements such as phages. Despite the exciting discoveries, almost all knowledge about CRISPRs is based only on microorganisms that can be isolated, cultured and sequenced in labs. However, about 95% of bacterial species cannot be cultured in labs. The fast accumulation of metagenomic data, which contains DNA sequences of microbial species from natural samples, provides a unique opportunity for CRISPR annotation in uncultivable microbial species. However, the large amount of data, heterogeneous coverage and shared leader sequences of some CRISPRs pose challenges for identifying CRISPRs efficiently in metagenomic data.'),
(80268, 2016, 'bio', 'Improve homology search sensitivity of PacBio data by correcting frameshifts', 'Du, Nan|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 0, 'Databases, Genetic|Frameshift Mutation|Molecular Sequence Annotation|Protein Isoforms|Sequence Alignment|Sequence Analysis, DNA|Sequence Deletion|Sequence Homology|Software', 'Journal Article', '10.1093/bioinformatics/btw458', '27587671', 'Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than secondary generation sequencing technologies such as Illumina. The long read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and identify gene isoforms with higher accuracy in transcriptomic sequencing. However, PacBio data has high sequencing error rate and most of the errors are insertion or deletion errors. During alignment-based homology search, insertion or deletion errors in genes will cause frameshifts and may only lead to marginal alignment scores and short alignments. As a result, it is hard to distinguish true alignments from random alignments and the ambiguity will incur errors in structural and functional annotation. Existing frameshift correction tools are designed for data with much lower error rate and are not optimized for PacBio data. As an increasing number of groups are using SMRT, there is an urgent need for dedicated homology search tools for PacBio data.'),
(80269, 2016, 'bio', 'Fast genotyping of known SNPs through approximate k-mer matching', 'Shajii, Ariya|Yorukoglu, Deniz|William Yu, Yun|Berger, Bonnie', 'Department of Electrical & Computer Engineering, Boston University, Boston, MA 02215, USA.|Computer Science and AI Lab.|Computer Science and AI Lab Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Computer Science and AI Lab Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States', 16, 'Algorithms|Alleles|Cluster Analysis|Genome, Human|Genotype|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw460', '27587672', 'As the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS).'),
(80270, 2016, 'bio', 'CoLoRMap: Correcting Long Reads by Mapping short reads', 'Haghshenas, Ehsan|Hach, Faraz|Sahinalp, S Cenk|Chauve, Cedric', 'School of Computing Sciences MADD-Gen Graduate Program, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|School of Computing Sciences Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|School of Computing Sciences Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada, School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.|Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.', 'Canada|Canada|Canada|Canada', 20, 'Algorithms|Computational Biology|Genome|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw463', '27587673', 'Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.'),
(80271, 2016, 'bio', 'Optimization of signal-to-noise ratio for efficient microarray probe design', 'Matveeva, Olga V|Nechipurenko, Yury D|Riabenko, Evgeniy|Ragan, Chikako|Nazipova, Nafisa N|Ogurtsov, Aleksey Y|Shabalina, Svetlana A', 'Biopolymer Design LLC, Acton, MA 01721, USA Engelhardt Institute of Molecular Biology, Moscow 119991, Russia.|Engelhardt Institute of Molecular Biology, Moscow 119991, Russia.|Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia.|Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072 Australia.|Institute of Mathematical Problems of Biology, Pushchino, Moscow Region, 142290, Russia.|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|Australia|United States|United States|United States', 6, 'DNA Probes|Gene Expression Profiling|Genome|Genomics|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Sensitivity and Specificity|Signal-To-Noise Ratio', 'Journal Article', '10.1093/bioinformatics/btw451', '27587674', 'Target-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis.'),
(80272, 2016, 'bio', 'Extending partial haplotypes to full genome haplotypes using chromosome conformation capture data', 'Ben-Elazar, Shay|Chor, Benny|Yakhini, Zohar', 'Department of Computer Science, Tel-Aviv University, Israel Microsoft R&D, HerzlyiaIsrael.|Department of Computer Science, Tel-Aviv University, Israel.|Agilent Laboratories, Tel-Aviv, Israel Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel School of computer science, Herzeliya Interdisciplinary Center.', 'Israel|Israel|Israel', 12, 'Algorithms|Chromosomes|Genetic Variation|Genome, Human|Genome-Wide Association Study|Haplotypes|Humans|Molecular Conformation', 'Journal Article', '10.1093/bioinformatics/btw453', '27587675', 'Complex interactions among alleles often drive differences in inherited properties including disease predisposition. Isolating the effects of these interactions requires phasing information that is difficult to measure or infer. Furthermore, prevalent sequencing technologies used in the essential first step of determining a haplotype limit the range of that step to the span of reads, namely hundreds of bases. With the advent of pseudo-long read technologies, observable partial haplotypes can span several orders of magnitude more. Yet, measuring whole-genome-single-individual haplotypes remains a challenge. A different view of whole genome measurement addresses the 3D structure of the genome-with great development of Hi-C techniques in recent years. A shortcoming of current Hi-C, however, is the difficulty in inferring information that is specific to each of a pair of homologous chromosomes.'),
(80273, 2016, 'bio', 'MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures', 'Girotto, Samuele|Pizzi, Cinzia|Comin, Matteo', 'Department of Information Engineering, University of Padova, Padova, Italy.|Department of Information Engineering, University of Padova, Padova, Italy.|Department of Information Engineering, University of Padova, Padova, Italy.', 'Italy|Italy|Italy', 25, 'Algorithms|Cluster Analysis|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw466', '27587676', 'Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Taxonomic analysis of microbial communities, a process referred to as binning, is one of the most challenging tasks when analyzing metagenomic reads data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species and the limitations due to short read lengths and sequencing errors.'),
(80274, 2016, 'bio', 'Characterizing leader sequences of CRISPR loci', 'Alkhnbashi, Omer S|Shah, Shiraz A|Garrett, Roger A|Saunders, Sita J|Costa, Fabrizio|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.|Archaea Centre, Department of Biology, University of Copenhagen N, DK2200 Copenhagen N, Denmark.|Archaea Centre, Department of Biology, University of Copenhagen N, DK2200 Copenhagen N, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany BIOSS Centre for Biological Signalling Studies, Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.', 'Germany|Denmark|Denmark|Germany|Germany|Germany', 24, 'Archaea|Base Sequence|CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Conserved Sequence|Genetic Loci|Molecular Sequence Annotation|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btw454', '27587677', 'The CRISPR-Cas system is an adaptive immune system in many archaea and bacteria, which provides resistance against invading genetic elements. The first phase of CRISPR-Cas immunity is called adaptation, in which small DNA fragments are excised from genetic elements and are inserted into a CRISPR array generally adjacent to its so called leader sequence at one end of the array. It has been shown that transcription initiation and adaptation signals of the CRISPR array are located within the leader. However, apart from promoters, there is very little knowledge of sequence or structural motifs or their possible functions. Leader properties have mainly been characterized through transcriptional initiation data from single organisms but large-scale characterization of leaders has remained challenging due to their low level of sequence conservation.'),
(80275, 2016, 'bio', 'Pathway-based approach using hierarchical components of collapsed rare variants', 'Lee, Sungyoung|Choi, Sungkyoung|Kim, Young Jin|Kim, Bong-Jo|T2d-Genes Consortium|Hwang, Heungsun|Park, Taesung', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.|Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea.|Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea.|Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea.|Department of Psychology, McGill University, Montreal, QC H3A 1B1, Canada.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea Department of Statistics, Seoul National University, Seoul 151-747, Korea.', '-|-|-|-|-|Canada|-', 14, 'Computational Biology|Computer Simulation|Databases, Factual|Exome|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Liver|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1093/bioinformatics/btw425', '27587678', 'To address missing heritability issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates, hence our method considers the correlation of pathways without constraint by a multiple testing problem.'),
(80276, 2016, 'bio', 'Detecting horizontal gene transfer by mapping sequencing reads across species boundaries', 'Trappe, Kathrin|Marschall, Tobias|Renard, Bernhard Y', 'Robert Koch Institute, Research Group Bioinformatics (NG4), 13353 Berlin, Germany.|Center for Bioinformatics, Saarland University, and Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.|Robert Koch Institute, Research Group Bioinformatics (NG4), 13353 Berlin, Germany.', 'Germany|Germany|Germany', 7, 'Chromosome Mapping|Computational Biology|Gene Transfer, Horizontal|Genome|High-Throughput Nucleotide Sequencing|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btw423', '27587679', 'Horizontal gene transfer (HGT) is a fundamental mechanism that enables organisms such as bacteria to directly transfer genetic material between distant species. This way, bacteria can acquire new traits such as antibiotic resistance or pathogenic toxins. Current bioinformatics approaches focus on the detection of past HGT events by exploring phylogenetic trees or genome composition inconsistencies. However, these techniques normally require the availability of finished and fully annotated genomes and of sufficiently large deviations that allow detection and are thus not widely applicable. Especially in outbreak scenarios with HGT-mediated emergence of new pathogens, like the enterohemorrhagic Escherichia coli outbreak in Germany 2011, there is need for fast and precise HGT detection. Next-generation sequencing (NGS) technologies facilitate rapid analysis of unknown pathogens but, to the best of our knowledge, so far no approach detects HGTs directly from NGS reads.'),
(80277, 2016, 'bio', 'A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions', 'Yu, Wenbao|Lee, Seungyeoun|Park, Taesung', 'Department of Statistics, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742, Korea.|Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea.|Department of Statistics, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742, Korea.', '-|-|-', 13, 'Computer Simulation|Epistasis, Genetic|Genome-Wide Association Study|Humans|Models, Genetic|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btw424', '27587680', 'Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGI effects. However, there are several disadvantages of the existing MDR-based approaches, such as the lack of an efficient way of evaluating the significance of multi-locus models and the high computational burden due to intensive permutation. Furthermore, the MDR method does not distinguish marginal effects from pure interaction effects.'),
(80278, 2016, 'bio', 'Gene-set association tests for next-generation sequencing data', 'Lee, Jaehoon|Kim, Young Jin|Lee, Juyoung|T2D-Genes Consortium|Kim, Bong-Jo|Lee, Seungyeoun|Park, Taesung', 'Department of Statistics, Seoul National University, Seoul 151-742, Korea.|Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.|Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.|Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.|Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.|Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea.|Department of Statistics, Seoul National University, Seoul 151-742, Korea.', '-|-|-|-|-|-|-', 4, 'Algorithms|Datasets as Topic|Exome|Genetic Association Studies|Genetic Testing|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Korea|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btw429', '27587681', 'Recently, many methods have been developed for conducting rare-variant association studies for sequencing data. These methods have primarily been based on gene-level associations but have not been proven to be as effective as expected. Gene-set-level tests have shown great advantages over gene-level tests in terms of power and robustness, because complex diseases are often caused by multiple genes that comprise of biological gene sets.'),
(80279, 2016, 'bio', 'XGSA: A statistical method for cross-species gene set analysis', 'Djordjevic, Djordje|Kusumi, Kenro|Ho, Joshua W K', 'Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia, St Vincents Clinical School, University of New South Wales Australia, Darlinghurst, NSW 2010, Australia.|School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia, St Vincents Clinical School, University of New South Wales Australia, Darlinghurst, NSW 2010, Australia.', 'Australia|United States|Australia', 8, 'Animals|Conserved Sequence|Datasets as Topic|Gene Expression Profiling|Gene Ontology|Regeneration|Software|Species Specificity|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btw428', '27587682', 'Gene set analysis is a powerful tool for determining whether an experimentally derived set of genes is statistically significantly enriched for genes in other pre-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene sets. Current gene set analysis methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species gene set analysis.'),
(80280, 2016, 'bio', 'PeakXus: comprehensive transcription factor binding site discovery from ChIP-Nexus and ChIP-Exo experiments', 'Hartonen, Tuomo|Sahu, Biswajyoti|Dave, Kashyap|Kivioja, Teemu|Taipale, Jussi', 'Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.|Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.|Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.|Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.|Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.', 'Finland|Finland|Sweden|Finland|Finland', 3, 'Animals|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|Drosophila melanogaster|Gene Expression Profiling|Genetic Loci|Humans|Protein Binding|Protein Interaction Mapping|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw448', '27587683', 'Transcription factor (TF) binding can be studied accurately in vivo with ChIP-exo and ChIP-Nexus experiments. Only fraction of TF binding mechanisms are yet fully understood and accurate knowledge of binding locations and patterns of TFs is key to understanding binding that is not explained by simple positional weight matrix models. ChIP-exo/Nexus experiments can also offer insight on the effect of single nucleotide polymorphism (SNP) at TF binding sites on expression of the target genes. This is an important mechanism of action for disease-causing SNPs at non-coding genomic regions.'),
(80281, 2016, 'bio', 'DeepChrome: deep-learning for predicting gene expression from histone modifications', 'Singh, Ritambhara|Lanchantin, Jack|Robins, Gabriel|Qi, Yanjun', 'Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.', 'United States|United States|United States|United States', 72, 'Cluster Analysis|Computational Biology|Epigenesis, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Histone Code|Humans|Neural Networks (Computer)|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw427', '27587684', 'Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing epigenetic drugs for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes.'),
(80282, 2016, 'bio', 'Snowball: strain aware gene assembly of metagenomes', 'Gregor, I|Schönhuth, A|McHardy, A C', 'Department of Algorithmic Bioinformatics, Heinrich-Heine-University Düsseldorf, Düsseldorf 40225, Germany Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig 38124, Germany.|Centrum Wiskunde & Informatica, Amsterdam, XG 1098, The Netherlands.|Department of Algorithmic Bioinformatics, Heinrich-Heine-University Düsseldorf, Düsseldorf 40225, Germany Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig 38124, Germany.', 'Germany|Netherlands|Germany', 7, 'Metagenome|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btw426', '27587685', 'Gene assembly is an important step in functional analysis of shotgun metagenomic data. Nonetheless, strain aware assembly remains a challenging task, as current assembly tools often fail to distinguish among strain variants or require closely related reference genomes of the studied species to be available.'),
(80283, 2016, 'bio', 'ModuleAlign: module-based global alignment of protein-protein interaction networks', 'Hashemifar, Somaye|Ma, Jianzhu|Naveed, Hammad|Canzar, Stefan|Xu, Jinbo', 'Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.', 'United States|United States|United States|United States|United States', 19, 'Algorithms|Humans|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw447', '27587686', 'As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80284, 2016, 'bio', 'PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins', 'Tsirigos, Konstantinos D|Elofsson, Arne|Bagos, Pantelis G', 'Department of Biochemistry and Biophysics, Science for Life Laboratory, Swedish E-Science Research Center, Stockholm University, 17121 Solna, Sweden Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, Greece.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Swedish E-Science Research Center, Stockholm University, 17121 Solna, Sweden.|Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, Greece.', 'Greece|Sweden|Greece', 30, 'Algorithms|Computational Biology|Markov Chains|Membrane Proteins|Protein Structure, Secondary|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btw444', '27587687', 'The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments.'),
(80285, 2016, 'bio', 'AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields', 'Wang, Sheng|Ma, Jianzhu|Xu, Jinbo', 'Toyota Technological Institute at Chicago, Chicago, IL, USA Department of Human Genetics, University of Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.', 'United States|United States|United States', 29, 'Area Under Curve|Forecasting|Machine Learning|Neural Networks (Computer)|Proteome|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btw446', '27587688', 'Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it challenging to accurately predict IDRs. Most IDR prediction methods use sequence profile to improve accuracy, which prevents its application to proteome-wide prediction since it is time-consuming to generate sequence profiles. On the other hand, the methods without using sequence profile fare much worse than using sequence profile.'),
(80286, 2016, 'bio', 'SWORD-a highly efficient protein database search', 'Vaser, Robert|Pavlovixc4x87, Dario|xc5xa0ikixc4x87, Mile', 'Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10000, Croatia.|Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10000, Croatia.|Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10000, Croatia Bioinformatics Institute, a*STAR, #07-01 Matrix, 138671, Singapore.', 'Croatia|Croatia|Croatia', 5, 'Algorithms|Databases, Protein|Search Engine|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw445', '27587689', 'Protein database search is one of the fundamental problems in bioinformatics. For decades, it has been explored and solved using different exact and heuristic approaches. However, exponential growth of data in recent years has brought significant challenges in improving already existing algorithms. BLAST has been the most successful tool for protein database search, but is also becoming a bottleneck in many applications. Due to that, many different approaches have been developed to complement or replace it. In this article, we present SWORD, an efficient protein database search implementation that runs 8-16 times faster than BLAST in the sensitive mode and up to 68 times faster in the fast and less accurate mode. It is designed to be used in nearly all database search environments, but is especially suitable for large databases. Its sensitivity exceeds that of BLAST for majority of input datasets and provides guaranteed optimal alignments.'),
(80287, 2016, 'bio', 'Patterns of amino acid conservation in human and animal immunodeficiency viruses', 'Voitenko, Olga S|Dhroso, Andi|Feldmann, Anna|Korkin, Dmitry|Kalinina, Olga V', 'Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken 66123, Germany, Graduate School for Computer Science, Saarland University, Campus E1 3, Saarbrücken 66123, Germany.|Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA.|Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken 66123, Germany, Graduate School for Computer Science, Saarland University, Campus E1 3, Saarbrücken 66123, Germany.|Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA.|Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken 66123, Germany.', 'Germany|United States|Germany|United States|Germany', 2, 'Amino Acid Sequence|Amino Acids|Animals|Conserved Sequence|Evolution, Molecular|HIV-1|Humans|Immunogenetics|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/btw441', '27587690', 'Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity.'),
(80288, 2016, 'bio', 'PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation', 'Neveu, Emilie|Ritchie, David W|Popov, Petr|Grudinin, Sergei', 'Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France.|Inria Nancy - Grand Est, 54600 Villers-les-Nancy, France.|Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.|Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France.', 'France|France|France|France', 11, 'Algorithms|Machine Learning|Models, Theoretical|Molecular Conformation|Molecular Docking Simulation|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw443', '27587691', 'Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential.'),
(80289, 2016, 'bio', 'Simulated linear test applied to quantitative proteomics', 'Pham, T V|Jimenez, C R', 'OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, The Netherlands.|OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands', 1, 'Gene Expression Profiling|Linear Models|Proteomics', 'Journal Article', '10.1093/bioinformatics/btw440', '27587692', 'Omics studies aim to find significant changes due to biological or functional perturbation. However, gene and protein expression profiling experiments contain inherent technical variation. In discovery proteomics studies where the number of samples is typically small, technical variation plays an important role because it contributes considerably to the observed variation. Previous methods place both technical and biological variations in tightly integrated mathematical models that are difficult to adapt for different technological platforms. Our aim is to derive a statistical framework that allows the inclusion of a wide range of technical variability.'),
(80290, 2016, 'bio', 'A probabilistic model for detecting rigid domains in protein structures', 'Nguyen, Thach|Habeck, Michael', 'Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen.|Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany.', '-|Germany', 1, 'Algorithms|Bayes Theorem|Models, Statistical|Monte Carlo Method|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw442', '27587693', 'Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states.'),
(80291, 2016, 'bio', 'L1 regularization facilitates detection of cell type-specific parameters in dynamical systems', 'Steiert, Bernhard|Timmer, Jens|Kreutz, Clemens', 'Institute of Physics.|Institute of Physics BIOSS Centre for Biological Signalling Studies Freiburg Center for Systems Biology (ZBSA), University of Freiburg, Germany.|Institute of Physics Freiburg Center for Systems Biology (ZBSA), University of Freiburg, Germany.', '-|Germany|Germany', 0, 'Algorithms|Cells|Drug Delivery Systems|Least-Squares Analysis|Nonlinear Dynamics|Probability|Programming Languages|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw461', '27587694', 'A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary.'),
(80292, 2016, 'bio', 'Large-scale inference of conjunctive Bayesian networks', 'Montazeri, Hesam|Kuipers, Jack|Kouyos, Roger|Bxc3xb6ni, Jxc3xbcrg|Yerly, Sabine|Klimkait, Thomas|Aubert, Vincent|Gxc3xbcnthard, Huldrych F|Beerenwinkel, Niko|Swiss HIV Cohort Study', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland Institute of Medical Virology.|Swiss National Center for Retroviruses, Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland.|Laboratory of Virology, Division of Infectious Diseases, Geneva University Hospital, Geneva, Switzerland.|Department of Biomedicine-Petersplatz, University of Basel, Basel, Switzerland.|Division of Immunology and Allergy, University Hospital Lausanne, Lausanne, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland Institute of Medical Virology.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 3, 'Algorithms|Bayes Theorem|Cohort Studies|Humans|Monte Carlo Method|Mutation', 'Journal Article', '10.1093/bioinformatics/btw459', '27587695', 'The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of genetic changes (mutations). CT-CBN models have been successfully used in several biological applications such as HIV drug resistance development and genetic progression of cancer. However, current approaches for parameter estimation and network structure learning of CBNs can only deal with a small number of mutations (<20). Here, we address this limitation by presenting an efficient and accurate approximate inference algorithm using a Monte Carlo expectation-maximization algorithm based on importance sampling. The new method can now be used for a large number of mutations, up to one thousand, an increase by two orders of magnitude. In simulation studies, we present the accuracy as well as the running time efficiency of the new inference method and compare it with a MLE method, expectation-maximization, and discrete time CBN model, i.e. a first-order approximation of the CT-CBN model. We also study the application of the new model on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine (AZTxe2x80x89+xe2x80x893TC) as well as under no treatment, both extracted from the Swiss HIV Cohort Study database.'),
(80293, 2016, 'bio', 'A weighted exact test for mutually exclusive mutations in cancer', 'Leiserson, Mark D M|Reyna, Matthew A|Raphael, Benjamin J', 'Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.|Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.', 'United States|United States|United States', 0, 'Algorithms|Humans|Mutation|Neoplasms|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btw462', '27587696', 'The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision.'),
(80294, 2016, 'bio', 'Genome wide predictions of miRNA regulation by transcription factors', 'Ruffalo, Matthew|Bar-Joseph, Ziv', 'Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213.|Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213.', '-|-', 8, 'Gene Expression Profiling|Gene Regulatory Networks|Humans|Machine Learning|MicroRNAs|RNA, Messenger|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw452', '27587697', 'Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated.'),
(80295, 2016, 'bio', 'iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models', 'Sajitz-Hermstein, Max|Töpfer, Nadine|Kleessen, Sabrina|Fernie, Alisdair R|Nikoloski, Zoran', 'Systems Biology and Mathematical Modeling Group.|Department of Plant and Environmental Sciences, Faculty of Biochemistry, Weizmann Institute of Science, Rehovot, 7610001, Israel.|Targenomix GmbH, Am Mühlenberg 11, Potsdam 14476, Germany.|Central Metabolism Group, Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam 14476, Germany.|Systems Biology and Mathematical Modeling Group.', '-|Israel|Germany|Germany|-', 13, 'Forecasting|Genome|Metabolic Networks and Pathways|Metabolomics|Models, Biological|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw465', '27587698', 'Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks.'),
(80296, 2016, 'bio', 'Edge-based sensitivity analysis of signaling networks by using Boolean dynamics', 'Trinh, Hung-Cuong|Kwon, Yung-Keun', 'School of Electrical Engineering, University of Ulsan, 93 Daehak-Ro, Ulsan, 44610 Nam -Gu, Republic of Korea.|School of Electrical Engineering, University of Ulsan, 93 Daehak-Ro, Ulsan, 44610 Nam -Gu, Republic of Korea.', '-|-', 0, 'Apoptosis|Drug Delivery Systems|Feedback|Genes, p53|Humans|Models, Statistical|Models, Theoretical|Mutation|Neoplasms|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw464', '27587699', 'Biological networks are composed of molecular components and their interactions represented by nodes and edges, respectively, in a graph model. Based on this model, there were many studies with respect to effects of node-based mutations on the network dynamics, whereas little attention was paid to edgetic mutations so far.'),
(80297, 2016, 'bio', 'Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation', 'Traynard, Pauline|Fauré, Adrien|Fages, François|Thieffry, Denis', 'Computational Systems Biology Team, Institut de Biologie de LEcole Normale Supérieure (IBENS), CNRS, Inserm, Ecole Normale Supérieure, PSL Research University, Paris, France EPI Lifeware, Inria Inria Saclay Ile-de-France, Palaiseau, France.|Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi, Japan.|EPI Lifeware, Inria Inria Saclay Ile-de-France, Palaiseau, France.|Computational Systems Biology Team, Institut de Biologie de LEcole Normale Supérieure (IBENS), CNRS, Inserm, Ecole Normale Supérieure, PSL Research University, Paris, France EPI Lifeware, Inria Inria Saclay Ile-de-France, Palaiseau, France.', 'France|Japan|France|France', 14, 'Animals|Cell Cycle|Computer Simulation|Humans|Logic|Mammals|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btw457', '27587700', 'Understanding the temporal behaviour of biological regulatory networks requires the integration of molecular information into a formal model. However, the analysis of model dynamics faces a combinatorial explosion as the number of regulatory components and interactions increases.'),
(80298, 2016, 'bio', 'Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach', 'Koutroumpas, Konstantinos|Ballarini, Paolo|Votsi, Irene|Cournède, Paul-Henry', 'Lab MICS, CentraleSupélec, University of Paris Saclay, 92295 Chatenay-Malabry, France.|Lab MICS, CentraleSupélec, University of Paris Saclay, 92295 Chatenay-Malabry, France.|Lab MICS, CentraleSupélec, University of Paris Saclay, 92295 Chatenay-Malabry, France.|Lab MICS, CentraleSupélec, University of Paris Saclay, 92295 Chatenay-Malabry, France.', 'France|France|France|France', 5, 'Bayes Theorem|Likelihood Functions|Models, Statistical|Monte Carlo Method|Wnt Signaling Pathway', 'Journal Article', '10.1093/bioinformatics/btw471', '27587701', 'Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels.'),
(80299, 2016, 'bio', 'On cross-conditional and fluctuation correlations in competitive RNA networks', 'Sánchez-Taltavull, Daniel|MacLeod, Matthew|Perkins, Theodore J', 'Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa K1H8L6, Canada.|Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa K1H8L6, Canada Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa K1H8M5, Canada.|Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa K1H8L6, Canada Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa K1H8M5, Canada Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa K1H8M5, Canada.', 'Canada|Canada|Canada', 0, 'Gene Regulatory Networks|MicroRNAs|RNA, Messenger|Receptor Cross-Talk', 'Journal Article', '10.1093/bioinformatics/btw472', '27587702', 'In competitive endogenous RNA (ceRNA) networks, different mRNAs targeted by the same miRNA can cross-talk by absorbing miRNAs and relieving repression on the other mRNAs. This creates correlations in mRNA expression even without direct interaction. Most previous theoretical study of cross-talk has focused on correlations in stochastic fluctuations of mRNAs around their steady state values. However, the experimentally known examples of cross-talk do not involve single-cell fluctuations, but rather bulk tissue-level changes between conditions, such as due to differentiation or disease. In our study, we quantify for the first time both fluctuational and cross-conditional cross-talk in chemical kinetic models of miRNA-mRNA interaction networks. We study the parameter regions under which these different types of cross-talk arise, and how they are affected by network structure.'),
(80300, 2017, 'bio', 'popSTR: population-scale detection of STR variants', 'Kristmundsdóttir, Snædís|Sigurpálsdóttir, Brynja D|Kehr, Birte|Halldórsson, Bjarni V', 'deCODE genetics/Amgen.|School of Science and Engineering, Reykjavík University, Reykjavík, 101, Iceland.|deCODE genetics/Amgen.|deCODE genetics/Amgen.', '-|Iceland|-|-', 9, 'Genotype|Humans|Microsatellite Repeats|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btw568', '27591079', 'Microsatellites, also known as short tandem repeats (STRs), are tracts of repetitive DNA sequences containing motifs ranging from two to six bases. Microsatellites are one of the most abundant type of variation in the human genome, after single nucleotide polymorphisms (SNPs) and Indels. Microsatellite analysis has a wide range of applications, including medical genetics, forensics and construction of genetic genealogy. However, microsatellite variations are rarely considered in whole-genome sequencing studies, in large due to a lack of tools capable of analyzing them.'),
(80301, 2017, 'bio', 'signeR: an empirical Bayesian approach to mutational signature discovery', 'Rosales, Rafael A|Drummond, Rodrigo D|Valieris, Renan|Dias-Neto, Emmanuel|da Silva, Israel T', 'Departamento de Computaxc3xa7xc3xa3o e Matemxc3xa1tica, Universidade de Sxc3xa3o Paulo, Ribeirxc3xa3o Preto, SP 14040-901, Brazil.|Laboratory of Bioinformatics and Computational Biology, A. C. Camargo Cancer Center, Sxc3xa3o Paulo, SP 01509-010, Brazil.|Laboratory of Bioinformatics and Computational Biology, A. C. Camargo Cancer Center, Sxc3xa3o Paulo, SP 01509-010, Brazil.|Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sxc3xa3o Paulo, SP 01509-010, Brazil.|Laboratory of Bioinformatics and Computational Biology, A. C. Camargo Cancer Center, Sxc3xa3o Paulo, SP 01509-010, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 30, 'Algorithms|Animals|Bayes Theorem|DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw572', '27591080', 'Mutational signatures can be used to understand cancer origins and provide a unique opportunity to group tumor types that share the same origins and result from similar processes. These signatures have been identified from high throughput sequencing data generated from cancer genomes by using non-negative matrix factorisation (NMF) techniques. Current methods based on optimization techniques are strongly sensitive to initial conditions due to high dimensionality and nonconvexity of the NMF paradigm. In this context, an important question consists in the determination of the actual number of signatures that best represent the data. The extraction of mutational signatures from high-throughput data still remains a daunting task.'),
(80302, 2017, 'bio', 'Precrec: fast and accurate precision-recall and ROC curve calculations in R', 'Saito, Takaya|Rehmsmeier, Marc', 'Computational Biology Unit, Department of Informatics, University of Bergen, N-5020 Bergen, Norway.|Computational Biology Unit, Department of Informatics, University of Bergen, N-5020 Bergen, Norway.', 'Norway|Norway', 0, 'Computational Biology|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btw570', '27591081', 'The precision-recall plot is more informative than the ROC plot when evaluating classifiers on imbalanced datasets, but fast and accurate curve calculation tools for precision-recall plots are currently not available. We have developed Precrec, an R library that aims to overcome this limitation of the plot. Our tool provides fast and accurate precision-recall calculations together with multiple functionalities that work efficiently under different conditions.'),
(80303, 2017, 'bio', 'HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics', 'Zheng, Jie|Rodriguez, Santiago|Laurin, Charles|Baird, Denis|Trela-Larsen, Lea|Erzurumluoglu, Mesut A|Zheng, Yi|White, Jon|Giambartolomei, Claudia|Zabaneh, Delilah|Morris, Richard|Kumari, Meena|Casas, Juan P|Hingorani, Aroon D|UCLEB Consortium|Evans, David M|Gaunt, Tom R|Day, Ian N M', 'MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK.|School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK.|Dedman College of Humanities and Sciences, Southern Methodist University, Dallas, TX 750235, USA.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.|MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.|School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 3, 'Chromosome Mapping|Gene Frequency|Genome-Wide Association Study|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btw565', '27591082', 'Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.'),
(80304, 2017, 'bio', 'Gene- and pathway-based association tests for multiple traits with GWAS summary statistics', 'Kwak, Il-Youp|Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States', 13, 'Biostatistics|Genes|Genetic Variation|Genome-Wide Association Study|Metabolic Networks and Pathways|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw577', '27592708', 'To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels, that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods.'),
(80305, 2017, 'bio', 'Rail-RNA: scalable analysis of RNA-seq splicing and coverage', 'Nellore, Abhinav|Collado-Torres, Leonardo|Jaffe, Andrew E|Alquicira-Hernxc3xa1ndez, Josxc3xa9|Wilks, Christopher|Pritt, Jacob|Morton, James|Leek, Jeffrey T|Langmead, Ben', 'Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Exons|Gene Expression Profiling|RNA Splicing|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw575', '27592709', 'RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it requires extra work to obtain analysis products that incorporate data from across samples.'),
(80306, 2017, 'bio', 'Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens', 'Taylor, Sandra L|Ruhaak, L Renee|Weiss, Robert H|Kelly, Karen|Kim, Kyoungmi', 'Division of Biostatistics, Department of Public Health Sciences, University of California Davis, CA, 95616, USA.|Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands.|Division of Nephrology, Department of Internal Medicine.|Division of Hematology and Oncology, Department of Internal Medicine School of Medicine, University of California, Davis, CA 95616, USA.|Division of Biostatistics, Department of Public Health Sciences, University of California Davis, CA, 95616, USA.', 'United States|Netherlands|United States|United States|United States', 0, 'Animals|Glycomics|High-Throughput Screening Assays|Humans|Mass Spectrometry|Metabolomics|Mice|Multivariate Analysis|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw578', '27592710', 'High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results.'),
(80307, 2017, 'bio', 'DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics', 'Wieczorek, Samuel|Combes, Florence|Lazar, Cosmin|Giai Gianetto, Quentin|Gatto, Laurent|Dorffer, Alexia|Hesse, Anne-Marie|Couté, Yohann|Ferro, Myriam|Bruley, Christophe|Burger, Thomas', 'Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Computational Proteomics Unit, Cambridge, CB2 1GA, UK.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.|Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France.', 'France|France|France|France|United Kingdom|France|France|France|France|France|France', 0, 'Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btw580', '27605098', 'DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser.'),
(80308, 2017, 'bio', 'OntoBrowser: a collaborative tool for curation of ontologies by subject matter experts', 'Ravagli, Carlo|Pognan, Francois|Marc, Philippe', 'PreClinical Safety, Translational Sciences, Novartis Institute for Biomedical Research, Basel, CH-4002, Switzerland.|PreClinical Safety, Translational Sciences, Novartis Institute for Biomedical Research, Basel, CH-4002, Switzerland.|PreClinical Safety, Translational Sciences, Novartis Institute for Biomedical Research, Basel, CH-4002, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Biological Ontologies|Data Curation|Databases, Factual|Peer Review|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btw579', '27605099', 'The lack of controlled terminology and ontology usage leads to incomplete search results and poor interoperability between databases. One of the major underlying challenges of data integration is curating data to adhere to controlled terminologies and/or ontologies. Finding subject matter experts with the time and skills required to perform data curation is often problematic. In addition, existing tools are not designed for continuous data integration and collaborative curation. This results in time-consuming curation workflows that often become unsustainable. The primary objective of OntoBrowser is to provide an easy-to-use online collaborative solution for subject matter experts to map reported terms to preferred ontology (or code list) terms and facilitate ontology evolution. Additional features include web service access to data, visualization of ontologies in hierarchical/graph format and a peer review/approval workflow with alerting.'),
(80309, 2017, 'bio', 'MPRAnator: a web-based tool for the design of massively parallel reporter assay experiments', 'Georgakopoulos-Soares, Ilias|Jain, Naman|Gray, Jesse M|Hemberg, Martin', 'Department of Computational Genomics, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.|Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.|Department of Genetics, Harvard Medical School, Boston, MA 02135, USA.|Department of Computational Genomics, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'DNA|Gene Regulatory Networks|Genes, Reporter|High-Throughput Screening Assays|Internet|Polymorphism, Single Nucleotide|Research Design|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw584', '27605100', 'With the rapid advances in DNA synthesis and sequencing technologies and the continuing decline in the associated costs, high-throughput experiments can be performed to investigate the regulatory role of thousands of oligonucleotide sequences simultaneously. Nevertheless, designing high-throughput reporter assay experiments such as massively parallel reporter assays (MPRAs) and similar methods remains challenging.'),
(80310, 2017, 'bio', 'LncVar: a database of genetic variation associated with long non-coding genes', 'Chen, Xiaowei|Hao, Yajing|Cui, Ya|Fan, Zhen|He, Shunmin|Luo, Jianjun|Chen, Runsheng', 'CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.|CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.|CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.|CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.|CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.|CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences.', '-|-|-|-|China|-|-', 0, 'Animals|Arabidopsis|Databases, Nucleic Acid|Genetic Variation|Humans|Open Reading Frames|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|RNA, Long Noncoding', 'Journal Article', '10.1093/bioinformatics/btw581', '27605101', 'Long non-coding RNAs (lncRNAs) are essential in many molecular pathways, and are frequently associated with disease but the mechanisms of most lncRNAs have not yet been characterized. Genetic variations, including single nucleotide polymorphisms (SNPs) and structural variations, are widely distributed in the genome, including lncRNA gene regions. As the number of studies on lncRNAs grows rapidly, it is necessary to evaluate the effects of genetic variations on lncRNAs.'),
(80311, 2017, 'bio', 'PHYLOViZ 20: providing scalable data integration and visualization for multiple phylogenetic inference methods', 'Nascimento, Marta|Sousa, Adriano|Ramirez, Mário|Francisco, Alexandre P|Carriço, João A|Vaz, Cátia', 'INESC-ID, 1000-029 Lisboa, Portugal.|Instituto Superior de Engenharia de Lisboa, 1959-007 Lisboa, Portugal.|Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal.|INESC-ID, 1000-029 Lisboa, Portugal.|Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal.|INESC-ID, 1000-029 Lisboa, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 96, 'Bacteria|Bacterial Typing Techniques|Genome, Bacterial|Multilocus Sequence Typing|Phylogeny|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw582', '27605102', 'High Throughput Sequencing provides a cost effective means of generating high resolution data for hundreds or even thousands of strains, and is rapidly superseding methodologies based on a few genomic loci. The wealth of genomic data deposited on public databases such as Sequence Read Archive/European Nucleotide Archive provides a powerful resource for evolutionary analysis and epidemiological surveillance. However, many of the analysis tools currently available do not scale well to these large datasets, nor provide the means to fully integrate ancillary data. Here we present PHYLOViZ 2.0, an extension of PHYLOViZ tool, a platform independent Java tool that allows phylogenetic inference and data visualization for large datasets of sequence based typing methods, including Single Nucleotide Polymorphism (SNP) and whole genome/core genome Multilocus Sequence Typing (wg/cgMLST) analysis. PHYLOViZ 2.0 incorporates new data analysis algorithms and new visualization modules, as well as the capability of saving projects for subsequent work or for dissemination of results.'),
(80312, 2017, 'bio', 'stringMLST: a fast k-mer based tool for multilocus sequence typing', 'Gupta, Anuj|Jordan, I King|Rishishwar, Lavanya', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia', 0, 'Bacteria|Bacterial Typing Techniques|Campylobacter jejuni|Chlamydia trachomatis|High-Throughput Nucleotide Sequencing|Multilocus Sequence Typing|Neisseria meningitidis|Sequence Analysis, DNA|Software|Streptococcus pneumoniae', 'Journal Article', '10.1093/bioinformatics/btw586', '27605103', 'Rapid and accurate identification of the sequence type (ST) of bacterial pathogens is critical for epidemiological surveillance and outbreak control. Cheaper and faster next-generation sequencing (NGS) technologies have taken preference over the traditional method of amplicon sequencing for multilocus sequence typing (MLST). But data generated by NGS platforms necessitate quality control, genome assembly and sequence similarity searching before an isolates ST can be determined. These are computationally intensive and time consuming steps, which are not ideally suited for real-time molecular epidemiology. Here, we present stringMLST, an assembly- and alignment-free, lightweight, platform-independent program capable of rapidly typing bacterial isolates directly from raw sequence reads. The program implements a simple hash table data structure to find exact matches between short sequence strings (k-mers) and an MLST allele library. We show that stringMLST is more accurate, and order of magnitude faster, than its contemporary genome-based ST detection tools.'),
(80313, 2017, 'bio', 'TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation', 'Perovic, Vladimir|Sumonja, Neven|Gemovic, Branislava|Toska, Eneda|Roberts, Stefan G|Veljkovic, Nevena', 'Centre for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia.|Centre for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia.|Centre for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia.|Department of Biological Sciences, University at Buffalo, Buffalo, NY 14260, USA.|Department of Biological Sciences, University at Buffalo, Buffalo, NY 14260, USA.|Centre for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia.', 'Serbia|Serbia|Serbia|United States|United States|Serbia', 0, 'Computational Biology|Gene Expression Regulation|Humans|Internet|Protein Binding|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btw590', '27605104', 'The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.'),
(80314, 2017, 'bio', 'ReliableGenome: annotation of genomic regions with high/low variant calling concordance', 'Popitsch, Niko|WGS500 Consortium|Schuh, Anna|Taylor, Jenny C', 'Wellcome Trust Centre of Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|Wellcome Trust Centre of Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, The Churchill Hospital, Old Road OX3 7LE, UK.|Wellcome Trust Centre of Human Genetics, University of Oxford, Oxford OX3 7BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw587', '27605105', 'The increasing adoption of clinical whole-genome resequencing (WGS) demands for highly accurate and reproducible variant calling (VC) methods. The observed discordance between state-of-the-art VC pipelines, however, indicates that the current practice still suffers from non-negligible numbers of false positive and negative SNV and INDEL calls that were shown to be enriched among discordant calls but also in genomic regions with low sequence complexity.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80315, 2017, 'bio', 'tHapMix: simulating tumour samples through haplotype mixtures', 'Ivakhno, Sergii|Colombo, Camilla|Tanner, Stephen|Tedder, Philip|Berri, Stefano|Cox, Anthony J', 'Chesterford Research Park, Illumina Cambridge Ltd, Little Chesterford, CB10 1XL, UK.|Chesterford Research Park, Illumina Cambridge Ltd, Little Chesterford, CB10 1XL, UK.|Illumina Inc, San Diego, CA 92122, USA.|Chesterford Research Park, Illumina Cambridge Ltd, Little Chesterford, CB10 1XL, UK.|Chesterford Research Park, Illumina Cambridge Ltd, Little Chesterford, CB10 1XL, UK.|Chesterford Research Park, Illumina Cambridge Ltd, Little Chesterford, CB10 1XL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Simulation|DNA Copy Number Variations|DNA, Neoplasm|Genome|Genomics|Haplotypes|Humans|Neoplasms|Ploidies|Software', 'Journal Article', '10.1093/bioinformatics/btw589', '27605106', 'Large-scale rearrangements and copy number changes combined with different modes of clonal evolution create extensive somatic genome diversity, making it difficult to develop versatile and scalable variant calling tools and create well-calibrated benchmarks.'),
(80316, 2017, 'bio', 'BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals', 'Kim, Minsuk|Sun, Gwanggyu|Lee, Dong-Yup|Kim, Byung-Gee', 'School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.|School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore.|School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.', '-|-|Singapore|-', 0, 'Algorithms|Computer Simulation|Escherichia coli|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Metabolic Networks and Pathways|Models, Biological|Reproducibility of Results|Streptomyces coelicolor|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btw557', '27605107', 'Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models.'),
(80317, 2017, 'bio', 'A novel method for predicting activity of cis-regulatory modules, based on a diverse training set', 'Yang, Wei|Sinha, Saurabh', 'Department of Computer Science, University of Illinois, Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois, Urbana-Champaign, Urbana, IL, USA.', 'United States|United States', 0, 'Algorithms|Animals|Binding Sites|Drosophila melanogaster|Enhancer Elements, Genetic|Gene Regulatory Networks|Genome, Insect|Genomics|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw552', '27609510', 'With the rapid emergence of technologies for locating cis-regulatory modules (CRMs) genome-wide, the next pressing challenge is to assign precise functions to each CRM, i.e. to determine the spatiotemporal domains or cell-types where it drives expression. A popular approach to this task is to model the typical k-mer composition of a set of CRMs known to drive a common expression pattern, and assign that pattern to other CRMs exhibiting a similar k-mer composition. This approach does not rely on prior knowledge of transcription factors relevant to the CRM or their binding motifs, and is thus more widely applicable than motif-based methods for predicting CRM activity, but is also prone to false positive predictions.'),
(80318, 2017, 'bio', 'Nanocall: an open source basecaller for Oxford Nanopore sequencing data', 'David, Matei|Dursi, L J|Yao, Delia|Boutros, Paul C|Simpson, Jared T', 'Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.|Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.', 'Canada|Canada|Canada|Canada|Canada', 0, 'DNA|Escherichia coli|Humans|Polymerase Chain Reaction|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw569', '27614348', 'The highly portable Oxford Nanopore MinION sequencer has enabled new applications of genome sequencing directly in the field. However, the MinION currently relies on a cloud computing platform, Metrichor (metrichor.com), for translating locally generated sequencing data into basecalls.'),
(80319, 2017, 'bio', 'The OGCleaner: filtering false-positive homology clusters', 'Fujimoto, M Stanley|Suvorov, Anton|Jensen, Nicholas O|Clement, Mark J|Snell, Quinn|Bybee, Seth M', 'Computer Science Department, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Computer Science Department, Brigham Young University, Provo, UT 84602, USA.|Computer Science Department, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Molecular Sequence Annotation|Phylogeny|Protein Conformation|Proteins|Proteomics|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btw571', '27614349', 'Detecting homologous sequences in organisms is an essential step in protein structure and function prediction, gene annotation and phylogenetic tree construction. Heuristic methods are often employed for quality control of putative homology clusters. These heuristics, however, usually only apply to pairwise sequence comparison and do not examine clusters as a whole. We present the Orthology Group Cleaner (the OGCleaner), a tool designed for filtering putative orthology groups as homology or non-homology clusters by considering all sequences in a cluster. The OGCleaner relies on high-quality orthologous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish between true-positive and false-positive homology groups. This package aims to improve the quality of phylogenetic tree construction especially in instances of lower-quality transcriptome assemblies.'),
(80320, 2017, 'bio', 'Assessment of cancer and virus antigens for cross-reactivity in human tissues', 'Jaravine, Victor|Raffegerst, Silke|Schendel, Dolores J|Frishman, Dmitrij', 'Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universitxc3xa4t Mxc3xbcnchen, Freising 85354, Germany.|Medigene Immunotherapies GmbH, Martinsried/Planegg 82152, Germany.|Medigene Immunotherapies GmbH, Martinsried/Planegg 82152, Germany.|Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universitxc3xa4t Mxc3xbcnchen, Freising 85354, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Antigens, Neoplasm|Antigens, Viral|Cross Reactions|Epitopes|Female|Histocompatibility Antigens Class I|Humans|Male|Neoplasm Proteins|Neoplasms|Peptides|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btw567', '27614350', 'Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens.'),
(80321, 2017, 'bio', 'DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency', 'Wang, Xiao|Gu, Jinghua|Hilakivi-Clarke, Leena|Clarke, Robert|Xuan, Jianhua', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC 20057, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC 20057, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States', 3, 'Bayes Theorem|Breast Neoplasms|DNA Methylation|DNA, Neoplasm|Female|Genomics|Humans|Neoplasm Recurrence, Local|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw596', '27616707', 'The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples.'),
(80322, 2017, 'bio', 'ImmuneDB: a system for the analysis and exploration of high-throughput adaptive immune receptor sequencing data', 'Rosenfeld, Aaron M|Meng, Wenzhao|Luning Prak, Eline T|Hershberg, Uri', 'School of Biomedical Engineering Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104xe2x80x89PA, USA.|Department of Pathology and Laboratory Medicine Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104xe2x80x89PA, USA.|School of Biomedical Engineering Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States', 10, 'Animals|High-Throughput Nucleotide Sequencing|Humans|Receptors, Immunologic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw593', '27616708', 'As high-throughput sequencing of B cells becomes more common, the need for tools to analyze the large quantity of data also increases. This article introduces ImmuneDB, a system for analyzing vast amounts of heavy chain variable region sequences and exploring the resulting data. It can take as input raw FASTA/FASTQ data, identify genes, determine clones, construct lineages, as well as provide information such as selection pressure and mutation analysis. It uses an industry leading database, MySQL, to provide fast analysis and avoid the complexities of using error prone flat-files.'),
(80323, 2017, 'bio', 'D-VASim: an interactive virtual laboratory environment for the simulation and analysis of genetic circuits', 'Baig, Hasan|Madsen, Jan', 'Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.', 'Denmark|Denmark', 0, 'Computer Simulation|Gene Regulatory Networks|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btw592', '27616709', 'Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation, and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model.'),
(80324, 2017, 'bio', 'Reference point insensitive molecular data analysis', 'Altenbuchinger, M|Rehberg, T|Zacharias, H U|Stämmler, F|Dettmer, K|Weber, D|Hiergeist, A|Gessner, A|Holler, E|Oefner, P J|Spang, R', 'Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany.|Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany.|Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany.|Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 13, 'Algorithms|Bacteria|Computational Biology|Computer Simulation|Gastrointestinal Microbiome|Gene Expression Regulation, Bacterial|Humans|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btw598', '27634945', 'In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed.'),
(80325, 2017, 'bio', 'AKT: ancestry and kinship toolkit', 'Arthur, Rudy|Schulz-Trieglaff, Ole|Cox, Anthony J|OConnell, Jared', 'Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Cluster Analysis|Family|Female|Genome, Human|Humans|Male|Pedigree|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw576', '27634946', 'Ancestry and Kinship Toolkit (AKT) is a statistical genetics tool for analysing large cohorts of whole-genome sequenced samples. It can rapidly detect related samples, characterize sample ancestry, calculate correlation between variants, check Mendel consistency and perform data clustering. AKT brings together the functionality of many state-of-the-art methods, with a focus on speed and a unified interface. We believe it will be an invaluable tool for the curation of large WGS datasets.'),
(80326, 2017, 'bio', 'IsotopicLabelling: an R package for the analysis of MS isotopic patterns of labelled analytes', 'Ferrazza, Ruggero|Griffin, Julian L|Guella, Graziano|Franceschi, Pietro', 'Bioorganic Chemistry Laboratory, Department of Physics.|Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.|Bioorganic Chemistry Laboratory, Department of Physics.|Edmund Mach Foundation, Research and Innovation Centre, Computational Biology Unit, I-38010 San Michele AllAdige (TN), Italy.', 'United Kingdom|United Kingdom|United Kingdom|Italy', 0, 'Carbon Isotopes|Chromatography, Gas|Chromatography, Liquid|Deuterium|Isotope Labeling|Mass Spectrometry|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw588', '27634947', '-'),
(80327, 2017, 'bio', 'Seeksv: an accurate tool for somatic structural variation and virus integration detection', 'Liang, Ying|Qiu, Kunlong|Liao, Bo|Zhu, Wen|Huang, Xuanlin|Li, Lin|Chen, Xiangtao|Li, Keqin', 'College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.|BGI, Shenzhen, Guangdong 518083, China.|College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.|College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.|BGI, Shenzhen, Guangdong 518083, China.|BGI, Shenzhen, Guangdong 518083, China.|College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.|College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.', 'China|China|China|China|China|China|China|China', 11, 'Carcinoma, Squamous Cell|Esophageal Neoplasms|Esophageal Squamous Cell Carcinoma|Genome, Human|Genomic Structural Variation|Humans|Sequence Analysis, DNA|Software|Virus Integration', 'Journal Article', '10.1093/bioinformatics/btw591', '27634948', 'Many forms of variations exist in the human genome including single nucleotide polymorphism, small insert/deletion (DEL) (indel) and structural variation (SV). Somatically acquired SV may regulate the expression of tumor-related genes and result in cell proliferation and uncontrolled growth, eventually inducing tumor formation. Virus integration with host genome sequence is a type of SV that causes the related gene instability and normal cells to transform into tumor cells. Cancer SVs and viral integration sites must be discovered in a genome-wide scale for clarifying the mechanism of tumor occurrence and development.'),
(80328, 2017, 'bio', 'Verdant: automated annotation, alignment and phylogenetic analysis of whole chloroplast genomes', 'McKain, Michael R|Hartsock, Ryan H|Wohl, Molly M|Kellogg, Elizabeth A', 'Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.', 'United States|United States|United States|United States', 31, 'Databases, Nucleic Acid|Genome, Chloroplast|Molecular Sequence Annotation|Phylogeny|Plants|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw583', '27634949', 'Chloroplast genomes are now produced in the hundreds for angiosperm phylogenetics projects, but current methods for annotation, alignment and tree estimation still require some manual intervention reducing throughput and increasing analysis time for large chloroplast systematics projects.'),
(80329, 2017, 'bio', 'RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints', 'Zhong, Yi|Karaletsos, Theofanis|Drewe, Philipp|Sreedharan, Vipin T|Kuo, David|Singh, Kamini|Wendel, Hans-Guido|Rätsch, Gunnar', 'Computational Biology Program, Sloan Kettering Institute, New York, NY 1275, USA.|Computational Biology Program, Sloan Kettering Institute, New York, NY 1275, USA.|Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany.|Computational Biology Program, Sloan Kettering Institute, New York, NY 1275, USA.|Computational Biology Program, Sloan Kettering Institute, New York, NY 1275, USA.|Cancer Biology Program, Sloan Kettering Institute, New York, NY, USA.|Cancer Biology Program, Sloan Kettering Institute, New York, NY, USA.|Computational Biology Program, Sloan Kettering Institute, New York, NY 1275, USA.', 'United States|United States|Germany|United States|United States|United States|United States|United States', 45, 'Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Protein Biosynthesis|RNA, Messenger|Ribosomes|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw585', '27634950', 'Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed.'),
(80330, 2017, 'bio', 'BOSS: a novel scaffolding algorithm based on an optimized scaffold graph', 'Luo, Junwei|Wang, Jianxin|Zhang, Zhen|Li, Min|Wu, Fang-Xiang', 'School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan S7N 5A9, Canada.', 'China|China|China|China|Canada', 17, 'Algorithms|Genome|Humans|Plasmodium falciparum|Repetitive Sequences, Nucleic Acid|Rhodobacter sphaeroides|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1093/bioinformatics/btw597', '27634951', 'While aiming to determine orientations and orders of fragmented contigs, scaffolding is an essential step of assembly pipelines and can make assembly results more complete. Most existing scaffolding tools adopt scaffold graph approaches. However, due to repetitive regions in genome, sequencing errors and uneven sequencing depth, constructing an accurate scaffold graph is still a challenge task.'),
(80331, 2016, 'bio', 'MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems', 'Gonzxc3xa1lez-Domxc3xadnguez, Jorge|Liu, Yongchao|Tourixc3xb1o, Juan|Schmidt, Bertil', 'Campus de Elvixc3xb1a, Grupo de Arquitectura de Computadores, Universidade da Coruxc3xb1a, 15071 A Coruxc3xb1a, Spain.|School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332, USA.|Campus de Elvixc3xb1a, Grupo de Arquitectura de Computadores, Universidade da Coruxc3xb1a, 15071 A Coruxc3xb1a, Spain.|Institut fxc3xbcr Informatik, Johannes Gutenberg Universitxc3xa4t Mainz, Staudingerweg 9, Mainz 55128, Germany.', 'Spain|Georgia|Spain|Germany', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Markov Chains|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw558', '27638400', 'MSAProbs is a state-of-the-art protein multiple sequence alignment tool based on hidden Markov models. It can achieve high alignment accuracy at the expense of relatively long runtimes for large-scale input datasets. In this work we present MSAProbs-MPI, a distributed-memory parallel version of the multithreaded MSAProbs tool that is able to reduce runtimes by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on a cluster with 32 nodes (each containing two Intel Haswell processors) shows reductions in execution time of over one order of magnitude for typical input datasets. Furthermore, MSAProbs-MPI using eight nodes is faster than the GPU-accelerated QuickProbs running on a Tesla K20. Another strong point is that MSAProbs-MPI can deal with large datasets for which MSAProbs and QuickProbs might fail due to time and memory constraints, respectively.'),
(80332, 2017, 'bio', 'PDB2CD: a web-based application for the generation of circular dichroism spectra from protein atomic coordinates', 'Mavridis, Lazaros|Janes, Robert W', 'School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.|School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.', 'United Kingdom|United Kingdom', 0, 'Circular Dichroism|Databases, Protein|Internet|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw554', '27651482', 'Circular dichroism (CD) spectroscopy is extensively utilized for determining the percentages of secondary structure content present in proteins. However, although a large contributor, secondary structure is not the only factor that influences the shape and magnitude of the CD spectrum produced. Other structural features can make contributions so an entire protein structural conformation can give rise to a CD spectrum. There is a need for an application capable of generating protein CD spectra from atomic coordinates. However, no empirically derived method to do this currently exists.'),
(80333, 2017, 'bio', 'Tissue-specific pathway association analysis using genome-wide association study summaries', 'Wang, Wenyu|Hao, Jingcan|Zheng, Shuyu|Fan, Qianrui|He, Awen|Wen, Yan|Guo, Xiong|Wu, Cuiyan|Wang, Sen|Yang, Tielin|Shen, Hui|Chen, Xiangding|Tian, Qing|Tan, Lijun|Deng, Hong-Wen|Zhang, Feng', 'Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Department of Radiation Oncology, First Affiliated Hospital, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian, P.R. China.|Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine.|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, P. R. China.|Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine.|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, P. R. China.|Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine.|Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.', '-|-|-|-|-|-|-|-|-|China|-|China|-|China|-|-', 4, 'Algorithms|Asian Continental Ancestry Group|Data Interpretation, Statistical|European Continental Ancestry Group|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Metabolic Networks and Pathways|Organ Specificity', 'Journal Article', '10.1093/bioinformatics/btw595', '27651483', 'Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity.'),
(80334, 2017, 'bio', 'GeneEvolve: a fast and memory efficient forward-time simulator of realistic whole-genome sequence and SNP data', 'Tahmasbi, Rasool|Keller, Matthew C', 'Institute for Behavioral Genetics (IBG).|Institute for Behavioral Genetics (IBG).', '-|-', 0, 'Biological Evolution|Computer Simulation|Genetics, Population|Genome|Haplotypes|Polymorphism, Single Nucleotide|Recombination, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw606', '27659450', 'Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes that cannot be analytically predicted and for creating realistic genetic data. There are many software packages that simulate genetic data, but they are typically not fast or memory efficient enough to simulate realistic, individual-level genome-wide SNP/sequence data.'),
(80335, 2017, 'bio', 'PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification', 'Chen, Xi|Shi, Xu|Hilakivi-Clarke, Leena|Shajahan-Haq, Ayesha N|Clarke, Robert|Xuan, Jianhua', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|DNA Mutational Analysis|DNA, Neoplasm|Female|Gene Expression Regulation, Neoplastic|Genomic Structural Variation|Humans|Mutation|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/btw605', '27659451', 'Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs.'),
(80336, 2017, 'bio', 'TwoPaCo: an efficient algorithm to build the compacted de Bruijn graph from many complete genomes', 'Minkin, Ilia|Pham, Son|Medvedev, Paul', 'Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.|BioTuring Inc., San Diego, CA 92121, USA.|Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.', 'United States|United States|United States', 10, 'Algorithms|Animals|Genome, Human|Genomics|Humans|Primates|Software', 'Journal Article', '10.1093/bioinformatics/btw609', '27659452', 'de Bruijn graphs have been proposed as a data structure to facilitate the analysis of related whole genome sequences, in both a population and comparative genomic settings. However, current approaches do not scale well to many genomes of large size (such as mammalian genomes).'),
(80337, 2017, 'bio', 'Evolutionary conservation of Ebola virus proteins predicts important functions at residue level', 'Arslan, Ahmed|van Noort, Vera', 'KU Leuven, Center of Microbial and Plant Genetics, 3001 Leuven, Belgium.|KU Leuven, Center of Microbial and Plant Genetics, 3001 Leuven, Belgium.', 'Belgium|Belgium', 0, 'Amino Acid Sequence|Ataxia Telangiectasia Mutated Proteins|Conserved Sequence|DNA Damage|DNA Repair|Ebolavirus|Evolution, Molecular|Hemorrhagic Fever, Ebola|Humans|Models, Molecular|Phosphorylation|Protein Processing, Post-Translational|Proteomics|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btw610', '27659453', 'The recent outbreak of Ebola virus disease (EVD) resulted in a large number of human deaths. Due to this devastation, the Ebola virus has attracted renewed interest as model for virus evolution. Recent literature on Ebola virus (EBOV) has contributed substantially to our understanding of the underlying genetics and its scope with reference to the 2014 outbreak. But no study yet, has focused on the conservation patterns of EBOV proteins.'),
(80338, 2017, 'bio', 'Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark', 'Klein, Max|Sharma, Rati|Bohrer, Chris H|Avelis, Cameron M|Roberts, Elijah', 'Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States|United States', 12, 'Computational Biology|Computer Simulation|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btw614', '27663493', 'Data-parallel programming techniques can dramatically decrease the time needed to analyze large datasets. While these methods have provided significant improvements for sequencing-based analyses, other areas of biological informatics have not yet adopted them. Here, we introduce Biospark, a new framework for performing data-parallel analysis on large numerical datasets. Biospark builds upon the open source Hadoop and Spark projects, bringing domain-specific features for biology.'),
(80339, 2017, 'bio', 'A generative model for the behavior of RNA polymerase', 'Azofeifa, Joseph G|Dowell, Robin D', 'Department of Computer Science, University of Colorado, Boulder, CO, USA.|Department of Computer Science, University of Colorado, Boulder, CO, USA.', 'United States|United States', 0, 'Chromatin|DNA-Directed RNA Polymerases|Eukaryota|Models, Biological|Models, Molecular|Models, Statistical|Promoter Regions, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btw599', '27663494', 'Transcription by RNA polymerase is a highly dynamic process involving multiple distinct points of regulation. Nascent transcription assays are a relatively new set of high throughput techniques that measure the location of actively engaged RNA polymerase genome wide. Hence, nascent transcription is a rich source of information on the regulation of RNA polymerase activity. To fully dissect this data requires the development of stochastic models that can both deconvolve the stages of polymerase activity and identify significant changes in activity between experiments.'),
(80340, 2017, 'bio', 'An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data', 'Kato, Yuki|Mori, Tomoya|Sato, Kengo|Maegawa, Shingo|Hosokawa, Hiroshi|Akutsu, Tatsuya', 'Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.|Center for iPS Cell Research and Application (CiRA), Kyoto University, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.|Faculty of Science and Technology, Keio University, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.|Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.|Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 2, 'Base Pairing|Computational Biology|Molecular Docking Simulation|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw603', '27663495', 'RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data.'),
(80341, 2017, 'bio', 'FBB: a fast Bayesian-bound tool to calibrate RNA-seq aligners', 'Rodriguez-Lujan, Irene|Hasty, Jeff|Huerta, Ramón', 'BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093-0328, USA.|BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093-0328, USA.|BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093-0328, USA.', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Bayes Theorem|Calibration|Escherichia coli|Genome|RNA|Saccharomyces cerevisiae|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw608', '27663496', 'Despite RNA-seq reads provide quality scores that represent the probability of calling a correct base, these values are not probabilistically integrated in most alignment algorithms. Based on the quality scores of the reads, we propose to calculate a lower bound of the probability of alignment of any fast alignment algorithm that generates SAM files. This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference to compare alignment results across different algorithms. This Bayesian Bound intends to provide additional support to the current state-of-the-art aligners, not to replace them.'),
(80342, 2017, 'bio', 'AcCNET (Accessory Genome Constellation Network): comparative genomics software for accessory genome analysis using bipartite networks', 'Lanza, Val F|Baquero, Fernando|de la Cruz, Fernando|Coque, Teresa M', 'Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.|Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.|Departamento de Biología Molecular, Universidad de Cantabria and Instituto de Biomedicina y Biotecnología de Cantabria IBBTEC (UC-CSIC), Santander, Spain.|Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.', 'Spain|Spain|Spain|Spain', 17, 'Bacteria|Genome, Bacterial|Genomics|Metagenome|Phylogeny|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/btw601', '27663497', 'AcCNET (Accessory genome Constellation Network) is a Perl application that aims to compare accessory genomes of a large number of genomic units, both at qualitative and quantitative levels. Using the proteomes extracted from the analysed genomes, AcCNET creates a bipartite network compatible with standard network analysis platforms. AcCNET allows merging phylogenetic and functional information about the concerned genomes, thus improving the capability of current methods of network analysis. The AcCNET bipartite network opens a new perspective to explore the pangenome of bacterial species, focusing on the accessory genome behind the idiosyncrasy of a particular strain and/or population.'),
(80343, 2017, 'bio', 'Robust classification of single-cell transcriptome data by nonnegative matrix factorization', 'Shao, Chunxuan|Höfer, Thomas', 'Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.|Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.', 'Germany|Germany', 0, 'Animals|Cerebral Cortex|Computational Biology|Gene Expression Profiling|Mice|Sequence Analysis, RNA|Single-Cell Analysis|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw607', '27663498', 'Single-cell transcriptome data provide unprecedented resolution to study heterogeneity in cell populations and present a challenge for unsupervised classification. Popular methods, like principal component analysis (PCA), often suffer from the high level of noise in the data.'),
(80344, 2017, 'bio', 'FastRFS: fast and accurate Robinson-Foulds Supertrees using constrained exact optimization', 'Vachaspati, Pranjal|Warnow, Tandy', 'Department of Computer Science.|Department of Computer Science.', '-|-', 16, 'Algorithms|Animals|Bayes Theorem|Genomics|Humans|Likelihood Functions|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btw600', '27663499', 'The estimation of phylogenetic trees is a major part of many biological dataset analyses, but maximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate-sized datasets. Supertree methods, which are used to construct trees from trees computed on subsets, are critically important tools for enabling the statistical estimation of phylogenies for large and potentially heterogeneous datasets. Supertree estimation is itself NP-hard, and no current supertree method has sufficient accuracy and scalability to provide good accuracy on the large datasets that supertree methods were designed for, containing thousands of species and many subset trees.'),
(80345, 2017, 'bio', 'FoldAtlas: a repository for genome-wide RNA structure probing data', 'Norris, Matthew|Kwok, Chun Kit|Cheema, Jitender|Hartley, Matthew|Morris, Richard J|Aviran, Sharon|Ding, Yiliang', 'John Innes Centre, Norwich Research Park, Norwich, UK.|Department of Biology and Chemistry, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.|John Innes Centre, Norwich Research Park, Norwich, UK.|John Innes Centre, Norwich Research Park, Norwich, UK.|John Innes Centre, Norwich Research Park, Norwich, UK.|Department of Biomedical Engineering and Genome Center, UC Davis, Davis, CA, USA.|John Innes Centre, Norwich Research Park, Norwich, UK.', 'United Kingdom|China|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Arabidopsis|Computer Simulation|Databases, Nucleic Acid|Nucleic Acid Conformation|RNA|RNA, Plant|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw611', '27663500', 'Most RNA molecules form internal base pairs, leading to a folded secondary structure. Some of these structures have been demonstrated to be functionally significant. High-throughput RNA structure chemical probing methods generate millions of sequencing reads to provide structural constraints for RNA secondary structure prediction. At present, processed data from these experiments are difficult to access without computational expertise. Here we present FoldAtlas, a web interface for accessing raw and processed structural data across thousands of transcripts. FoldAtlas allows a researcher to easily locate, view, and retrieve probing data for a given RNA molecule. We also provide in silico and in vivo secondary structure predictions for comparison, visualized in the browser as circle plots and topology diagrams. Data currently integrated into FoldAtlas are from a new high-depth Structure-seq data analysis in Arabidopsis thaliana, released with this work.'),
(80346, 2017, 'bio', 'Improved methods for multi-trait fine mapping of pleiotropic risk loci', 'Kichaev, Gleb|Roytman, Megan|Johnson, Ruth|Eskin, Eleazar|Lindström, Sara|Kraft, Peter|Pasaniuc, Bogdan', 'Bioinformatics Interdepartmental Program.|Bioinformatics Interdepartmental Program.|Department of Mathematics.|Bioinformatics Interdepartmental Program.|Department of Epidemiology, University of Washington, Seattle, WA, USA.|Program in Genetic Epidemiology and Statistical Genetics, Harvard School T.H. Chan of Public Health, Boston, MA, USA.|Bioinformatics Interdepartmental Program.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Chromosome Mapping|Genetic Diseases, Inborn|Genetic Loci|Genetic Pleiotropy|Genome-Wide Association Study|Genomics|Humans|Linkage Disequilibrium|Lipid Metabolism|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw615', '27663501', 'Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.'),
(80347, 2017, 'bio', 'LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis', 'Zheng, Jie|Erzurumluoglu, A Mesut|Elsworth, Benjamin L|Kemp, John P|Howe, Laurence|Haycock, Philip C|Hemani, Gibran|Tansey, Katherine|Laurin, Charles|Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium|Pourcain, Beate St|Warrington, Nicole M|Finucane, Hilary K|Price, Alkes L|Bulik-Sullivan, Brendan K|Anttila, Verneri|Paternoster, Lavinia|Gaunt, Tom R|Evans, David M|Neale, Benjamin M', 'MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.', 'United Kingdom|United Kingdom|United Kingdom|Australia|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Australia|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 0, 'Databases, Nucleic Acid|Female|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Male|Phenotype|Polymorphism, Single Nucleotide|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btw613', '27663502', 'LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80348, 2017, 'bio', 'cMapper: gene-centric connectivity mapper for EBI-RDF platform', 'Shoaib, Muhammad|Ansari, Adnan Ahmad|Ahn, Sung-Min', 'Department of Biomedical Engineering, College of Medicine, University of Ulsan, Asan Medical Center, Seoul, Republic of Korea.|Department of Biomedical Engineering, College of Medicine, University of Ulsan, Asan Medical Center, Seoul, Republic of Korea.|Gachon Institute of Genome Medicine and Sciences.', '-|-|-', 0, 'Biological Ontologies|Databases, Factual|Gene Expression|Genes|Humans|Internet|Metabolic Networks and Pathways|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw612', '27667790', 'In this era of biological big data, data integration has become a common task and a challenge for biologists. The Resource Description Framework (RDF) was developed to enable interoperability of heterogeneous datasets. The EBI-RDF platform enables an efficient data integration of six independent biological databases using RDF technologies and shared ontologies. However, to take advantage of this platform, biologists need to be familiar with RDF technologies and SPARQL query language. To overcome this practical limitation of the EBI-RDF platform, we developed cMapper, a web-based tool that enables biologists to search the EBI-RDF databases in a gene-centric manner without a thorough knowledge of RDF and SPARQL.'),
(80349, 2016, 'bio', 'LongISLND: in silico sequencing of lengthy and noisy datatypes', 'Lau, Bayo|Mohiyuddin, Marghoob|Mu, John C|Fang, Li Tai|Bani Asadi, Narges|Dallett, Carolina|Lam, Hugo Y K', 'Roche Sequencing Solutions, Belmont, CA 94002, USA.|Roche Sequencing Solutions, Belmont, CA 94002, USA.|Roche Sequencing Solutions, Belmont, CA 94002, USA.|Roche Sequencing Solutions, Belmont, CA 94002, USA.|Roche Sequencing Solutions, Belmont, CA 94002, USA.|Roche Sequencing Solutions, Pleasanton, CA 94588, USA.|Roche Sequencing Solutions, Belmont, CA 94002, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Simulation|High-Throughput Nucleotide Sequencing|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw602', '27667791', 'LongISLND is a software package designed to simulate sequencing data according to the characteristics of third generation, single-molecule sequencing technologies. The general software architecture is easily extendable, as demonstrated by the emulation of Pacific Biosciences (PacBio) multi-pass sequencing with P5 and P6 chemistries, producing data in FASTQ, H5, and the latest PacBio BAM format. We demonstrate its utility by downstream processing with consensus building and variant calling.'),
(80350, 2017, 'bio', 'Detecting subnetwork-level dynamic correlations', 'Yan, Yan|Qiu, Shangzhao|Jin, Zhuxuan|Gong, Sihong|Bai, Yun|Lu, Jianwei|Yu, Tianwei', 'School of Software Engineering, Tongji University, Shanghai, China.|School of Software Engineering, Tongji University, Shanghai, China.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.|School of Software Engineering, Tongji University, Shanghai, China.|Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA.|School of Software Engineering, Tongji University, Shanghai, China.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.', 'China|China|United States|China|United States|China|United States', 5, 'Computer Simulation|Gene Regulatory Networks|Humans|Protein Interaction Maps|Signal Transduction|Software|Systems Biology|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw616', '27667792', 'The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them.'),
(80351, 2017, 'bio', 'LAMSA: fast split read alignment with long approximate matches', 'Liu, Bo|Gao, Yan|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|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw594', '27667793', 'Read length is continuously increasing with the development of novel high-throughput sequencing technologies, which has enormous potentials on cutting-edge genomic studies. However, longer reads could more frequently span the breakpoints of structural variants (SVs) than that of shorter reads. This may greatly influence read alignment, since most state-of-the-art aligners are designed for handling relatively small variants in a co-linear alignment framework. Meanwhile, long read alignment is still not as efficient as that of short reads, which could be also a bottleneck for the upcoming wide application.'),
(80352, 2017, 'bio', 'Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond', 'He, Shuaibing|Li, Manman|Ye, Xiaotong|Wang, Hongyu|Yu, Wenkang|He, Wenjing|Wang, Yun|Qiao, Yanjiang', 'Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Xinjiang Medical University Institute of TCM, Urumuqi, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.|Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China.', 'China|China|China|China|China|China|China|China', 11, 'Animals|Area Under Curve|Computer Simulation|Cytochrome P-450 Enzyme System|Humans|Metabolic Networks and Pathways|Models, Chemical|Oxidation-Reduction|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btw617', '27667794', 'The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes.'),
(80353, 2017, 'bio', 'Combining multiple tools outperforms individual methods in gene set enrichment analyses', 'Alhamdoosh, Monther|Ng, Milica|Wilson, Nicholas J|Sheridan, Julie M|Huynh, Huy|Wilson, Michael J|Ritchie, Matthew E', 'CSL Limited, Bio21 Institute, Parkville, Australia.|CSL Limited, Bio21 Institute, Parkville, Australia.|CSL Limited, Bio21 Institute, Parkville, Australia.|ACRF Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|CSL Limited, Bio21 Institute, Parkville, Australia.|CSL Limited, Bio21 Institute, Parkville, Australia.|Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 42, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Female|Gene Expression Profiling|Humans|Interleukin-13|Leukocytes, Mononuclear|Mammary Glands, Human|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw623', '27694195', 'Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular dataset. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions.'),
(80354, 2017, 'bio', 'bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria and Escherichia coli', 'Shahmuradov, Ilham Ayub|Mohamad Razali, Rozaimi|Bougouffa, Salim|Radovanovic, Aleksandar|Bajic, Vladimir B', 'Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), 4700 King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), 4700 King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), 4700 King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), 4700 King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), 4700 King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Cyanobacteria|DNA-Directed RNA Polymerases|Escherichia coli|Genome, Bacterial|Promoter Regions, Genetic|Sigma Factor|Software|Transcription Initiation Site|Transcription, Genetic|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btw629', '27694198', 'The computational search for promoters in prokaryotes remains an attractive problem in bioinformatics. Despite the attention it has received for many years, the problem has not been addressed satisfactorily. In any bacterial genome, the transcription start site is chosen mostly by the sigma (xcfx83) factor proteins, which control the gene activation. The majority of published bacterial promoter prediction tools target xcfx83 70 promoters in Escherichia coli . Moreover, no xcfx83-specific classification of promoters is available for prokaryotes other than for E. coli .'),
(80355, 2017, 'bio', 'Visualizing the geography of genetic variants', 'Marcus, Joseph H|Novembre, John', 'Department of Human Genetics.|Department of Human Genetics.', '-|-', 34, 'Genetic Variation|Genome, Human|Genomics|Humans|Phylogeography|Software', 'Journal Article', '10.1093/bioinformatics/btw643', '27742697', 'One of the key characteristics of any genetic variant is its geographic distribution. The geographic distribution can shed light on where an allele first arose, what populations it has spread to, and in turn on how migration, genetic drift, and natural selection have acted. The geographic distribution of a genetic variant can also be of great utility for medical/clinical geneticists and collectively many genetic variants can reveal population structure. Here we develop an interactive visualization tool for rapidly displaying the geographic distribution of genetic variants. Through a REST API and dynamic front-end, the Geography of Genetic Variants (GGV) browser ( http://popgen.uchicago.edu/ggv/ ) provides maps of allele frequencies in populations distributed across the globe.'),
(80356, 2017, 'bio', 'HiLive: real-time mapping of illumina reads while sequencing', 'Lindner, Martin S|Strauch, Benjamin|Schulze, Jakob M|Tausch, Simon H|Dabrowski, Piotr W|Nitsche, Andreas|Renard, Bernhard Y', 'Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.|Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Algorithms|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw659', '27794555', 'Next Generation Sequencing is increasingly used in time critical, clinical applications. While read mapping algorithms have always been optimized for speed, they follow a sequential paradigm and only start after finishing of the sequencing run and conversion of files. Since Illumina machines write intermediate output results, HiLive performs read mapping while still sequencing and thereby drastically reduces crucial overall sample analysis time, e.g. in precision medicine.'),
(80357, 2017, 'bio', 'gargammel: a sequence simulator for ancient DNA', 'Renaud, Gabriel|Hanghøj, Kristian|Willerslev, Eske|Orlando, Ludovic', 'Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350K Copenhagen, Denmark.|Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350K Copenhagen, Denmark.|Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350K Copenhagen, Denmark.|Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350K Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 0, 'Animals|Computer Simulation|DNA Contamination|DNA Damage|DNA, Ancient|Humans|Postmortem Changes|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw670', '27794556', 'Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico , we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets.'),
(80358, 2017, 'bio', 'FractBias: a graphical tool for assessing fractionation bias following polyploidy', 'Joyce, Blake L|Haug-Baltzell, Asher|Davey, Sean|Bomhoff, Matthew|Schnable, James C|Lyons, Eric', 'BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|Genetis GIDP, University of Arizona, Tucson, AZ 85721, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.|Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Evolution, Molecular|Genes, Plant|Genome, Plant|Genomics|Plants|Polyploidy|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw666', '27794557', 'Following polyploidy events, genomes undergo massive reduction in gene content through a process known as fractionation. Importantly, the fractionation process is not always random, and a bias as to which homeologous chromosome retains or loses more genes can be observed in some species. The process of characterizing whole genome fractionation requires identifying syntenic regions across genomes followed by post-processing of those syntenic datasets to identify and plot gene retention patterns. We have developed a tool, FractBias, to calculate and visualize gene retention and fractionation patterns across whole genomes. Through integration with SynMap and its parent platform CoGe, assembled genomes are pre-loaded and available for analysis, as well as letting researchers integrate their own data with security options to keep them private or make them publicly available.'),
(80359, 2017, 'bio', 'Efficient detection of differentially methylated regions using DiMmeR', 'Almeida, Diogo|Skov, Ida|Silva, Artur|Vandin, Fabio|Tan, Qihua|Röttger, Richard|Baumbach, Jan', 'Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark.|Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.|Laboratory for Genomics and Bioinformatics, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil.|Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.|Clinical Institute (CI), University of Southern Denmark, 5000 Odense, Denmark.|Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.|Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.', 'Denmark|Denmark|Brazil|Denmark|Denmark|Denmark|Denmark', 4, 'CpG Islands|DNA Methylation|Epigenomics|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw657', '27794558', 'Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.'),
(80360, 2017, 'bio', 'Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach', 'Marín de Mas, Igor|Fanchon, Eric|Papp, Balázs|Kalko, Susana|Roca, Josep|Cascante, Marta', 'Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.|Université Grenoble Alpes-CNRS, TIMC-IMAG UMR 5525, Faculté de Médecine, Grenoble 38041, France.|Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary.|Bioinformatics Core Facility, IDIBAPS-CEK, Hospital Clínic, University de Barcelona, Barcelona 08036, Spain.|Institut d Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.', 'Spain|France|Hungary|Spain|Spain|Spain', 0, 'Energy Metabolism|Exercise|Humans|Muscle, Skeletal|Pulmonary Disease, Chronic Obstructive|Reactive Oxygen Species|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw566', '27794560', 'Skeletal muscle dysfunction is a systemic effect in one-third of patients with chronic obstructive pulmonary disease (COPD), characterized by high reactive-oxygen-species (ROS) production and abnormal endurance training-induced adaptive changes. However, the role of ROS in COPD remains unclear, not least because of the lack of appropriate tools to study multifactorial diseases.'),
(80361, 2017, 'bio', 'Uncertainty estimation of predictions of peptides chromatographic retention times in shotgun proteomics', 'Maboudi Afkham, Heydar|Qiu, Xuanbin|The, Matthew|Käll, Lukas', 'Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, 17121 Solna, Sweden.|Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, 17121 Solna, Sweden.|Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, 17121 Solna, Sweden.|Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, 17121 Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 0, 'Amino Acid Sequence|Chromatography, Liquid|Mass Spectrometry|Models, Theoretical|Peptides|Proteomics|Software|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btw619', '27797755', 'Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptides retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction.'),
(80362, 2017, 'bio', 'MIToSjl: mutual information tools for protein sequence analysis in the Julia language', 'Zea, Diego J|Anfossi, Diego|Nielsen, Morten|Marino-Buslje, Cristina', 'Structural Bioinformatics Unit, Fundación Instituto Leloir, C1405BWE, Ciudad Autónoma de Buenos Aires, Argentina.|Structural Bioinformatics Unit, Fundación Instituto Leloir, C1405BWE, Ciudad Autónoma de Buenos Aires, Argentina.|Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.|Structural Bioinformatics Unit, Fundación Instituto Leloir, C1405BWE, Ciudad Autónoma de Buenos Aires, Argentina.', 'Argentina|Argentina|Denmark|Argentina', 0, 'Humans|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw646', '27797756', 'MIToS is an environment for mutual information analysis and a framework for protein multiple sequence alignments (MSAs) and protein structures (PDB) management in Julia language. It integrates sequence and structural information through SIFTS, making Pfam MSAs analysis straightforward. MIToS streamlines the implementation of any measure calculated from residue contingency tables and its optimization and testing in terms of protein contact prediction. As an example, we implemented and tested a BLOSUM62-based pseudo-count strategy in mutual information analysis.'),
(80363, 2017, 'bio', 'stAIcalc: tRNA adaptation index calculator based on species-specific weights', 'Sabi, Renana|Volvovitch Daniel, Renana|Tuller, Tamir', 'Department of Biomedical Engineering.|Department of Biomedical Engineering.|Department of Biomedical Engineering.', '-|-|-', 20, 'Archaea|Bacteria|Computational Biology|Eukaryota|RNA, Transfer|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btw647', '27797757', 'The tRNA Adaptation Index (tAI) is a tRNA-centric measure of translation efficiency which includes weights that take into account the efficiencies of the different wobble interactions. To enable the calculation of the index based on a species-specific inference of these weights, we created the stAI calc . The calculator includes optimized tAI weights for 100 species from the three domains of life along with a standalone software package that optimizes the weights for new organisms. The tAI with the optimized weights should enable performing large scale studies in disciplines such as molecular evolution, genomics, systems biology and synthetic biology.'),
(80364, 2017, 'bio', 'gsrc: an R package for genome structure rearrangement calling', 'Grandke, Fabian|Snowdon, Rod|Samans, Birgit', 'Department of Plant Breeding, IFZ Research Centre for BioSystems, Land Use and Nutrition, Justus Liebig University, 35392 Giessen, Germany.|Department of Plant Breeding, IFZ Research Centre for BioSystems, Land Use and Nutrition, Justus Liebig University, 35392 Giessen, Germany.|Department of Plant Breeding, IFZ Research Centre for BioSystems, Land Use and Nutrition, Justus Liebig University, 35392 Giessen, Germany.', 'Germany|Germany|Germany', 8, 'Brassica napus|Genome, Plant|Genomics|Polyploidy|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw648', '27797758', 'Genome structure rearrangements are a common phenomenon in allopolyploid species. Deletions, duplications and homeologous non-reciprocal translocations (HNRT) between the highly similar subgenomes can be observed, which are known to have a large impact on phenotypic traits. Current research is limited because these rearrangements can be located genome wide only by cost intensive sequencing approaches and not reliably in high-density array genotyping data. We developed gsrc, an R-package to detect genome structure rearrangements from genotyping data in allopolyploid species including exchanges between subgenomes. We exemplarily apply gsrc to a publicly available Brassica napus dataset.'),
(80365, 2017, 'bio', 'Transfer learning across ontologies for phenome-genome association prediction', 'Petegrosso, Raphael|Park, Sunho|Hwang, Tae Hyun|Kuang, Rui', 'Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Ontology|Genome|Humans|Models, Genetic|Phenotype|Protein Interaction Maps', 'Journal Article', '10.1093/bioinformatics/btw649', '27797759', 'To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.'),
(80366, 2017, 'bio', 'Improving cross-study prediction through addon batch effect adjustment or addon normalization', 'Hornung, Roman|Causeur, David|Bernau, Christoph|Boulesteix, Anne-Laure', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany.|Applied Mathematics Department, Agrocampus Ouest, Rennes, France.|Leibniz Supercomputing Center, Garching, Germany.|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany.', 'Germany|France|Germany|Germany', 0, 'Algorithms|Datasets as Topic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Research Design|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw650', '27797760', 'To date most medical tests derived by applying classification methods to high-dimensional molecular data are hardly used in clinical practice. This is partly because the prediction error resulting when applying them to external data is usually much higher than internal error as evaluated through within-study validation procedures. We suggest the use of addon normalization and addon batch effect removal techniques in this context to reduce systematic differences between external data and the original dataset with the aim to improve prediction performance.'),
(80367, 2017, 'bio', 'Isomorphic semantic mapping of variant call format (VCF2RDF)', 'Penha, Emanuel Diego S|Iriabho, Egiebade|Dussaq, Alex|de Oliveira, Diana Magalhães|Almeida, Jonas S', 'Department of Pathology, Informatics Division, University of Alabama at Birmingham, Birmingham, AL 35233, USA.|Department of Pathology, Informatics Division, University of Alabama at Birmingham, Birmingham, AL 35233, USA.|Department of Pathology, Informatics Division, University of Alabama at Birmingham, Birmingham, AL 35233, USA.|Rede Nordeste de Biotecnologia, Universidade Estadual do Ceará, Fortaleza CE 60740-000, Brazil.|Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA.', 'United States|United States|United States|Brazil|United States', 1, 'Genetic Variation|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Semantics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw652', '27797761', 'The move of computational genomics workflows to Cloud Computing platforms is associated with a new level of integration and interoperability that challenges existing data representation formats. The Variant Calling Format (VCF) is in a particularly sensitive position in that regard, with both clinical and consumer-facing analysis tools relying on this self-contained description of genomic variation in Next Generation Sequencing (NGS) results. In this report we identify an isomorphic map between VCF and the reference Resource Description Framework. RDF is advanced by the World Wide Web Consortium (W3C) to enable representations of linked data that are both distributed and discoverable. The resulting ability to decompose VCF reports of genomic variation without loss of context addresses the need to modularize and govern NGS pipelines for Precision Medicine. Specifically, it provides the flexibility (i.e. the indexing) needed to support the wide variety of clinical scenarios and patient-facing governance where only part of the VCF data is fitting.'),
(80368, 2017, 'bio', 'CLIP Tool Kit (CTK): a flexible and robust pipeline to analyze CLIP sequencing data', 'Shah, Ankeeta|Qian, Yingzhi|Weyn-Vanhentenryck, Sebastien M|Zhang, Chaolin', 'Department of Systems BiologyDepartment of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, NY 10032, USA.|Department of Systems BiologyDepartment of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, NY 10032, USA.|Department of Systems BiologyDepartment of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, NY 10032, USA.|Department of Systems BiologyDepartment of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, NY 10032, USA.', 'United States|United States|United States|United States', 31, 'High-Throughput Nucleotide Sequencing|Humans|Immunoprecipitation|Protein Binding|RNA|RNA-Binding Proteins|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw653', '27797762', 'UV cross-linking and immunoprecipitation (CLIP), followed by high-throughput sequencing, is a powerful biochemical assay that maps in vivo protein-RNA interactions on a genome-wide scale. The CLIP Tool Kit (CTK) aims at providing a set of tools for flexible, streamlined and comprehensive CLIP data analysis. This software package extends the scope of our original CIMS package.'),
(80369, 2017, 'bio', 'Rabifier2: an improved bioinformatic classifier of Rab GTPases', 'Surkont, Jaroslaw|Diekmann, Yoan|Pereira-Leal, José B', 'Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal.|Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal.|Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal.', 'Portugal|Portugal|Portugal', 0, 'Animals|Computational Biology|Eukaryota|Humans|Software|rab GTP-Binding Proteins', 'Journal Article', '10.1093/bioinformatics/btw654', '27797763', 'The Rab family of small GTPases regulates and provides specificity to the endomembrane trafficking system, each Rab subfamily is associated with specific pathways. Thus, characterization of Rab repertoires provides functional information about organisms and evolution of the eukaryotic cell. Yet, the complex structure of the Rab family limits the application of existing methods for protein classification. Here, we present a major redesign of the Rabifier, a bioinformatic pipeline for detection and classification of Rab GTPases. It is more accurate, significantly faster than the original version and is now open source, both the code and the data, allowing for community participation.'),
(80370, 2017, 'bio', 'RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications', 'Alkan, Ferhat|Erten, Cesim', 'Center for Non-coding RNA in Technology and Health.|Department of Computer Engineering, Kadir Has University, Cibali, 34083 Istanbul, Turkey.', '-|Turkey', 0, 'Algorithms|Animals|Gene Ontology|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw655', '27797764', 'Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However, a major issue with these networks is their erroneous nature, they contain false-positive interactions and usually many more false-negatives. Recently, several computational methods have been proposed for network reconstruction based on topology, where given an input PPI network the goal is to reconstruct the network by identifying false-positives/-negatives as correctly as possible.'),
(80371, 2017, 'bio', 'yMap: an automated method to map yeast variants to protein modifications and functional regions', 'Arslan, Ahmed|van Noort, Vera', 'KU Leuven, Centre for Microbial and Plants Genetics (CMPG), 3001 Leuven, Belgium.|KU Leuven, Centre for Microbial and Plants Genetics (CMPG), 3001 Leuven, Belgium.', 'Belgium|Belgium', 0, 'Fungal Proteins|Genetic Variation|Protein Processing, Post-Translational|Proteomics|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw658', '27797766', 'Recent advances in sequence technology result in large datasets of sequence variants. For the human genome, several tools are available to predict the impact of these variants on gene and protein functions. However, for model organisms such as yeast such tools are lacking, specifically to predict the effect of protein sequence altering variants on the protein level. We present a python framework that enables users to map in a fully automated fashion large set of variants to protein functional regions and post-translationally modified residues. Furthermore, we provide the user with the possibility to retrieve predicted functional information on modified residues from other resources for example that are predicted to play a role in protein-protein interactions. The results are complemented by statistical tests to highlight the significance of underlying functions and pathways affected by mutations. We show the application of this package on a yeast dataset derived from a recent evolutionary experiment on adaptation to ethanol.'),
(80372, 2017, 'bio', 'miRNAmeConverter: an R/bioconductor package for translating mature miRNA names to different miRBase versions', 'Haunsberger, Stefan J|Connolly, Niamh M C|Prehn, Jochen H M', 'Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Centre for Systems Medicine, Dublin 2, Ireland.|Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Centre for Systems Medicine, Dublin 2, Ireland.|Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Centre for Systems Medicine, Dublin 2, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Animals|Gene Ontology|Humans|Mice|MicroRNAs|Molecular Sequence Annotation|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btw660', '27797767', 'The miRBase database is the central and official repository for miRNAs and the current release is miRBase version 21.0. Name changes in different miRBase releases cause inconsistencies in miRNA names from version to version. When working with only a small number of miRNAs the translation can be done manually. However, with large sets of miRNAs, the necessary correction of such inconsistencies becomes burdensome and error-prone. We developed miRNAmeConverter , available as a Bioconductor R package and web interface that addresses the challenges associated with mature miRNA name inconsistencies. The main algorithm implemented enables high-throughput automatic translation of species-independent mature miRNA names to user selected miRBase versions. The web interface enables users less familiar with R to translate miRNA names given in form of a list or embedded in text and download of the results.'),
(80373, 2017, 'bio', 'ViSiBooL-visualization and simulation of Boolean networks with temporal constraints', 'Schwab, Julian|Burkovski, Andre|Siegle, Lea|Müssel, Christoph|Kestler, Hans A', 'Institute of Medical Systems Biology.|Institute of Medical Systems Biology.|Institute of Medical Systems Biology.|Institute of Medical Systems Biology.|Institute of Medical Systems Biology.', '-|-|-|-|-', 0, 'Algorithms|Computational Biology|Computer Simulation|Gene Regulatory Networks|Humans|Models, Genetic|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btw661', '27797768', 'Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret. ViSiBooL aims at providing a graphical way of modeling and simulating networks. By providing visualizations of static and dynamic network properties simultaneously, it is possible to directly observe the effects of changes in the network structure on the behavior. In order to address the challenges of clear design and a user-friendly graphical user interface (GUI), ViSiBooL implements visual representations of BNs. Additionally temporal extensions of the BNs for the modeling of regulatory time delays are incorporated. The GUI of ViSiBooL allows to model, organize, simulate and visualize BNs as well as corresponding simulation results such as attractors. Attractor searches are performed in parallel to the modeling process. Hence, changes in the network behavior are visualized at the same time.'),
(80374, 2017, 'bio', 'Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework', 'Yang, Hai|Wei, Qiang|Zhong, Xue|Yang, Hushan|Li, Bingshan', 'Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA.|Vanderbilt Genetics Institute, Nashville, TN, USA.|Department of Medical Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA.|Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States|United States|United States', 12, 'Bayes Theorem|Cell Transformation, Neoplastic|DNA Methylation|Female|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Genomics|Humans|Models, Statistical|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btw662', '27797769', 'Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data.'),
(80375, 2017, 'bio', 'KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies', 'Mapleson, Daniel|Garcia Accinelli, Gonzalo|Kettleborough, George|Wright, Jonathan|Clavijo, Bernardo J', 'Earlham Institute, Norwich Research Park, Norwich, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 67, 'Fraxinus|Genome, Plant|High-Throughput Nucleotide Sequencing|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw663', '27797770', 'De novo assembly of whole genome shotgun (WGS) next-generation sequencing (NGS) data benefits from high-quality input with high coverage. However, in practice, determining the quality and quantity of useful reads quickly and in a reference-free manner is not trivial. Gaining a better understanding of the WGS data, and how that data is utilized by assemblers, provides useful insights that can inform the assembly process and result in better assemblies.'),
(80376, 2017, 'bio', 'Imputing gene expression to maximize platform compatibility', 'Zhou, Weizhuang|Han, Lichy|Altman, Russ B', 'Department of Bioengineering.|Biomedical Informatics Training Program.|Department of Bioengineering.', '-|-|-', 9, 'Gene Expression Profiling|Gene Expression Regulation|Genomics|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btw664', '27797771', 'Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples, the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis.'),
(80377, 2017, 'bio', 'ImpulseDE: detection of differentially expressed genes in time series data using impulse models', 'Sander, Jil|Schultze, Joachim L|Yosef, Nir', 'Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, 53115, Germany.|Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, 53115, Germany.|Electrical Engineering and Computer Science, Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720-1776, USA.', 'Germany|Germany|United States', 10, 'Animals|Gene Expression Profiling|Gene Expression Regulation|Mice|Sequence Analysis, RNA|Software|T-Lymphocytes|Time', 'Journal Article', '10.1093/bioinformatics/btw665', '27797772', 'Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data.'),
(80378, 2017, 'bio', 'LRFragLib: an effective algorithm to identify fragments for de novo protein structure prediction', 'Wang, Tong|Yang, Yuedong|Zhou, Yaoqi|Gong, Haipeng', 'MOE Key Laboratory of Bioinformatics, School of Life Sciences.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.|MOE Key Laboratory of Bioinformatics, School of Life Sciences.', '-|Australia|Australia|-', 0, 'Algorithms|Caspases|Computational Biology|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw668', '27797773', 'The quality of fragment library determines the efficiency of fragment assembly, an approach that is widely used in most de novo protein-structure prediction algorithms. Conventional fragment libraries are constructed mainly based on the identities of amino acids, sometimes facilitated by predicted information including dihedral angles and secondary structures. However, it remains challenging to identify near-native fragment structures with low sequence homology.'),
(80379, 2017, 'bio', 'Web-igloo: a web based platform for multivariate data visualization', 'Kuntal, Bhusan K|Mande, Sharmila S', 'Bio-Sciences R&D Division, TCS Research, Tata Research Development & Design Centre, Pune, Maharashtra 411 013, India.|Bio-Sciences R&D Division, TCS Research, Tata Research Development & Design Centre, Pune, Maharashtra 411 013, India.', 'India|India', 3, 'Data Mining|Humans|Internet|Metagenomics|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btw669', '27797774', 'The majority of data generated routinely from various experiments are essentially multivariate, often categorized with multiple experimental metadata. Analyzing such results with interactive visualizations often yields interesting and intuitive results which otherwise remains undisclosed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80380, 2017, 'bio', 'Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning', 'Hocking, Toby Dylan|Goerner-Potvin, Patricia|Morin, Andreanne|Shao, Xiaojian|Pastinen, Tomi|Bourque, Guillaume', 'Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.|Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.|Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.|Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.|Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.|Department of Human Genetics, McGill University, H3A-1A4, Montréal, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 5, 'Animals|Chromatin Immunoprecipitation|Genomics|Humans|Sequence Analysis, DNA|Software|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btw672', '27797775', 'Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks. The main idea is to manually label a small subset of the genome, and then learn a model that makes consistent peak predictions on the rest of the genome.'),
(80381, 2017, 'bio', 'INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery', 'Zhang, Jin|Mardis, Elaine R|Maher, Christopher A', 'McDonnell Genome Institute.|McDonnell Genome Institute.|McDonnell Genome Institute.', '-|-|-', 41, 'Antigens, Neoplasm|Gene Fusion|Genomics|Humans|Male|Prostatic Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw674', '27797777', 'While high-throughput sequencing (HTS) has been used successfully to discover tumor-specific mutant peptides (neoantigens) from somatic missense mutations, the field currently lacks a method for identifying which gene fusions may generate neoantigens.'),
(80382, 2017, 'bio', 'Network-based analysis of omics data: the LEAN method', 'Gwinner, Frederik|Boulday, Gwénola|Vandiedonck, Claire|Arnould, Minh|Cardoso, Cécile|Nikolayeva, Iryna|Guitart-Pla, Oriol|Denis, Cécile V|Christophe, Olivier D|Beghain, Johann|Tournier-Lasserve, Elisabeth|Schwikowski, Benno', 'Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMRS 958, F-75010 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.|Systems Biology Lab, C3BI, USR 3756, Institut Pasteur/CNRS, Institut Pasteur, F-75015 Paris, France.|Systems Biology Lab, C3BI, USR 3756, Institut Pasteur/CNRS, Institut Pasteur, F-75015 Paris, France.|Unité 1176, INSERM, Univ Paris-Sud, Université Paris-Saclay, F-94270 Le Kremlin-Bicêtre, France.|Unité 1176, INSERM, Univ Paris-Sud, Université Paris-Saclay, F-94270 Le Kremlin-Bicêtre, France.|Functional Genetics of Infectious Diseases Unit, Institut Pasteur, F-75015 Paris, France.|Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.|Systems Biology Lab, C3BI, USR 3756, Institut Pasteur/CNRS, Institut Pasteur, F-75015 Paris, France.', 'France|France|France|France|France|France|France|France|France|France|France|France', 12, 'Animals|Computational Biology|Gene Regulatory Networks|Hemangioma, Cavernous, Central Nervous System|Humans|Mice|Proteins|Software|Transcriptome|von Willebrand Factor', 'Journal Article', '10.1093/bioinformatics/btw676', '27797778', 'Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.'),
(80383, 2017, 'bio', 'JADOPPT: java based AutoDock preparing and processing tool', 'García-Pérez, Carlos|Peláez, Rafael|Therón, Roberto|Luis López-Pérez, José', 'Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Mexico.|Quimica Farmacéutica.|Departmento de Informática y Automática.|Quimica Farmacéutica.', 'Mexico|-|-|-', 2, 'Animals|Binding Sites|Cluster Analysis|Humans|Ligands|Molecular Docking Simulation|Protein Binding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw677', '27797779', 'AutoDock is a very popular software package for docking and virtual screening. However, currently it is hard work to visualize more than one result from the virtual screening at a time. To overcome this limitation we have designed JADOPPT, a tool for automatically preparing and processing multiple ligand-protein docked poses obtained from AutoDock. It allows the simultaneous visual assessment and comparison of multiple poses through clustering methods. Moreover, it permits the representation of reference ligands with known binding modes, binding site residues, highly scoring regions for the ligand, and the calculated binding energy of the best ranked results.'),
(80384, 2017, 'bio', 'Big Data Smart Socket (BDSS): a system that abstracts data transfer habits from end users', 'Watts, Nicholas A|Feltus, Frank A', 'Clemson Computing & Information Technology.|Clemson University Department of Genetics & Biochemistry, Clemson, SC 29634, USA.', 'United States|United States', 5, 'Computational Biology|Databases, Factual|Habits|Internet|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btw679', '27797780', 'The ability to centralize and store data for long periods on an end users computational resources is increasingly difficult for many scientific disciplines. For example, genomics data is increasingly large and distributed, and the data needs to be moved into workflow execution sites ranging from lab workstations to the cloud. However, the typical user is not always informed on emerging network technology or the most efficient methods to move and share data. Thus, the user defaults to using inefficient methods for transfer across the commercial internet.'),
(80385, 2017, 'bio', 'tcpl: the ToxCast pipeline for high-throughput screening data', 'Filer, Dayne L|Kothiya, Parth|Setzer, R Woodrow|Judson, Richard S|Martin, Matthew T', 'Oak Ridge Institute for Science Education Fellow at National Center for Computational Toxicology.|Oak Ridge Institute for Science Education Fellow at National Center for Computational Toxicology.|National Center for Computational Toxicology, US EPA, Research Triangle Park, NC, USA.|National Center for Computational Toxicology, US EPA, Research Triangle Park, NC, USA.|National Center for Computational Toxicology, US EPA, Research Triangle Park, NC, USA.', 'United States|United States|United States|United States|United States', 67, 'Algorithms|Computer Simulation|Dose-Response Relationship, Drug|Drug Evaluation, Preclinical|High-Throughput Screening Assays|Models, Biological|Software|Toxicity Tests', 'Journal Article', '10.1093/bioinformatics/btw680', '27797781', 'Large high-throughput screening (HTS) efforts are widely used in drug development and chemical toxicity screening. Wide use and integration of these data can benefit from an efficient, transparent and reproducible data pipeline. Summary: The tcpl R package and its associated MySQL database provide a generalized platform for efficiently storing, normalizing and dose-response modeling of large high-throughput and high-content chemical screening data. The novel dose-response modeling algorithm has been tested against millions of diverse dose-response series, and robustly fits data with outliers and cytotoxicity-related signal loss.'),
(80386, 2017, 'bio', 'ImageJ-MATLAB: a bidirectional framework for scientific image analysis interoperability', 'Hiner, Mark C|Rueden, Curtis T|Eliceiri, Kevin W', 'Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Humans|Image Interpretation, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btw681', '27797782', 'ImageJ-MATLAB is a lightweight Java library facilitating bi-directional interoperability between MATLAB and ImageJ. By defining a standard for translation between matrix and image data structures, researchers are empowered to select the best tool for their image-analysis tasks.'),
(80387, 2017, 'bio', 'PyBoolNet: a python package for the generation, analysis and visualization of boolean networks', 'Klarner, Hannes|Streck, Adam|Siebert, Heike', 'Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, Madison, WI 53706, USA.', 'United States|United States|United States', 15, 'Algorithms|Computational Biology|Gene Regulatory Networks|Models, Biological|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btw682', '27797783', 'The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts.'),
(80388, 2017, 'bio', 'BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods', 'Oyetunde, Tolutola|Zhang, Muhan|Chen, Yixin|Tang, Yinjie|Lo, Cynthia', 'Department of Energy, Environmental and Chemical Engineering.|Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.|Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.|Department of Energy, Environmental and Chemical Engineering.|Department of Energy, Environmental and Chemical Engineering.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Machine Learning|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btw684', '27797784', 'Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability.'),
(80389, 2016, 'bio', 'Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals', 'Hu, X|Dong, Q|Yang, J|Zhang, Y', 'Department of Energy, Environmental and Chemical Engineering.|Department of Energy, Environmental and Chemical Engineering.|Department of Energy, Environmental and Chemical Engineering.|Department of Energy, Environmental and Chemical Engineering.', '-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/btw637', '27899383', '-'),
(80390, 2017, 'bio', 'EGAD: ultra-fast functional analysis of gene networks', 'Ballouz, Sara|Weber, Melanie|Pavlidis, Paul|Gillis, Jesse', 'Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY 11797, USA.|Department of Mathematics and Computer Science, University of Leipzig, Leipzig, Germany.|Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver, Canada.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY 11797, USA.', 'United States|Germany|Canada|United States', 0, 'Animals|Computational Biology|Gene Regulatory Networks|Humans|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btw695', '27993773', 'Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit high-throughput analysis of gene networks, we have implemented a set of very efficient tools to calculate functional properties in networks based on guilt-by-association methods. ( xtending  uilt-by- ssociation by egree) allows gene networks to be evaluated with respect to hundreds or thousands of gene sets. The methods predict novel members of gene groups, assess how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. By allowing fast evaluations, whether of random sets or real functional ones, provides the user with an assessment of performance which can easily be used in controlled evaluations across many parameters.'),
(80391, 2017, 'bio', 'KODAMA: an R package for knowledge discovery and data mining', 'Cacciatore, Stefano|Tenori, Leonardo|Luchinat, Claudio|Bennett, Phillip R|MacIntyre, David A', 'Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.|Department of Clinical and Experimental Medicine.|Centro Risonanze Magnetiche, University of Florence, Florence, Italy.|Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.|Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|Italy|United Kingdom|United Kingdom', 12, 'Algorithms|Data Mining|Female|Humans|Magnetic Resonance Spectroscopy|Male|Software|Unsupervised Machine Learning|Urinalysis', 'Journal Article', '10.1093/bioinformatics/btw705', '27993774', 'KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms.'),
(80392, 2017, 'bio', 'GENIUS: web server to predict local gene networks and key genes for biological functions', 'Puelma, Tomas|Araus, Viviana|Canales, Javier|Vidal, Elena A|Cabello, Juan M|Soto, Alvaro|Gutiérrez, Rodrigo A', 'Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, Departamento de Ciencias de la Computación, Facultad de Ingeniería, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.|Millennium Nucleus Center for Plant Systems and Synthetic Biology, FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.', 'Chile|Chile|Chile|Chile|Chile|Chile|Chile', 0, 'Arabidopsis|Gene Regulatory Networks|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/btw702', '27993775', 'GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.'),
(80393, 2017, 'bio', 'fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets', 'Madrigal, Pedro', 'Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0SZ, UK.', 'United Kingdom', 2, 'Chromatin|Chromatin Immunoprecipitation|Epigenomics|High-Throughput Nucleotide Sequencing|Histones|Humans|Methylation|Protein Processing, Post-Translational|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw724', '27993776', 'Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We show how this method differs from other measures of correlation, and exemplify how it can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers.'),
(80394, 2017, 'bio', 'A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life', 'Umu, Sinan Ugur|Gardner, Paul P', 'School of Biological Sciences.|School of Biological Sciences.', '-|-', 20, 'Algorithms|Bacteria|Benchmarking|Databases, Genetic|Models, Theoretical|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw728', '27993777', 'The aim of this study is to assess the performance of RNA-RNA interaction prediction tools for all domains of life.'),
(80395, 2017, 'bio', 'LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering', 'Specht, Alicia T|Li, Jun', 'School of Biological Sciences.|School of Biological Sciences.', '-|-', 23, 'Algorithms|Animals|Computational Biology|Dendritic Cells|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Mice|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw729', '27993778', 'To construct gene co-expression networks based on single-cell RNA-Sequencing data, we present an algorithm called LEAP, which utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay.'),
(80396, 2017, 'bio', 'Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks', 'Balaur, Irina|Mazein, Alexander|Saqi, Mansoor|Lysenko, Artem|Rawlings, Christopher J|Auffray, Charles', 'European Institute for Systems Biology and Medicine (EISBM), CIRI CNRS UMR 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.|European Institute for Systems Biology and Medicine (EISBM), CIRI CNRS UMR 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.|European Institute for Systems Biology and Medicine (EISBM), CIRI CNRS UMR 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.|Rothamsted Research, Harpenden, West Common, Hertfordshire AL5 2JQ, UK.|Rothamsted Research, Harpenden, West Common, Hertfordshire AL5 2JQ, UK.|European Institute for Systems Biology and Medicine (EISBM), CIRI CNRS UMR 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.', 'France|France|France|United Kingdom|United Kingdom|France', 10, 'Computer Graphics|Database Management Systems|Databases, Factual|Genome|Humans|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btw731', '27993779', 'The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing.'),
(80397, 2017, 'bio', 'RBPPred: predicting RNA-binding proteins from sequence using SVM', 'Zhang, Xiaoli|Liu, Shiyong', 'School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China.|School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China.', 'China|China', 25, 'Computational Biology|Humans|RNA|RNA-Binding Proteins|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw730', '27993780', 'Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design.'),
(80398, 2017, 'bio', 'Message from the ISCB: The 5th ISCB Wikipedia competition: coming to a classroom near you?', 'Kilpatrick, Alastair', 'University of California, San Diego.', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw671', '27993781', '-'),
(80399, 2017, 'bio', 'ReconMap: an interactive visualization of human metabolism', 'Noronha, Alberto|Daníelsdóttir, Anna Dröfn|Gawron, Piotr|Jóhannsson, Freyr|Jónsdóttir, Soffía|Jarlsson, Sindri|Gunnarsson, Jón Pétur|Brynjólfsson, Sigurður|Schneider, Reinhard|Thiele, Ines|Fleming, Ronan M T', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Iceland|Luxembourg|Iceland|Iceland|Iceland|Iceland|Iceland|Luxembourg|Luxembourg|Luxembou', 28, 'Databases, Factual|Genome, Human|Humans|Metabolic Networks and Pathways|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw667', '27993782', 'A genome-scale reconstruction of human metabolism, Recon 2, is available but no interface exists to interactively visualize its content integrated with omics data and simulation results.'),
(80400, 2017, 'bio', 'Hum-mPLoc 30: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features', 'Zhou, Hang|Yang, Yang|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China.|Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China.', 'China|China|China', 32, 'Algorithms|Cell Compartmentation|Computational Biology|Gene Ontology|Humans|Machine Learning|Models, Biological|Protein Transport|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw723', '27993784', 'Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models.'),
(80401, 2017, 'bio', 'Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications', 'Gao, Zhen|Chen, Yang|Cai, Xiaoshu|Xu, Rong', 'Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States|United States|United States', 16, 'Biological Transport|Blood-Brain Barrier|Databases, Pharmaceutical|Diffusion|Drug Therapy|Drug-Related Side Effects and Adverse Reactions|Humans|Models, Biological|Permeability|Pharmaceutical Preparations|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw713', '27993785', 'Blood-Brain-Barrier (BBB) is a rigorous permeability barrier for maintaining homeostasis of Central Nervous System (CNS). Determination of compounds permeability to BBB is prerequisite in CNS drug discovery. Existing computational methods usually predict drug BBB permeability from chemical structure and they generally apply to small compounds passing BBB through passive diffusion. As abundant information on drug side effects and indications has been recorded over time through extensive clinical usage, we aim to explore BBB permeability prediction from a new angle and introduce a novel approach to predict BBB permeability from drug clinical phenotypes (drug side effects and drug indications). This method can apply to both small compounds and macro-molecules penetrating BBB through various mechanisms besides passive diffusion.'),
(80402, 2017, 'bio', 'DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter', 'Quach, Bryan|Furey, Terrence S', 'Curriculum in Bioinformatics and Computational Biology.|Department of Genetics.', '-|-', 14, 'Area Under Curve|Base Sequence|Binding Sites|DNA Footprinting|Deoxyribonuclease I|Genomics|Humans|K562 Cells|Models, Molecular|Nucleotide Motifs|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw740', '27993786', 'Identifying the locations of transcription factor binding sites is critical for understanding how gene transcription is regulated across different cell types and conditions. Chromatin accessibility experiments such as DNaseI sequencing (DNase-seq) and Assay for Transposase Accessible Chromatin sequencing (ATAC-seq) produce genome-wide data that include distinct footprint patterns at binding sites. Nearly all existing computational methods to detect footprints from these data assume that footprint signals are highly homogeneous across footprint sites. Additionally, a comprehensive and systematic comparison of footprinting methods for specifically identifying which motif sites for a specific factor are bound has not been performed.'),
(80403, 2017, 'bio', 'Improved VCF normalization for accurate VCF comparison', 'Bayat, Arash|Gaëta, Bruno|Ignjatovic, Aleksandar|Parameswaran, Sri', 'Department of Genetics.|Department of Genetics.|Department of Genetics.|Department of Genetics.', '-|-|-|-', 2, 'Algorithms|Alleles|Base Sequence|Genetic Variation|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btw748', '27993787', 'The Variant Call Format (VCF) is widely used to store data about genetic variation. Variant calling workflows detect potential variants in large numbers of short sequence reads generated by DNA sequencing and report them in VCF format. To evaluate the accuracy of variant callers, it is critical to correctly compare their output against a reference VCF file containing a gold standard set of variants. However, comparing VCF files is a complicated task as an individual genomic variant can be represented in several different ways and is therefore not necessarily reported in a unique way by different software.'),
(80404, 2017, 'bio', 'MRUniNovo: an efficient tool for de novo peptide sequencing utilizing the hadoop distributed computing framework', 'Li, Chuang|Chen, Tao|He, Qiang|Zhu, Yunping|Li, Kenli', 'College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China.|School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria, Australia.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.', 'China|China|Australia|China|China', 10, 'Algorithms|Humans|Peptides|Sequence Analysis, Protein|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw721', '27993788', 'Tandem mass spectrometry-based de novo peptide sequencing is a complex and time-consuming process. The current algorithms for de novo peptide sequencing cannot rapidly and thoroughly process large mass spectrometry datasets. In this paper, we propose MRUniNovo, a novel tool for parallel de novo peptide sequencing. MRUniNovo parallelizes UniNovo based on the Hadoop compute platform. Our experimental results demonstrate that MRUniNovo significantly reduces the computation time of de novo peptide sequencing without sacrificing the correctness and accuracy of the results, and thus can process very large datasets that UniNovo cannot.'),
(80405, 2017, 'bio', 'SPADEVizR: an R package for visualization, analysis and integration of SPADE results', 'Gautreau, Guillaume|Pejoski, David|Le Grand, Roger|Cosma, Antonio|Beignon, Anne-Sophie|Tchitchek, Nicolas', 'College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.', 'China|China|China|China|China|China', 15, 'Algorithms|Cluster Analysis|Flow Cytometry|Software', 'Journal Article', '10.1093/bioinformatics/btw708', '27993789', 'Flow, hyperspectral and mass cytometry are experimental techniques measuring cell marker expressions at the single cell level. The recent increase of the number of markers simultaneously measurable has led to the development of new automatic gating algorithms. Especially, the SPADE algorithm has been proposed as a novel way to identify clusters of cells having similar phenotypes in high-dimensional cytometry data. While SPADE or other cell clustering algorithms are powerful approaches, complementary analysis features are needed to better characterize the identified cell clusters.'),
(80406, 2016, 'bio', 'An integer programming framework for inferring disease complexes from network data', 'Mazza, Arnon|Klockmeier, Konrad|Wanker, Erich|Sharan, Roded', 'College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.|College of Information Science and Engineering, Hunan University, National Supercomputing Center in Changsha, Changsha, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw692', '27993841', '-'),
(80407, 2017, 'bio', 'Xenolog classification', 'Darby, Charlotte A|Stolzer, Maureen|Ropp, Patrick J|Barker, Daniel|Durand, Dannie', 'Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Biology, University of St. Andrews, St. Andrews, Fife KY16 9TH, UK.|Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bacteria|Evolution, Molecular|Genes, Bacterial|Genomics|Phylogeny|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btw686', '27998934', 'Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes.'),
(80408, 2017, 'bio', 'CymeR: cytometry analysis using KNIME, docker and R', 'Muchmore, B|Alarcón-Riquelme, M E', 'Centre for Genomics and Oncological Research (GENYO), Area of Genomic Medicine, Genetics of Complex Diseases, Pfizer-University of Granada-Andalusian Regional Government, Health Sciences Technology Park, Granada 18016, Spain.|Centre for Genomics and Oncological Research (GENYO), Area of Genomic Medicine, Genetics of Complex Diseases, Pfizer-University of Granada-Andalusian Regional Government, Health Sciences Technology Park, Granada 18016, Spain.', 'Spain|Spain', 1, 'Algorithms|Computational Biology|Flow Cytometry|Software', 'Journal Article', '10.1093/bioinformatics/btw707', '27998935', 'Here we present open-source software for the analysis of high-dimensional cytometry data using state of the art algorithms. Importantly, use of the software requires no programming ability, and output files can either be interrogated directly in CymeR or they can be used downstream with any other cytometric data analysis platform. Also, because we use Docker to integrate the multitude of components that form the basis of CymeR, we have additionally developed a proof-of-concept of how future open-source bioinformatic programs with graphical user interfaces could be developed.'),
(80409, 2017, 'bio', 'CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort', 'Yu, Jessica S|Pertusi, Dante A|Adeniran, Adebola V|Tyo, Keith E J', 'Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Cell Separation|Flow Cytometry|Software|Support Vector Machine|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw710', '27998936', 'High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required.'),
(80410, 2017, 'bio', 'Improving flux predictions by integrating data from multiple strains', 'Long, Matthew R|Reed, Jennifer L', 'Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States', 0, 'Escherichia coli|Gene Knockout Techniques|Metabolic Networks and Pathways|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw706', '27998937', 'Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient to significantly improve metabolic flux predictions.'),
(80411, 2017, 'bio', 'Agent-based re-engineering of ErbB signaling: a modeling pipeline for integrative systemsxc2xa0biology', 'Das, Arya A|Ajayakumar Darsana, T|Jacob, Elizabeth', 'Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States|United States', 2, 'Computer Simulation|ErbB Receptors|Humans|Models, Biological|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw709', '27998938', 'Experiments in systems biology are generally supported by a computational model which quantitatively estimates the parameters of the system by finding the best fit to the experiment. Mathematical models have proved to be successful in reverse engineering the system. The data generated is interpreted to understand the dynamics of the underlying phenomena. The question we have sought to answer is that - is it possible to use an agent-based approach to re-engineer a biological process, making use of the available knowledge from experimental and modelling efforts? Can the bottom-up approach benefit from the top-down exercise so as to create an integrated modelling formalism for systems biology? We propose a modelling pipeline that learns from the data given by reverse engineering, and uses it for re-engineering the system, to carry out in-silico experiments.'),
(80412, 2017, 'bio', 'A pipeline for local assembly of minisatellite alleles from single-molecule sequencing data', 'Ogeh, Denye|Badge, Richard', 'Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States', 0, 'Alleles|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Minisatellite Repeats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw687', '27998939', 'The advent of Next Generation Sequencing (NGS) has led to the generation of enormous volumes of short read sequence data, cheaply and in reasonable time scales. Nevertheless, the quality of genome assemblies generated using NGS technologies has been greatly affected, compared to those generated using Sanger DNA sequencing. This is largely due to the inability of short read sequence data to scaffold repetitive structures, creating gaps, inversions and rearrangements and resulting in assemblies that are, at best, draft forms. Third generation single-molecule sequencing (SMS) technologies (e.g. Pacific Biosciences Single Molecule Real Time (SMRT) system) address this challenge by generating sequences with increased read lengths, offering the prospect to better recover these complex repetitive structures, concomitantly improving assembly quality.'),
(80413, 2017, 'bio', 'Recycler: an algorithm for detecting plasmids from de novo assembly graphs', 'Rozov, Roye|Brown Kav, Aya|Bogumil, David|Shterzer, Naama|Halperin, Eran|Mizrahi, Itzhak|Shamir, Ron', 'Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.|The Department of Life Sciences & the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|The Department of Life Sciences & the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|The Department of Life Sciences & the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.|The Department of Life Sciences & the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel', 40, 'Algorithms|Bacteria|Escherichia coli|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Metagenome|Plasmids|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw651', '28003256', 'Plasmids and other mobile elements are central contributors to microbial evolution and genome innovation. Recently, they have been found to have important roles in antibiotic resistance and in affecting production of metabolites used in industrial and agricultural applications. However, their characterization through deep sequencing remains challenging, in spite of rapid drops in cost and throughput increases for sequencing. Here, we attempt to ameliorate this situation by introducing a new circular element assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and alignments of paired-end reads to assemble cyclic sequences likely to be plasmids, phages and other circular elements.'),
(80414, 2017, 'bio', 'HYSYS: have you swapped your samples?', 'Schröder, Jan|Corbin, Vincent|Papenfuss, Anthony T', 'Bioinformatics Division, The Walter & Eliza Hall Institute, Parkville 3052, Australia.|Bioinformatics Division, The Walter & Eliza Hall Institute, Parkville 3052, Australia.|Bioinformatics Division, The Walter & Eliza Hall Institute, Parkville 3052, Australia.', 'Australia|Australia|Australia', 2, 'DNA Contamination|Genomics|Humans|Neoplasms|Polymorphism, Single Nucleotide|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw685', '28003257', 'The application of a genomics assay to samples from a cohort is a frequently applied experimental design in cancer genomics studies. The collection and analysis of cancer sequencing data in the clinical setting is an elaborate process that may involve consenting patients, obtaining possibly-multiple DNA samples, sequencing and analysis. Many of these steps are manual. At any stage mistakes can occur that cause a DNA sample to be labelled incorrectly. However, there is a paucity of methods in the literature to identify such swaps specifically in cancer studies.'),
(80415, 2017, 'bio', 'The Ark: a customizable web-based data management tool for health and medical research', 'Bickerstaffe, Adrian|Ranaweera, Thilina|Endersby, Travis|Ellis, Christopher|Maddumarachchi, Sanjaya|Gooden, George E|White, Paul|Moses, Eric K|Hewitt, Alex W|Hopper, John L', 'Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Curtin UWA Centre for Genetic Origins of Health and Disease, Faculty of Health Sciences, Curtin University, Bentley, 6102, Australia.|Curtin UWA Centre for Genetic Origins of Health and Disease, Faculty of Health Sciences, Curtin University, Bentley, 6102, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Genetics and Epidemiology, Lions Eye Institute, Nedlands, 6009, Australia.|Curtin UWA Centre for Genetic Origins of Health and Disease, Faculty of Health Sciences, Curtin University, Bentley, 6102, Australia.|Curtin UWA Centre for Genetic Origins of Health and Disease, Faculty of Health Sciences, Curtin University, Bentley, 6102, Australia.|Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, 3002, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 3, 'Animals|Biomedical Research|Databases, Factual|Female|Humans|Internet|Male|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btw675', '28003258', 'The Ark is an open-source web-based tool that allows researchers to manage health and medical research data for humans and animals without specialized database skills or programming expertise. The system provides data management for core research information including demographic, phenotype, biospecimen and pedigree data, in addition to supporting typical investigator requirements such as tracking participant consent and correspondence, whilst also being able to generate custom data exports and reports. The Ark is study generic by design and highly configurable via its web interface, allowing researchers to tailor the system to the specific data management requirements of their study.'),
(80416, 2017, 'bio', 'MEGA-V: detection of variant gene sets in patient cohorts', 'Gambardella, Gennaro|Cereda, Matteo|Benedetti, Lorena|Ciccarelli, Francesca D', 'Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.|Centre for Epidemiology and Biostatistics, The University of Melbourne, Carlton, 3010, Australia.', 'Australia|Australia|Australia|Australia', 4, 'Cohort Studies|Data Interpretation, Statistical|Humans|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btw809', '28003259', ': Detecting significant associations between genetic variants and disease may prove particularly challenging when the variants are rare in the population and/or act together with other variants to cause the disease. We have developed a statistical framework named Mutation Enrichment Gene set Analysis of Variants (MEGA-V) that specifically detects the enrichments of genetic alterations within a process in a cohort of interest. By focusing on the mutations of several genes contributing to the same function rather than on those affecting a single gene, MEGA-V increases the power to detect statistically significant associations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80417, 2017, 'bio', 'T-BAS: Tree-Based Alignment Selector toolkit for phylogenetic-based placement, alignment downloads and metadata visualization: an example with the Pezizomycotina tree of life', 'Carbone, Ignazio|White, James B|Miadlikowska, Jolanta|Arnold, A Elizabeth|Miller, Mark A|Kauff, Frank|URen, Jana M|May, Georgiana|Lutzoni, François', 'Center for Integrated Fungal Research, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695 USA.|Center for Integrated Fungal Research, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695 USA.|Department of Biology, Duke University, Durham, NC 27708, USA.|School of Plant Sciences, The University of Arizona, Tucson, AZ 85721, USA.|San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.|Department of Medicine, University of Giessen, Giessen, Germany.|School of Plant Sciences, The University of Arizona, Tucson, AZ 85721, USA.|Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA.|Department of Biology, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States|Germany|United States|United States|United States', 22, 'Ascomycota|Endophytes|Genetic Loci|Metadata|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw808', '28003260', 'High-quality phylogenetic placement of sequence data has the potential to greatly accelerate studies of the diversity, systematics, ecology and functional biology of diverse groups. We developed the Tree-Based Alignment Selector (T-BAS) toolkit to allow evolutionary placement and visualization of diverse DNA sequences representing unknown taxa within a robust phylogenetic context, and to permit the downloading of highly curated, single- and multi-locus alignments for specific clades.'),
(80418, 2017, 'bio', 'Innovations and challenges in detecting long read overlaps: an evaluation of the state-of-the-art', 'Chu, Justin|Mohamadi, Hamid|Warren, René L|Yang, Chen|Birol, Inanç', 'University of British Columbia, Vancouver, BC V6T 1Z4, Canada.|University of British Columbia, Vancouver, BC V6T 1Z4, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|University of British Columbia, Vancouver, BC V6T 1Z4, Canada.|University of British Columbia, Vancouver, BC V6T 1Z4, Canada.', 'Canada|Canada|Canada|Canada|Canada', 20, 'Algorithms|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw811', '28003261', 'Identifying overlaps between error-prone long reads, specifically those from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PB), is essential for certain downstream applications, including error correction and de novo assembly. Though akin to the read-to-reference alignment problem, read-to-read overlap detection is a distinct problem that can benefit from specialized algorithms that perform efficiently and robustly on high error rate long reads. Here, we review the current state-of-the-art read-to-read overlap tools for error-prone long reads, including BLASR, DALIGNER, MHAP, GraphMap and Minimap. These specialized bioinformatics tools differ not just in their algorithmic designs and methodology, but also in their robustness of performance on a variety of datasets, time and memory efficiency and scalability. We highlight the algorithmic features of these tools, as well as their potential issues and biases when utilizing any particular method. To supplement our review of the algorithms, we benchmarked these tools, tracking their resource needs and computational performance, and assessed the specificity and precision of each. In the versions of the tools tested, we observed that Minimap is the most computationally efficient, specific and sensitive method on the ONT datasets tested, whereas GraphMap and DALIGNER are the most specific and sensitive methods on the tested PB datasets. The concepts surveyed may apply to future sequencing technologies, as scalability is becoming more relevant with increased sequencing throughput.'),
(80419, 2017, 'bio', 'Defining the clonality of peripheral T cell lymphomas using RNA-seq', 'Brown, Scott D|Hapgood, Greg|Steidl, Christian|Weng, Andrew P|Savage, Kerry J|Holt, Robert A', 'Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada.|Centre for Lymphoid Cancer, Department of Medical Oncology, British Columbia Cancer Agency, Vancouver, Canada.|Centre for Lymphoid Cancer, Department of Lymphoid Cancer Research, British Columbia Cancer Agency, Vancouver, Canada.|Terry Fox Laboratory and Department of Pathology, British Columbia Cancer Agency, Vancouver, Canada.|Centre for Lymphoid Cancer, Department of Medical Oncology, British Columbia Cancer Agency, Vancouver, Canada.|Canadas Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 8, 'Base Sequence|Cell Line, Tumor|Clone Cells|Flow Cytometry|Humans|Immunophenotyping|Lymphoma, T-Cell, Peripheral|Receptors, Antigen, T-Cell, alpha-beta|Sequence Analysis, RNA|T-Lymphocytes', 'Journal Article', '10.1093/bioinformatics/btw810', '28003262', 'In T-cell lymphoma, malignant T cells arising from a founding clone share an identical T cell receptor (TCR) and can be identified by the over-representation of this TCR relative to TCRs from the patients repertoire of normal T cells. Here, we demonstrate that TCR information extracted from RNA-seq data can provide a higher resolution view of peripheral T cell lymphomas (PTCLs) than that provided by conventional methods.'),
(80420, 2017, 'bio', 'bnstruct: an R package for Bayesian Network structure learning in the presence of missing data', 'Franzin, Alberto|Sambo, Francesco|Di Camillo, Barbara', 'IRIDIA-CoDE, Université Libre de Bruxelles, 1050 Brussels, Belgium.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.', 'Belgium|Italy|Italy', 15, 'Bayes Theorem|Humans|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btw807', '28003263', 'A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice.'),
(80421, 2017, 'bio', 'A general framework for association analysis of microbial communities on a taxonomic tree', 'Tang, Zheng-Zheng|Chen, Guanhua|Alekseyenko, Alexander V|Li, Hongzhe', 'Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.|Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.|Department of Public Health Sciences.|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States', 18, 'Bacteria|Classification|Computer Simulation|Genomics|Humans|Microbiota|Models, Genetic|Virulence', 'Journal Article', '10.1093/bioinformatics/btw804', '28003264', ': Association analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities.'),
(80422, 2017, 'bio', 'GWAlpha: genome-wide estimation of additive effects (alpha) based on trait quantile distribution from pool-sequencing experiments', 'Fournier-Level, Alexandre|Robin, Charles|Balding, David J', 'School of BioSciences and Centre for Systems Genomics.|School of BioSciences and Centre for Systems Genomics.|School of BioSciences and Centre for Systems Genomics.', '-|-|-', 1, 'Genome|Genome-Wide Association Study|Phenotype|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btw805', '28003266', 'Sequencing pools of individuals (Pool-Seq) is a cost-effective way to gain insight into the genetics of complex traits, but as yet no parametric method has been developed to both test for genetic effects and estimate their magnitude. Here, we propose GWAlpha, a flexible method to obtain parametric estimates of genetic effects genome-wide from Pool-Seq experiments.'),
(80423, 2017, 'bio', 'RADER: a RApid DEcoy Retriever to facilitate decoy based assessment of virtual screening', 'Wang, Ling|Pang, Xiaoqian|Li, Yecheng|Zhang, Ziying|Tan, Wen', 'Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Institute of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.', 'China|China|China|China|China', 7, 'Databases, Chemical|Ligands|Molecular Docking Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btw783', '28011765', 'Evaluation of the capacity for separating actives from challenging decoys is a crucial metric of performance related to molecular docking or a virtual screening workflow. The Directory of Useful Decoys (DUD) and its enhanced version (DUD-E) provide a benchmark for molecular docking, although they only contain a limited set of decoys for limited targets. DecoyFinder was released to compensate the limitations of DUD or DUD-E for building target-specific decoy sets. However, desirable query template design, generation of multiple decoy sets of similar quality, and computational speed remain bottlenecks, particularly when the numbers of queried actives and retrieved decoys increases to hundreds or more. Here, we developed a program suite called RApid DEcoy Retriever (RADER) to facilitate the decoy-based assessment of virtual screening. This program adopts a novel database-management regime that supports rapid and large-scale retrieval of decoys, enables high portability of databases, and provides multifaceted options for designing initial query templates from a large number of active ligands and generating subtle decoy sets. RADER provides two operational modes: as a command-line tool and on a web server. Validation of the performance and efficiency of RADER was also conducted and is described.'),
(80424, 2017, 'bio', 'Structural insight to mutation effects uncover a common allosteric site in class C GPCRs', 'Harpsøe, Kasper|Boesgaard, Michael W|Munk, Christian|Bräuner-Osborne, Hans|Gloriam, David E', 'Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.', 'China|China|China|China|China', 7, 'Allosteric Regulation|Allosteric Site|Binding Sites|Humans|Ligands|Mutagenesis, Site-Directed|Mutation|Phylogeny|Protein Domains|Protein Multimerization|Protein Structure, Secondary|Receptor, Metabotropic Glutamate 5|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/btw784', '28011766', 'Class C G protein-coupled receptors (GPCRs) regulate important physiological functions and allosteric modulators binding to the transmembrane domain constitute an attractive and, due to a lack of structural insight, a virtually unexplored potential for therapeutics and the food industry. Combining pharmacological site-directed mutagenesis data with the recent class C GPCR experimental structures will provide a foundation for rational design of new therapeutics.'),
(80425, 2017, 'bio', 'Analyzing user-generated online content for drug discovery: development and use of MedCrawler', 'Helfenstein, Andreas|Tammela, Päivi', 'Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.|Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, 382 East Circle at Higher Education Mega Center, Guangzhou 510006, China.', 'China|China', 2, 'Data Mining|Drug Discovery|Ethnopharmacology|Software', 'Journal Article', '10.1093/bioinformatics/btw782', '28011767', 'Ethnopharmacology, or the scientific validation of traditional medicine, is a respected starting point in drug discovery. Home remedies and traditional use of plants are still widespread, also in Western societies. Instead of perusing ancient pharmacopeias, we developed MedCrawler, which we used to analyze blog posts for mentions of home remedies and their applications. This method is free and accessible from the office computer.'),
(80426, 2017, 'bio', 'SeqLib: a Cxe2x80x89++ API for rapid BAM manipulation, sequence alignment and sequence assembly', 'Wala, Jeremiah|Beroukhim, Rameen', 'The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.|The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.', 'United States|United States', 1, 'Chromosomes|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw741', '28011768', 'We present SeqLib, a Cxe2x80x89++xe2x80x89API and command line tool that provides a rapid and user-friendly interface to BAM/SAM/CRAM files, global sequence alignment operations and sequence assembly. Four C libraries perform core operations in SeqLib: HTSlib for BAM access, BWA-MEM and BLAT for sequence alignment and Fermi for error correction and sequence assembly. Benchmarking indicates that SeqLib has lower CPU and memory requirements than leading Cxe2x80x89++xe2x80x89sequence analysis APIs. We demonstrate an example of how minimal SeqLib code can extract, error-correct and assemble reads from a CRAM file and then align with BWA-MEM. SeqLib also provides additional capabilities, including chromosome-aware interval queries and read plotting. Command line tools are available for performing integrated error correction, micro-assemblies and alignment.'),
(80427, 2017, 'bio', 'The PPI3D web server for searching, analyzing and modeling protein-protein interactions in the context of 3D structures', 'Dapkunas, Justas|Timinskas, Albertas|Olechnovic, Kliment|Margelevicius, Mindaugas|Diciunas, Rytis|Venclovas, Ceslovas', 'Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.|Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.|Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.|Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.|Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.|Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.', 'Lithuania|Lithuania|Lithuania|Lithuania|Lithuania|Lithuania', 15, 'Internet|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btw756', '28011769', 'The PPI3D web server is focused on searching and analyzing the structural data on protein-protein interactions. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions.'),
(80428, 2017, 'bio', 'Simulation-based estimation of branching models for LTR retrotransposons', 'Moulin, Serge|Seux, Nicolas|Chrétien, Stéphane|Guyeux, Christophe|Lerat, Emmanuelle', 'Département dInformatique des Systèmes Complexes, FEMTO-ST Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, France.|Laboratoire de Mathématiques de Besançon, UMR 6623 CNRS, Université de Bourgogne Franche-Comté, Besançon, France.|National Physical Laboratory, Teddington, UK.|Département dInformatique des Systèmes Complexes, FEMTO-ST Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, France.|Université de Lyon, Université Claude Bernard Lyon 1, CNRS, UMR 5558, Laboratoire Biométrie et Biologie Evolutive, Villeurbanne, France.', 'France|France|United Kingdom|France|France', 1, 'Animals|Chromosomes|Computer Simulation|Drosophila melanogaster|Genome|Models, Genetic|Programming Languages|Retroelements|Software', 'Journal Article', '10.1093/bioinformatics/btw622', '28011770', 'LTR retrotransposons are mobile elements that are able, like retroviruses, to copy and move inside eukaryotic genomes. In the present work, we propose a branching model for studying the propagation of LTR retrotransposons in these genomes. This model allows us to take into account both the positions and the degradation level of LTR retrotransposons copies. In our model, the duplication rate is also allowed to vary with the degradation level.'),
(80429, 2017, 'bio', 'Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks', 'Hanson, Jack|Yang, Yuedong|Paliwal, Kuldip|Zhou, Yaoqi', 'Signal Processing Laboratory, Griffith University, Brisbane 4122, Australia.|Institute for Glycomics, Griffith University, Gold Coast 4215, Australia.|Signal Processing Laboratory, Griffith University, Brisbane 4122, Australia.|Institute for Glycomics, Griffith University, Gold Coast 4215, Australia.', 'Australia|Australia|Australia|Australia', 98, 'Algorithms|Caspases|Computational Biology|Genetic Diseases, Inborn|Machine Learning|Memory, Short-Term|Neural Networks (Computer)|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btw678', '28011771', 'Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction.'),
(80430, 2017, 'bio', 'Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies', 'Jiang, Wei|Yu, Weichuan', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.', 'China|China', 5, 'Computer Simulation|Genetic Diseases, Inborn|Genetic Variation|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btw690', '28011772', 'In genome-wide association studies (GWASs) of common diseases/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is difficult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze datasets from multiple GWASs.'),
(80431, 2017, 'bio', 'ALTRE: workflow for defining ALTered Regulatory Elements using chromatin accessibility data', 'Baskin, Elizabeth|Farouni, Rick|Mathé, Ewy A', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.', 'China|China|China', 2, 'Chromatin|Chromatin Immunoprecipitation|Computational Biology|Humans|Regulatory Sequences, Nucleic Acid|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btw688', '28011773', 'Regulatory elements regulate gene transcription, and their location and accessibility is cell-type specific, particularly for enhancers. Mapping and comparing chromatin accessibility between different cell types may identify mechanisms involved in cellular development and disease progression. To streamline and simplify differential analysis of regulatory elements genome-wide using chromatin accessibility data, such as DNase-seq, ATAC-seq, we developed ALTRE ( ALT ered R egulatory E lements), an R package and associated R Shiny web app. ALTRE makes such analysis accessible to a wide range of users-from novice to practiced computational biologists.'),
(80432, 2017, 'bio', 'Fast motif matching revisited: high-order PWMs, SNPs and indels', 'Korhonen, Janne H|Palin, Kimmo|Taipale, Jussi|Ukkonen, Esko', 'School of Computer Science, Reykjavík University, Reykjavík, Iceland.|Genome-Scale Biology Research Program, Research Programs Unit.|Department of Biosciences and Nutrition, Karolinska Institutet, Genome Scale Biology Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Helsinki, Finland.', 'Iceland|-|Finland|Finland', 9, 'Algorithms|Chromosomes, Human, Pair 22|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Position-Specific Scoring Matrices|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw683', '28011774', 'While the position weight matrix (PWM) is the most popular model for sequence motifs, there is growing evidence of the usefulness of more advanced models such as first-order Markov representations, and such models are also becoming available in well-known motif databases. There has been lots of research of how to learn these models from training data but the problem of predicting putative sites of the learned motifs by matching the model against new sequences has been given less attention. Moreover, motif site analysis is often concerned about how different variants in the sequence affect the sites. So far, though, the corresponding efficient software tools for motif matching have been lacking.'),
(80433, 2017, 'bio', 'MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways', 'Rodriguez-Martinez, Andrea|Ayala, Rafael|Posma, Joram M|Neves, Ana L|Gauguier, Dominique|Nicholson, Jeremy K|Dumas, Marc-Emmanuel', 'Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Adipose Tissue|Animals|Computational Biology|Metabolic Networks and Pathways|Quantitative Trait Loci|Rats|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btw697', '28011775', 'MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.'),
(80434, 2017, 'bio', 'SarConfoCal: simultaneous sarcomere length and cytoplasmic calcium measurements for laser scanning confocal microscopy images', 'Pasqualin, Côme|Gannier, François|Yu, Angèle|Malécot, Claire O|Bredeloux, Pierre|Maupoil, Véronique', 'Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Animals|Calcium|Humans|Microscopy, Confocal|Muscle Cells|Sarcomeres|Software', 'Journal Article', '10.1093/bioinformatics/btw754', '28011776', 'Simultaneous recordings of myocytes contractility and their cytoplasmic calcium concentration allow powerful studies, particularly on heart failure and other cardiac dysfunctions. Such studies require dedicated and expensive experimental devices that are difficult to use. Thus we propose SarConfoCal, the first and only software to simultaneously analyse both cytoplasmic calcium variations (from fluorescence signal) and myocytes contractility (from sarcomere length measurement) on laser scanning confocal microscopy images. SarConfoCal is easy to set up and use, especially by people without programming skills.'),
(80435, 2017, 'bio', 'Expanding the toolkit for membrane protein modeling in Rosetta', 'Koehler Leman, Julia|Mueller, Benjamin K|Gray, Jeffrey J', 'Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemistry, Vanderbilt University, Nashville, TN 37221, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States', 9, 'Membrane Proteins|Models, Molecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btw716', '28011777', 'A range of membrane protein modeling tools has been developed in the past 5-10 years, yet few of these tools are integrated and make use of existing functionality for soluble proteins. To extend existing methods in the Rosetta biomolecular modeling suite for membrane proteins, we recently implemented RosettaMP, a general framework for membrane protein modeling. While RosettaMP facilitates implementation of new methods, addressing real-world biological problems also requires a set of accessory tools that are used to carry out standard modeling tasks.'),
(80436, 2017, 'bio', 'Mutual enrichment in aggregated ranked lists with applications to gene expression regulation', 'Cohn-Alperovich, Dalia|Rabner, Alona|Kifer, Ilona|Mandel-Gutfreund, Yael|Yakhini, Zohar', 'Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw727', '28011778', '-'),
(80437, 2017, 'bio', 'cerebroViz: an R package for anatomical visualization of spatiotemporal brain data', 'Bahl, Ethan|Koomar, Tanner|Michaelson, Jacob J', 'Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States', 7, 'Brain|Databases, Factual|Gene Expression|Gene Expression Profiling|Humans|Software|Spatio-Temporal Analysis', 'Journal Article', '10.1093/bioinformatics/btw726', '28011779', 'Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a diseases age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics.'),
(80438, 2017, 'bio', 'FUEL-mLoc: feature-unified prediction and explanation of multi-localization of cellular proteins in multiple organisms', 'Wan, Shibiao|Mak, Man-Wai|Kung, Sun-Yuan', 'Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China.|Department of Electrical Engineering, Princeton University, NJ, USA.', 'United States|China|United States', 0, 'Bacteria|Cell Compartmentation|Computational Biology|Eukaryota|Humans|Protein Transport|Proteins|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btw717', '28011780', 'Although many web-servers for predicting protein subcellular localization have been developed, they often have the following drawbacks: (i) lack of interpretability or interpreting results with heterogenous information which may confuse users, (ii) ignoring multi-location proteins and (iii) only focusing on specific organism. To tackle these problems, we present an interpretable and efficient web-server, namely FUEL-mLoc, using eature- nified prediction and xplanation of m ulti- oc alization of cellular proteins in multiple organisms. Compared to conventional localization predictors, FUEL-mLoc has the following advantages: (i) using unified features (i.e. essential GO terms) to interpret why a prediction is made, (ii) being capable of predicting both single- and multi-location proteins and (iii) being able to handle proteins of multiple organisms, including Eukaryota, Homo sapiens, Viridiplantae, Gram-positive Bacteria, Gram-negative Bacteria and Virus . Experimental results demonstrate that FUEL-mLoc outperforms state-of-the-art subcellular-localization predictors.'),
(80439, 2017, 'bio', 'cBinderDB: a covalent binding agent database', 'Du, Jiewen|Yan, Xin|Liu, Zhihong|Cui, Lu|Ding, Peng|Tan, Xiaoqing|Li, Xiuming|Zhou, Huihao|Gu, Qiong|Xu, Jun', 'Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.|Department of Electrical Engineering, Princeton University, NJ, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Mining|Databases, Pharmaceutical|Drug Discovery|Ligands|Proteins', 'Journal Article', '10.1093/bioinformatics/btw801', '28011781', 'Small molecule drug candidates with attractive toxicity profiles that modulate target proteins through non-covalent interactions are usually favored by scientists and pharmaceutical industry. In the past decades, many non-covalent binding agents have been developed for different diseases. However, an increasing attention has been paid to covalent binding agents in pharmaceutical fields during recent years. Many covalent binding agents entered clinical trials and exerted significant advantages for diseases such as infection, cancers, gastrointestinal disorders, central nervous system or cardiovascular diseases. It has been recognized that covalent binding ligands can be attractive drug candidates. But, there is lack of resource to support covalent ligand discovery.'),
(80440, 2017, 'bio', 'Inference of cellular level signaling networks using single-cell gene expression data in Caenorhabditis elegans reveals mechanisms of cell fate specification', 'Huang, Xiao-Tai|Zhu, Yuan|Chan, Lai Hang Leanne|Zhao, Zhongying|Yan, Hong', 'School of Computer Science and Technology, Xidian University, Xian, Shaanxi, China.|School of Automation, China University of Geosciences, Wuhan, China.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.|Department of Biology, Hong Kong Baptist University, Hong Kong, China.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.', 'China|China|China|China|China', 1, 'Algorithms|Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Cell Differentiation|DNA-Binding Proteins|Embryonic Development|Gene Expression Profiling|Gene Expression Regulation, Developmental|RNA Interference|Signal Transduction|Single-Cell Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw796', '28011782', 'Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout Caenorhabditis elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels.'),
(80441, 2017, 'bio', 'A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm', 'Lafond-Lapalme, Joël|Duceppe, Marc-Olivier|Wang, Shengrui|Moffett, Peter|Mimee, Benjamin', 'Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada.|Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada.|Département dinformatique, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.|Département de biologie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.|Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada.', 'Canada|Canada|Canada|Canada|Canada', 10, 'Algorithms|Animals|Cluster Analysis|Gene Expression Profiling|Sequence Analysis, RNA|Software|Transcriptome|Tylenchida|Weevils', 'Journal Article', '10.1093/bioinformatics/btw793', '28011783', 'The identification of contaminating sequences in a de novo assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination.'),
(80442, 2017, 'bio', 'VarMatch: robust matching of small variant datasets using flexible scoring schemes', 'Sun, Chen|Medvedev, Paul', 'Department of Computer Science and Engineering, The Pennsylvania State University, State College, PA, USA.|Department of Computer Science and Engineering, The Pennsylvania State University, State College, PA, USA.', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Data Accuracy|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw797', '28011786', 'Small variant calling is an important component of many analyses, and, in many instances, it is important to determine the set of variants which appear in multiple callsets. Variant matching is complicated by variants that have multiple equivalent representations. Normalization and decomposition algorithms have been proposed, but are not robust to different representation of complex variants. Variant matching is also usually done to maximize the number of matches, as opposed to other optimization criteria.'),
(80443, 2017, 'bio', 'switchde: inference of switch-like differential expression along single-cell trajectories', 'Campbell, Kieran R|Yau, Christopher', 'Department of Physiology, Anatomy and Genetics.|Wellcome Trust Centre for Human Genetics.', '-|-', 0, 'Gene Expression Profiling|Models, Genetic|Models, Statistical|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw798', '28011787', 'Pseudotime analyses of single-cell RNA-seq data have become increasingly common. Typically, a latent trajectory corresponding to a biological process of interest-such as differentiation or cell cycle-is discovered. However, relatively little attention has been paid to modelling the differential expression of genes along such trajectories.'),
(80444, 2017, 'bio', 'myGenomeBrowser: building and sharing your own genome browser', 'Carrere, Sébastien|Gouzy, Jérôme', 'Wellcome Trust Centre for Human Genetics.|Wellcome Trust Centre for Human Genetics.', '-|-', 5, 'Data Mining|Genome|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btw800', '28011789', 'myGenomeBrowser is a web-based environment that provides biologists with a way to build, query and share their genome browsers. This tool, that builds on JBrowse, is designed to give users more autonomy while simplifying and minimizing intervention from system administrators. We have extended genome browser basic features to allow users to query, analyze and share their data.'),
(80445, 2017, 'bio', 'High-throughput interpretation of gene structure changes in human and nonhuman resequencing data, using ACE', 'Majoros, William H|Campbell, Michael S|Holt, Carson|DeNardo, Erin K|Ware, Doreen|Allen, Andrew S|Yandell, Mark|Reddy, Timothy E', 'Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.|Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA.|Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Animals|Eukaryota|Exons|Genomics|Haplotypes|Humans|Mutation|Polymorphism, Genetic|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw799', '28011790', 'The accurate interpretation of genetic variants is critical for characterizing genotype-phenotype associations. Because the effects of genetic variants can depend strongly on their local genomic context, accurate genome annotations are essential. Furthermore, as some variants have the potential to disrupt or alter gene structure, variant interpretation efforts stand to gain from the use of individualized annotations that account for differences in gene structure between individuals or strains.'),
(80446, 2017, 'bio', 'A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases', 'Chen, Xing|Huang, Yu-An|You, Zhu-Hong|Yan, Gui-Ying|Wang, Xue-Song', 'School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Chinese Academy of Science, Xinjiang Technical Institute of Physics and Chemistry, Ürümqi 830011, China.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.', 'China|Hong Kong|China|China|China', 0, 'Algorithms|Bacteria|Bacterial Physiological Phenomena|Computational Biology|Disease|Host-Pathogen Interactions|Humans|Microbiota|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btw715', '28025197', 'Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now.'),
(80447, 2017, 'bio', 'DACE: a scalable DP-means algorithm for clustering extremely large sequence data', 'Jiang, Linhao|Dong, Yichao|Chen, Ning|Chen, Ting', 'Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.|Program in Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA, USA.|Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.|Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China.', 'China|United States|China|China', 6, 'Algorithms|Cluster Analysis|Eukaryota|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|RNA, Ribosomal, 16S|RNA, Ribosomal, 18S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw722', '28025198', 'Advancements in next-generation sequencing technology have produced large amounts of reads at low cost in a short time. In metagenomics, 16S and 18S rRNA gene have been widely used as marker genes to profile diversity of microorganisms in environmental samples. Through clustering of sequencing reads we can determine both number of OTUs and their relative abundance. In many applications, clustering of very large sequencing data with high efficiency and accuracy is essential for downstream analysis.'),
(80448, 2017, 'bio', 'Computational prediction of species-specific malonylation sites via enhanced characteristic strategy', 'Wang, Li-Na|Shi, Shao-Ping|Xu, Hao-Dong|Wen, Ping-Ping|Qiu, Jian-Ding', 'Department of Chemistry, Nanchang University, Nanchang, China.|Department of Mathematics, Nanchang University, Nanchang, China.|Department of Chemistry, Nanchang University, Nanchang, China.|Department of Chemistry, Nanchang University, Nanchang, China.|Department of Chemistry, Nanchang University, Nanchang, China.', 'China|China|China|China|China', 32, 'Animals|Escherichia coli|Humans|Lysine|Malonates|Mice|Protein Processing, Post-Translational|Proteomics|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btw755', '28025199', 'Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation.'),
(80449, 2017, 'bio', 'Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud', 'Yang, Andrian|Troup, Michael|Lin, Peijie|Ho, Joshua W K', 'Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.|Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.|Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.|Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Animals|Computational Biology|Dendritic Cells|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Mice|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw732', '28025200', 'Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a xe2x88xbc65% reduction in cost of analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80450, 2017, 'bio', 'tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles', 'He, Sijin|Yong, May|Matthews, Paul M|Guo, Yike', 'European Bioinformatics Institute, Cambridge, UK.|Data Science Institute, Imperial College London, London, UK.|Division of Brain Sciences, Imperial College London, London, UK.|Data Science Institute, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Computational Biology|Image Processing, Computer-Assisted|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btw714', '28025201', 'TranSMART has a wide range of functionalities for translational research and a large user community, but it does not support imaging data. In this context, imaging data typically includes 2D or 3D sets of magnitude data and metadata information. Imaging data may summarise complex feature descriptions in a less biased fashion than user defined plain texts and numeric numbers. Imaging data also is contextualised by other data sets and may be analysed jointly with other data that can explain features or their variation.'),
(80451, 2017, 'bio', 'Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions', 'Zakrzewski, Martha|Proietti, Carla|Ellis, Jonathan J|Hasan, Shihab|Brion, Marie-Jo|Berger, Bernard|Krause, Lutz', 'QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.|QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.|The University of Queensland Diamantina Institute, Brisbane, QLD 4102, Australia.|QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.|The University of Queensland Diamantina Institute, Brisbane, QLD 4102, Australia.|Nestle Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland.|QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.', 'Australia|Australia|Australia|Australia|Australia|Switzerland|Australia', 0, 'Data Mining|Environment|Humans|Internet|Metagenome|Microbiota|Plants|RNA, Ribosomal, 16S|Software|Statistics as Topic|Supervised Machine Learning|Symbiosis', 'Journal Article', '10.1093/bioinformatics/btw725', '28025202', 'Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page.'),
(80452, 2017, 'bio', 'Accelerating Bayesian inference for evolutionary biology models', 'Meyer, Xavier|Chopard, Bastien|Salamin, Nicolas', 'Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 2, 'Algorithms|Bayes Theorem|Biological Evolution|Fossils|Models, Biological|Phylogeny|Plants|Software', 'Journal Article', '10.1093/bioinformatics/btw712', '28025203', 'Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods.'),
(80453, 2017, 'bio', 'Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies', 'Bhaskar, Anand|Javanmard, Adel|Courtade, Thomas A|Tse, David', 'Department of Genetics, Stanford University, Stanford, CA, USA.|Marshall School of Business, University of Southern California, Los Angeles, CA, USA.|Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.|Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Evolution, Molecular|Gene Frequency|Genome-Wide Association Study|Humans|Models, Genetic|Models, Statistical|Phylogeography|Polymorphism, Single Nucleotide|Population Groups|Principal Component Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw720', '28025204', 'Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait.'),
(80454, 2017, 'bio', 'dAPE: a web server to detect homorepeats and follow their evolution', 'Mier, Pablo|Andrade-Navarro, Miguel A', 'Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.|Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.', 'United States|United States', 0, 'Animals|Evolution, Molecular|Humans|Internet|Repetitive Sequences, Amino Acid|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw790', '28031183', 'Homorepeats are low complexity regions consisting of repetitions of a single amino acid residue. There is no current consensus on the minimum number of residues needed to define a functional homorepeat, nor even if mismatches are allowed. Here we present dAPE, a web server that helps following the evolution of homorepeats based on orthology information, using a sensitive but tunable cutoff to help in the identification of emerging homorepeats.'),
(80455, 2017, 'bio', 'SVScore: an impact prediction tool for structural variation', 'Ganel, Liron|Abel, Haley J|FinMetSeq Consortium|Hall, Ira M', 'McDonnell Genome Institute.|McDonnell Genome Institute.|McDonnell Genome Institute.|McDonnell Genome Institute.', '-|-|-|-', 9, 'Gene Frequency|Genomic Structural Variation|Genomics|Humans|Polymorphism, Single Nucleotide|Sequence Deletion|Software', 'Evaluation Studies', '10.1093/bioinformatics/btw789', '28031184', 'Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population.'),
(80456, 2017, 'bio', 'MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer', 'Zhu, Li|Ding, Ying|Chen, Cho-Yi|Wang, Lin|Huo, Zhiguang|Kim, SungHwan|Sotiriou, Christos|Oesterreich, Steffi|Tseng, George C', 'Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Breast Cancer Translational Research Laboratory, J. C. Heuson, Institut Jules Bordet, University Libre de Bruxelles, Brussels 1000, Belgium.|Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.', 'United States|United States|United States|United States|United States|United States|Belgium|United States|United States', 9, 'Breast Neoplasms|Computational Biology|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Humans|Receptors, Estrogen|Sample Size', 'Journal Article', '10.1093/bioinformatics/btw788', '28031185', 'Gene co-expression network analysis from transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms. Differential co-expression analysis helps further detect alterations of regulatory activities in case/control comparison. Co-expression networks estimated from single transcriptomic study is often unstable and not generalizable due to cohort bias and limited sample size. With the rapid accumulation of publicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic studies can provide more accurate and robust results.'),
(80457, 2017, 'bio', 'ACTG: novel peptide mapping onto gene models', 'Choi, Seunghyuk|Kim, Hyunwoo|Paek, Eunok', 'Department of Computer Science, Hanyang University, Seongdong-gu, Seoul, Korea.|Scientific Data Technology Lab, Korea Institute of Science and Technology Information, Yuseong-gu, Daejeon, 34141, Korea.|Department of Computer Science, Hanyang University, Seongdong-gu, Seoul, Korea.', '-|-|-', 3, 'Alternative Splicing|Exons|Genomics|Humans|Models, Genetic|Peptide Mapping|Peptides|RNA Splice Sites|Software', 'Journal Article', '10.1093/bioinformatics/btw787', '28031186', 'In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input.'),
(80458, 2017, 'bio', 'PhosD: inferring kinase-substrate interactions based on protein domains', 'Qin, Gui-Min|Li, Rui-Yi|Zhao, Xing-Ming', 'School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.|School of Computer Science and Technology, Xidian University, Xian 710071, China.|School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.', 'China|China|China', 0, 'Models, Statistical|Phosphorylation|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Kinases|Signal Transduction|Software', 'Evaluation Studies', '10.1093/bioinformatics/btw792', '28031187', 'Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.'),
(80459, 2017, 'bio', 'The discordant method: a novel approach for differential correlation', 'Siska, Charlotte|Bowler, Russell|Kechris, Katerina', 'School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.|School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.|School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw693', '28034858', '-'),
(80460, 2017, 'bio', 'Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi', 'Fortin, Jean-Philippe|Triche, Timothy J|Hansen, Kasper D', 'Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.|Jane Anne Nohl Division of Hematology, Keck School of Medicine of USC, Los Angeles, CA 90033, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.', 'United States|United States|United States', 124, 'DNA Methylation|Epigenomics|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw691', '28035024', 'The minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC (EPIC) array from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 (450k) and EPIC platforms. We introduce the single-sample Noob ( ssNoob ) method, a normalization procedure suitable for incremental preprocessing of individual methylation arrays and conclude that this method should be used when integrating data from multiple generations of Infinium methylation arrays. We show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data.'),
(80461, 2017, 'bio', 'Statistical significance based on length and position of the local score in a model of iid sequences', 'Lagnoux, Agnès|Mercier, Sabine|Vallois, Pierre', 'Institut de Mathématiques de Toulouse, UMR5219, Université de Toulouse 2 Jean Jaurès, 5 allées Antonio Machado, Toulouse, Cedex 09 31058, France.|Institut de Mathématiques de Toulouse, UMR5219, Université de Toulouse 2 Jean Jaurès, 5 allées Antonio Machado, Toulouse, Cedex 09 31058, France.|Institut Elie Cartan, UMR7502 CNRS, INRIA-BIGS, Université de Lorraine, Vandoeuvre-lès-Nancy Cedex 54506, France.', 'France|France|France', 4, 'Models, Statistical|Sequence Analysis, Protein|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btw699', '28035025', 'The local score of a biological sequence analysis is a mathematical tool largely used to analyse biological sequences. Consequently, determining an accurate estimation of its distribution is crucial.'),
(80462, 2017, 'bio', 'InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites', 'Eggeling, Ralf|Grosse, Ivo|Grau, Jan', 'Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.', 'Finland|Germany|Germany', 0, 'Animals|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|Humans|Machine Learning|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btw689', '28035026', 'Recent studies have shown that the traditional position weight matrix model is often insufficient for modeling transcription factor binding sites, as intra-motif dependencies play a significant role for an accurate description of binding motifs. Here, we present the Java application InMoDe, a collection of tools for learning, leveraging and visualizing such dependencies of putative higher order. The distinguishing feature of InMoDe is a robust model selection from a class of parsimonious models, taking into account dependencies only if justified by the data while choosing for simplicity otherwise.'),
(80463, 2017, 'bio', 'QAcon: single model quality assessment using protein structural and contact information with machine learning techniques', 'Cao, Renzhi|Adhikari, Badri|Bhattacharya, Debswapna|Sun, Miao|Hou, Jie|Cheng, Jianlin', 'Department of Computer Science, Pacific Lutheran University, WA 98447, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260-0083, USA.|Department of Electrical and Computer Engineering.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States|United States|United States|United States', 53, 'Animals|Humans|Machine Learning|Models, Molecular|Protein Conformation|Proteins|Quality Control', 'Journal Article', '10.1093/bioinformatics/btw694', '28035027', 'Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool.'),
(80464, 2017, 'bio', 'seXY: a tool for sex inference from genotype arrays', 'Qian, David C|Busam, Jonathan A|Xiao, Xiangjun|OMara, Tracy A|Eeles, Rosalind A|Schumacher, Frederick R|Phelan, Catherine M|Amos, Christopher I', 'Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH 03756, USA.|Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA.|Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH 03756, USA.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.|Division of Genetics and Epidemiology, Institute of Cancer Research, London SW7 3RP, UK.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA.|Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH 03756, USA.', 'Lebanon|United Kingdom|Lebanon|Australia|United Kingdom|United Kingdom|United Kingdom|Lebanon', 2, 'Chromosomes, Human|Female|Genome-Wide Association Study|Humans|Male|Quality Control|Sex Chromosomes|Sex Determination Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw696', '28035028', 'Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data.'),
(80465, 2017, 'bio', 'geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation', 'Wuster, Arthur|Chang, Diana|Behrens, Timothy W|Bhangale, Tushar R', 'Department of Human Genetics.|Department of Human Genetics.|Department of Human Genetics.|Department of Human Genetics.', '-|-|-|-', 0, 'Genetic Association Studies|Genome, Human|Humans|Phenotype|Polymorphism, Genetic|Promoter Regions, Genetic|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btw698', '28035029', 'We have developed geneAttribution, an R package that assigns candidate causal gene(s) to a risk variant identified by a genetic association study such as a GWAS. The method combines user-supplied functional annotation such as expression quantitative trait loci (eQTL) or Hi-C genome conformation data and reports the most likely candidate genes. In the absence of annotation data, geneAttribution relies on the distances between the genes and the input variant.'),
(80466, 2017, 'bio', 'CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing', 'Piao, Yongjun|Lee, Seong Keon|Lee, Eun-Joon|Robertson, Keith D|Shi, Huidong|Ryu, Keun Ho|Choi, Jeong-Hyeon', 'Cancer Center, Georgia Regents University, Augusta, GA, USA.|Department of Statistics, Sungshin Womens University, Seoul, Republic of Korea.|Cancer Center, Georgia Regents University, Augusta, GA, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.|Cancer Center, Georgia Regents University, Augusta, GA, USA.|College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Republic of Korea.|Cancer Center, Georgia Regents University, Augusta, GA, USA.', 'Georgia|United States|Georgia|United States|Georgia|United States|Georgia', 2, 'Algorithms|Base Sequence|Colonic Neoplasms|Computer Simulation|CpG Islands|DNA Methylation|Databases, Genetic|Epigenesis, Genetic|HCT116 Cells|Humans|Nucleic Acid Conformation|Nucleosomes|ROC Curve|Reference Standards|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw785', '28035030', 'Chromatin accessibility plays a key role in epigenetic regulation of gene activation and silencing. Open chromatin regions allow regulatory elements such as transcription factors and polymerases to bind for gene expression while closed chromatin regions prevent the activity of transcriptional machinery. Recently, Methyltransferase Accessibility Protocol for individual templates-Bisulfite Genome Sequencing (MAPit-BGS) and nucleosome occupancy and methylome sequencing (NOMe-seq) have been developed for simultaneously profiling chromatin accessibility and DNA methylation on single molecules. Therefore, there is a great demand in developing computational methods to identify chromatin accessibility from MAPit-BGS and NOMe-seq.'),
(80467, 2017, 'bio', 'AnglerFish: a webserver for defining the geometry of xcexb1-helices in membrane proteins', 'Colledge, Matthew|Wallace, B A', 'Cancer Center, Georgia Regents University, Augusta, GA, USA.|Cancer Center, Georgia Regents University, Augusta, GA, USA.', 'Georgia|Georgia', 0, 'Internet|Membrane Proteins|Protein Conformation, alpha-Helical|Software', 'Journal Article', '10.1093/bioinformatics/btw781', '28035031', 'Integral membrane proteins that form helical pores and bundles constitute major drug targets, and many of their structures have been defined by crystallography and cryo-electron microscopy. The gating of channels and ligand binding of transporters generally involve changes in orientation of one or more the constituent helices in the structures. At present there is no standard easily accessible means for defining the orientation of a helix in a membrane protein structure. AnglerFish is a web-based tool for parameterising the angles of transmembrane helices based on PDB coordinates, with the helical orientations defined by the angles tilt and swing. AnglerFish is particularly useful for defining changes in structure between different states, including both symmetric and asymmetric transitions, and can be used to quantitate differences between related structures or different subunits within the same structure.'),
(80468, 2017, 'bio', 'Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data', 'Shringarpure, Suyash S|Mathias, Rasika A|Hernandez, Ryan D|OConnor, Timothy D|Szpiech, Zachary A|Torres, Raul|De La Vega, Francisco M|Bustamante, Carlos D|Barnes, Kathleen C|Taub, Margaret A|CAAPA Consortium', 'Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.|23 and Me Inc, Mountain View, CA, USA.|Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA.|Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.|Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA.|Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.|Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.|Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.|23 and Me Inc, Mountain View, CA, USA.|Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.|Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Accuracy|Genome, Human|Genomics|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btw786', '28035032', 'Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X).'),
(80469, 2017, 'bio', 'Training alignment parameters for arbitrary sequencers with LAST-TRAIN', 'Hamada, Michiaki|Ono, Yukiteru|Asai, Kiyoshi|Frith, Martin C', 'Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.|IMSBIO Co, Ltd, Tokyo 170-0013, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.|Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.', 'Japan|Japan|Japan|Japan', 14, 'Genome, Human|Humans|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw742', '28039163', 'LAST-TRAIN improves sequence alignment accuracy by inferring substitution and gap scores that fit the frequencies of substitutions, insertions, and deletions in a given dataset. We have applied it to mapping DNA reads from IonTorrent and PacBio RS, and we show that it reduces reference bias for Oxford Nanopore reads.'),
(80470, 2017, 'bio', 'AltORFev facilitates the prediction of alternative open reading frames in eukaryotic mRNAs', 'Kochetov, Alex V|Allmer, Jens|Klimenko, Alexandra I|Zuraev, Bulat S|Matushkin, Yury G|Lashin, Sergey A', 'Institute of Cytology & Genetics, SB RAS, Novosibirsk, Russia.|Molecular Biology and Genetics, Izmir Institute of Technologies, Izmir, Turkey.|Institute of Cytology & Genetics, SB RAS, Novosibirsk, Russia.|Institute of Cytology & Genetics, SB RAS, Novosibirsk, Russia.|Institute of Cytology & Genetics, SB RAS, Novosibirsk, Russia.|Institute of Cytology & Genetics, SB RAS, Novosibirsk, Russia.', '-|Turkey|-|-|-|-', 5, 'Eukaryota|Genomics|Open Reading Frames|Protein Biosynthesis|RNA, Messenger|Ribosome Subunits, Small, Eukaryotic|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw736', '28039164', 'Protein synthesis is not a straight forward process and one gene locus can produce many isoforms, for example, by starting mRNA translation from alternative start sites. altORF evaluator (altORFev) predicts alternative open reading frames within eukaryotic mRNA translated by a linear scanning mechanism and its modifications (leaky scanning and reinitiation). The program reveals the efficiently translated altORFs recognized by the majority of 40S ribosomal subunits landing on the 5-end of an mRNA. This information aids to reveal the functions of eukaryotic genes connected to synthesis of either unknown isoforms of annotated proteins or new unrelated polypeptides.'),
(80471, 2017, 'bio', 'PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases', 'Shende, Gauri|Haldankar, Harshala|Barai, Ram Shankar|Bharmal, Mohammed Husain|Shetty, Vinit|Idicula-Thomas, Susan', 'ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.|ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.|ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.|ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.|ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.|ICMR Biomedical Informatics Center, National Institute for Research in Reproductive Health, Mumbai, India.', 'India|India|India|India|India|India', 0, 'Candida albicans|Communicable Diseases|Computational Biology|Computer Simulation|Drug Discovery|Humans|Internet|Microbiota|Proteome', 'Journal Article', '10.1093/bioinformatics/btw760', '28039165', 'PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogens survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections.'),
(80472, 2017, 'bio', 'An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier', 'Xia, Jiaqi|Peng, Zhenling|Qi, Dawei|Mu, Hongbo|Yang, Jianyi', 'Department of Physics, Northeast Forestry University, Harbin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|Department of Physics, Northeast Forestry University, Harbin, China.|Department of Physics, Northeast Forestry University, Harbin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China|China|China', 0, 'Algorithms|Computational Biology|Protein Folding|Proteins|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw768', '28039166', 'Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before.'),
(80473, 2017, 'bio', 'SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns', 'Lin, Hui-Yi|Chen, Dung-Tsa|Huang, Po-Yu|Liu, Yung-Hsin|Ochoa, Augusto|Zabaleta, Jovanny|Mercante, Donald E|Fang, Zhide|Sellers, Thomas A|Pow-Sang, Julio M|Cheng, Chia-Ho|Eeles, Rosalind|Easton, Doug|Kote-Jarai, Zsofia|Amin Al Olama, Ali|Benlloch, Sara|Muir, Kenneth|Giles, Graham G|Wiklund, Fredrik|Gronberg, Henrik|Haiman, Christopher A|Schleutker, Johanna|Nordestgaard, Børge G|Travis, Ruth C|Hamdy, Freddie|Pashayan, Nora|Khaw, Kay-Tee|Stanford, Janet L|Blot, William J|Thibodeau, Stephen N|Maier, Christiane|Kibel, Adam S|Cybulski, Cezary|Cannon-Albright, Lisa|Brenner, Hermann|Kaneva, Radka|Batra, Jyotsna|Teixeira, Manuel R|Pandha, Hardev|Lu, Yong-Jie|PRACTICAL Consortium|Park, Jong Y', 'Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.|Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan.|Department of Biometrics, INC Research, LLC, Raleigh, NC, USA.|Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, USA.|Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, USA.|Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.|Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.|Department of Genitourinary Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.|The Institute of Cancer Research, London, UK.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.|The Institute of Cancer Research, London, UK.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.|University of Warwick, Coventry, UK.|Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia.|Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.|Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland.|Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark.|Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK.|Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.|Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.|Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK.|Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|International Epidemiology Institute, Rockville, MD, USA.|Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.|Institute of Human Genetics University Hospital Ulm, Ulm, Germany.|Brigham and Womens Hospital/Dana-Farber Cancer Institute, Boston, MA, USA.|International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.|Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.|Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University - Sofia, Sofia, Bulgaria.|Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia.|Department of Genetics, Portuguese Oncology Institute, Porto, Portugal.|The University of Surrey, Guildford, Surrey, UK.|Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.|Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.|Department of Cancer Epidemiology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 0, 'Epistasis, Genetic|ErbB Receptors|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Male|Matrix Metalloproteinase 16|Models, Genetic|Polymorphism, Single Nucleotide|Prostatic Neoplasms|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw762', '28039167', 'Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped.'),
(80474, 2017, 'bio', 'Parameter estimation for dynamical systems with discrete events and logical operations', 'Fröhlich, Fabian|Theis, Fabian J|Rädler, Joachim O|Hasenauer, Jan', 'Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Faculty of Physics, Ludwig-Maximilians-Universität, München 80539, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Green Fluorescent Proteins|Membrane Potentials|Models, Biological|Neurons|Software|Systems Biology|Transfection', 'Journal Article', '10.1093/bioinformatics/btw764', '28040696', 'Ordinary differential equation (ODE) models are frequently used to describe the dynamic behaviour of biochemical processes. Such ODE models are often extended by events to describe the effect of fast latent processes on the process dynamics. To exploit the predictive power of ODE models, their parameters have to be inferred from experimental data. For models without events, gradient based optimization schemes perform well for parameter estimation, when sensitivity equations are used for gradient computation. Yet, sensitivity equations for models with parameter- and state-dependent events and event-triggered observations are not supported by existing toolboxes.'),
(80475, 2017, 'bio', 'Virtual exploration of early stage atherosclerosis', 'Olivares, Andy L|González Ballester, Miguel A|Noailly, Jérôme', 'Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btw743', '28049130', '-'),
(80476, 2017, 'bio', 'SED-ML web tools: generate, modify and export standard-compliant simulation studies', 'Bergmann, Frank T|Nickerson, David|Waltemath, Dagmar|Scharm, Martin', 'COS/BioQUANT, Heidelberg University, Heidelberg, Germany.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.', 'Germany|New Zealand|Germany|Germany', 0, 'Computer Simulation|Internet|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btw812', '28049131', 'The Simulation Experiment Description Markup Language (SED-ML) is a standardized format for exchanging simulation studies independently of software tools. We present the SED-ML Web Tools, an online application for creating, editing, simulating and validating SED-ML documents. The Web Tools implement all current SED-ML specifications and, thus, support complex modifications and co-simulation of models in SBML and CellML formats. Ultimately, the Web Tools lower the bar on working with SED-ML documents and help users create valid simulation descriptions.'),
(80477, 2017, 'bio', 'LncRNA/DNA binding analysis reveals losses and gains and lineage specificity of genomic imprinting in mammals', 'Liu, Haihua|Shang, Xiaoxiao|Zhu, Hao', 'Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.', 'China|China|China', 13, 'Animals|Binding Sites|Computational Biology|DNA|Genomic Imprinting|Humans|Mammals|Mice|Promoter Regions, Genetic|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btw818', '28052924', 'Genomic imprinting is regulated by lncRNAs and is important for embryogenesis, physiology and behaviour in mammals. Aberrant imprinting causes diseases and disorders. Experimental studies have examined genomic imprinting primarily in humans and mice, thus leaving some fundamental issues poorly addressed. The cost of experimentally examining imprinted genes in many tissues in diverse species makes computational analysis of lncRNAs DNA binding sites valuable.'),
(80478, 2017, 'bio', 'ProQ3D: improved model quality assessments using deep learning', 'Uziela, Karolis|Menéndez Hurtado, David|Shu, Nanjiang|Wallner, Björn|Elofsson, Arne', 'Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.|Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.|Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.|Department of Physics, Chemistry and Biology (IFM)/Bioinformatics. Linköping University, ?Linköping, Sweden.|Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Computational Biology|Models, Molecular|Neural Networks (Computer)|Protein Conformation|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw819', '28052925', 'Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features).'),
(80479, 2017, 'bio', 'Lineage-specific mutational clustering in protein structures predicts evolutionary shifts in function', 'Adams, Jeremy|Mansfield, Michael J|Richard, Daniel J|Doxey, Andrew C', 'Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.|Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.|Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.|Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.', 'Canada|Canada|Canada|Canada', 8, 'Animals|Computational Biology|Evolution, Molecular|Mutation|Phylogeny|Plants|Protein Conformation|Proteins|Software|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btw815', '28052926', 'Spatially clustered mutations within specific regions of protein structure are thought to result from strong positive selection for altered protein functions and are a common feature of oncoproteins in cancer. Although previous studies have used spatial substitution clustering to identify positive selection between pairs of proteins, the ability of this approach to identify functional shifts in protein phylogenies has not been explored.'),
(80480, 2017, 'bio', 'DNABarcodes: an R package for the systematic construction of DNA sample tags', 'Buschmann, Tilo', 'Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.', 'Canada', 0, 'Algorithms|DNA Barcoding, Taxonomic|Software', 'Journal Article', '10.1093/bioinformatics/btw759', '28052927', 'DNA barcodes are commonly used for counting and discriminating purposes in molecular and cell biology. Not every set of DNA sequences is equally suitable for this goal. There is a growing demand for more sophisticated barcode designs, with only few tools available. We prepared an R package that combines known algorithms and innovative methods for the efficient, flexible and near-optimal generation of robust barcode sets.'),
(80481, 2017, 'bio', 'Planning bioinformatics workflows using an expert system', 'Chen, Xiaoling|Chang, Jeffrey T', 'School of Biomedical Informatics.|School of Biomedical Informatics.', '-|-', 6, 'Computational Biology|Expert Systems|High-Throughput Nucleotide Sequencing|Humans|Knowledge Bases|Oligonucleotide Array Sequence Analysis|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btw817', '28052928', 'Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used.'),
(80482, 2017, 'bio', 'PanViz: interactive visualization of the structure of functionally annotated pangenomes', 'Pedersen, Thomas Lin|Nookaew, Intawat|Wayne Ussery, David|Mxc3xa5nsson, Maria', 'Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, DK-2800 Lyngby, Denmark.|Comparative Genomics Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Comparative Genomics Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Assays, Culture and Enzymes Division, DK-2970?Hxc3xb8rsholm, Denmark.', 'Denmark|United States|United States|Denmark', 0, 'Computer Graphics|Gene Ontology|Genome|Genomics|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btw761', '28057677', 'PanViz is a novel, interactive, visualization tool for pangenome analysis. PanViz allows visualization of changes in gene group (groups of similar genes across genomes) classification as different subsets of pangenomes are selected, as well as comparisons of individual genomes to pangenomes with gene ontology based navigation of gene groups. Furthermore it allows for rich and complex visual querying of gene groups in the pangenome. PanViz visualizations require no external programs and are easily sharable, allowing for rapid pangenome analyses.'),
(80483, 2017, 'bio', 'SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library', 'Litfin, Thomas|Zhou, Yaoqi|Yang, Yuedong', 'School of Information and Communication Technology.|School of Information and Communication Technology.|School of Information and Communication Technology.', '-|-|-', 9, 'Drug Discovery|Ligands|Models, Molecular|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btw829', '28057679', 'The high cost of drug discovery motivates the development of accurate virtual screening tools. Binding-homology, which takes advantage of known protein-ligand binding pairs, has emerged as a powerful discrimination technique. In order to exploit all available binding data, modelled structures of ligand-binding sequences may be used to create an expanded structural binding template library.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80484, 2017, 'bio', 'An informative approach on differential abundance analysis for time-course metagenomic sequencing data', 'Luo, Dan|Ziebell, Sara|An, Lingling', 'Department of Epidemiology and Biostatistics, College of Public Health.|Interdisciplinary Program in Statistics.|Department of Epidemiology and Biostatistics, College of Public Health.', '-|-|-', 0, 'Animals|Bacteria|Diet|Diet, Fat-Restricted|Diet, Western|Gastrointestinal Microbiome|High-Throughput Nucleotide Sequencing|Male|Metagenomics|Mice|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw828', '28057680', 'The advent of high-throughput next generation sequencing technology has greatly promoted the field of metagenomics where previously unattainable information about microbial communities can be discovered. Detecting differentially abundant features (e.g. species or genes) plays a critical role in revealing the contributors (i.e. pathogens) to the biological or medical status of microbial samples. However, currently available statistical methods lack power in detecting differentially abundant features contrasting different biological or medical conditions, in particular, for time series metagenomic sequencing data. We have proposed a novel procedure, metaDprof, which is built upon a spline-based method assuming heterogeneous error, to meet the challenges of detecting differentially abundant features from metagenomic samples by comparing different biological/medical conditions across time. It contains two stages: (i) global detection on features and (ii) time interval detection for significant features. The detection procedures in both stages are based on sound statistical support.'),
(80485, 2017, 'bio', 'phyloSkeleton: taxon selection, data retrieval and marker identification for phylogenomics', 'Guy, Lionel', 'Department of Epidemiology and Biostatistics, College of Public Health.', '-', 5, 'Databases, Genetic|Genetic Markers|Genome|Genomics|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw824', '28057682', 'With the wealth of available genome sequences, a difficult and tedious part of inferring phylogenomic trees is now to select genomes with an appropriate taxon density in the different parts of the tree. The package described here offers tools to easily select the most representative organisms, following a set of simple rules based on taxonomy and assembly quality, to retrieve the genomes from public databases (NCBI, JGI), to annotate them if necessary, to identify given markers in these, and to prepare files for multiple sequence alignment.'),
(80486, 2017, 'bio', 'ORCAN-a web-based meta-server for real-time detection and functional annotation of orthologs', 'Zielezinski, Andrzej|Dziubek, Michal|Sliski, Jan|Karlowski, Wojciech M', 'Department of Epidemiology and Biostatistics, College of Public Health.|Department of Epidemiology and Biostatistics, College of Public Health.|Department of Epidemiology and Biostatistics, College of Public Health.|Department of Epidemiology and Biostatistics, College of Public Health.', '-|-|-|-', 0, 'Computational Biology|Gene Ontology|Internet|Protein Domains|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btw825', '28057683', 'ORCAN (ORtholog sCANner) is a web-based meta-server for one-click evolutionary and functional annotation of protein sequences. The server combines information from the most popular orthology-prediction resources, including four tools and four online databases. Functional annotation utilizes five additional comparisons between the query and identified homologs, including: sequence similarity, protein domain architectures, functional motifs, Gene Ontology term assignments and a list of associated articles. Furthermore, the server uses a plurality-based rating system to evaluate the orthology relationships and to rank the reference proteins by their evolutionary and functional relevance to the query. Using a dataset of xe2x88xbc1 million true yeast orthologs as a sample reference set, we show that combining multiple orthology-prediction tools in ORCAN increases the sensitivity and precision by 1-2 percent points.'),
(80487, 2017, 'bio', 'iSmaRT: a toolkit for a comprehensive analysis of small RNA-Seq data', 'Panero, Riccardo|Rinaldi, Antonio|Memoli, Domenico|Nassa, Giovanni|Ravo, Maria|Rizzo, Francesca|Tarallo, Roberta|Milanesi, Luciano|Weisz, Alessandro|Giurato, Giorgio', 'Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Institute for Biomedical Technologies, National Research Council, Segrate, MI, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.|Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, SA, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 9, 'High-Throughput Nucleotide Sequencing|Humans|Huntington Disease|MicroRNAs|RNA, Small Interfering|RNA, Small Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw734', '28057684', 'The interest in investigating the biological roles of small non-coding RNAs (sncRNAs) is increasing, due to the pleiotropic effects of these molecules exert in many biological contexts. While several methods and tools are available to study microRNAs (miRNAs), only few focus on novel classes of sncRNAs, in particular PIWI-interacting RNAs (piRNAs). To overcome these limitations, we implemented iSmaRT ( i ntegrative Sm all R NA T ool-kit), an automated pipeline to analyze smallRNA-Seq data.'),
(80488, 2017, 'bio', 'RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data', 'Shankar, Ravi D|Bhattacharya, Sanchita|Jujjavarapu, Chethan|Andorf, Sandra|Wiser, Jeffery A|Butte, Atul J', 'Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.|Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA.|Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA.|Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.|Northrop Grumman Information Technology Health Solutions, Rockville, MD 20850, USA.|Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Allergy and Immunology|Computational Biology|Humans|Research|Software', 'Journal Article', '10.1093/bioinformatics/btw719', '28057685', ': Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology.'),
(80489, 2017, 'bio', 'AFRESh: an adaptive framework for compression of reads and assembled sequences with random access functionality', 'Paridaens, Tom|Van Wallendael, Glenn|De Neve, Wesley|Lambert, Peter', 'Data Science Lab, Ghent University - iMinds, Ghent, Belgium.|Data Science Lab, Ghent University - iMinds, Ghent, Belgium.|Data Science Lab, Ghent University - iMinds, Ghent, Belgium.|Data Science Lab, Ghent University - iMinds, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 1, 'Algorithms|Bacteria|Data Compression|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Plants|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx001', '28057687', 'The past decade has seen the introduction of new technologies that lowered the cost of genomic sequencing increasingly. We can even observe that the cost of sequencing is dropping significantly faster than the cost of storage and transmission. The latter motivates a need for continuous improvements in the area of genomic data compression, not only at the level of effectiveness (compression rate), but also at the level of functionality (e.g. random access), configurability (effectiveness versus complexity, coding tool set …) and versatility (support for both sequenced reads and assembled sequences). In that regard, we can point out that current approaches mostly do not support random access, requiring full files to be transmitted, and that current approaches are restricted to either read or sequence compression.'),
(80490, 2017, 'bio', 'ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives', 'Wang, Jyun-Rong|Huang, Wen-Lin|Tsai, Ming-Ju|Hsu, Kai-Ti|Huang, Hui-Ling|Ho, Shinn-Ying', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Department and Institute of Industrial Engineering and Management, Minghsin University of Science and Technology, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.', '-|-|-|-|-|-', 22, 'Computational Biology|Humans|Software|Support Vector Machine|Ubiquitination', 'Journal Article', '10.1093/bioinformatics/btw701', '28062441', 'Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models.'),
(80491, 2017, 'bio', 'SINE_scan: an efficient tool to discover short interspersed nuclear elements (SINEs) in large-scale genomic datasets', 'Mao, Hongliang|Wang, Hao', 'T-Life Research Center, Department of Physics, Fudan University, Shanghai 200433, Peoples Republic of China.|T-Life Research Center, Department of Physics, Fudan University, Shanghai 200433, Peoples Republic of China.', 'China|China', 0, 'Animals|DNA Transposable Elements|Genomics|Humans|Plants|Sensitivity and Specificity|Sequence Analysis, DNA|Short Interspersed Nucleotide Elements|Software', 'Journal Article', '10.1093/bioinformatics/btw718', '28062442', 'Short Interspersed Nuclear Elements (SINEs) are transposable elements (TEs) that amplify through a copy-and-paste mode via RNA intermediates. The computational identification of new SINEs are challenging because of their weak structural signals and rapid diversification in sequences.'),
(80492, 2017, 'bio', 'A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on PAM50 algorithm', 'Fresno, Cristxc3xb3bal|Gonzxc3xa1lez, Germxc3xa1n Alexis|Merino, Gabriela Alejandra|Flesia, Ana Georgina|Podhajcer, Osvaldo Luis|Llera, Andrea Sabina|Fernxc3xa1ndez, Elmer Andrxc3xa9s', 'UA AREA CS. AGR. ING. BIO. Y S, CONICET, Universidad Catxc3xb3lica de Cxc3xb3rdoba, Cxc3xb3rdoba 5016, Argentina.|UA AREA CS. AGR. ING. BIO. Y S, CONICET, Universidad Catxc3xb3lica de Cxc3xb3rdoba, Cxc3xb3rdoba 5016, Argentina.|UA AREA CS. AGR. ING. BIO. Y S, CONICET, Universidad Catxc3xb3lica de Cxc3xb3rdoba, Cxc3xb3rdoba 5016, Argentina.|CIEM-CONICET and FAMAF, Universidad Nacional de Cxc3xb3rdoba, Cxc3xb3rdoba 5000, Argentina.|Laboratory of Molecular and Cellular Therapy, Instituto Leloir-CONICET, Buenos Aires 1405, Argentina.|Laboratory of Molecular and Cellular Therapy, Instituto Leloir-CONICET, Buenos Aires 1405, Argentina.|UA AREA CS. AGR. ING. BIO. Y S, CONICET, Universidad Catxc3xb3lica de Cxc3xb3rdoba, Cxc3xb3rdoba 5016, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 0, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Humans|Neoplasm Recurrence, Local|Prognosis|Risk|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btw704', '28062443', 'The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context.'),
(80493, 2017, 'bio', 'Robust parameter estimation for dynamical systems from outlier-corrupted data', 'Maier, Corinna|Loos, Carolin|Hasenauer, Jan', 'Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Likelihood Functions|Models, Biological|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btw703', '28062444', 'Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers.'),
(80494, 2017, 'bio', 'Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines', 'Teodoro, George|Kurxc3xa7, Tahsin M|Taveira, Luxc3xads F R|Melo, Alba C M A|Gao, Yi|Kong, Jun|Saltz, Joel H', 'Department of Computer Science, University of Brasxc3xadlia, Brasxc3xadlia 70910-900, Brazil.|Biomedical Informatics Department, Stony Brook University, Stony Brook, NY 11794-8322, USA.|Department of Computer Science, University of Brasxc3xadlia, Brasxc3xadlia 70910-900, Brazil.|Department of Computer Science, University of Brasxc3xadlia, Brasxc3xadlia 70910-900, Brazil.|Biomedical Informatics Department, Stony Brook University, Stony Brook, NY 11794-8322, USA.|Biomedical Informatics Department, Emory University, Atlanta, GA 30322, USA.|Biomedical Informatics Department, Stony Brook University, Stony Brook, NY 11794-8322, USA.', 'Brazil|United States|Brazil|Brazil|United States|United States|United States', 10, 'Algorithms|Brain Neoplasms|Glioblastoma|Humans|Image Processing, Computer-Assisted', 'Journal Article', '10.1093/bioinformatics/btw749', '28062445', 'Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows.'),
(80495, 2017, 'bio', 'pHMM-tree: phylogeny of profile hidden Markov models', 'Huo, Luyang|Zhang, Han|Huo, Xueting|Yang, Yasong|Li, Xueqiong|Yin, Yanbin', 'College of Computer and Control Engineering, Nankai University, Tianjin, China.|College of Computer and Control Engineering, Nankai University, Tianjin, China.|College of Computer and Control Engineering, Nankai University, Tianjin, China.|Department of Biological Sciences, Northern Illinois University, DeKalb, IL, USA.|Department of Biological Sciences, Northern Illinois University, DeKalb, IL, USA.|Department of Biological Sciences, Northern Illinois University, DeKalb, IL, USA.', 'China|China|China|United States|United States|United States', 9, 'Algorithms|Markov Chains|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw779', '28062446', 'Protein families are often represented by profile hidden Markov models (pHMMs). Homology between two distant protein families can be determined by comparing the pHMMs. Here we explored the idea of building a phylogeny of protein families using the distance matrix of their pHMMs. We developed a new software and web server (pHMM-tree) to allow four major types of inputs: (i) multiple pHMM files, (ii) multiple aligned protein sequence files, (iii) mixture of pHMM and aligned sequence files and (iv) unaligned protein sequences in a single file. The output will be a pHMM phylogeny of different protein families delineating their relationships. We have applied pHMM-tree to build phylogenies for CAZyme (carbohydrate active enzyme) classes and Pfam clans, which attested its usefulness in the phylogenetic representation of the evolutionary relationship among distant protein families.'),
(80496, 2017, 'bio', 'Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity', 'Negi, Surendra S|Braun, Werner', 'Department of Biological Sciences, Northern Illinois University, DeKalb, IL, USA.|Department of Biological Sciences, Northern Illinois University, DeKalb, IL, USA.', 'United States|United States', 8, 'Allergens|Amino Acid Sequence|Amino Acids|Animals|Antibodies|Bees|Cattle|Computational Biology|Cross Reactions|Crystallography, X-Ray|Databases, Protein|Epitopes|Humans|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btw767', '28062447', 'The phenomenon of cross-reactivity between allergenic proteins plays an important role to understand how the immune system recognizes different antigen proteins. Allergen proteins are known to cross-react if their sequence comparison shows a high sequence identity which also implies that the proteins have a similar 3D fold. In such cases, linear sequence alignment methods are frequently used to predict cross-reactivity between allergenic proteins. However, the prediction of cross-reactivity between distantly related allergens continues to be a challenging task. To overcome this problem, we developed a new structure-based computational method, Cross-React, to predict cross-reactivity between allergenic proteins available in the Structural Database of Allergens (SDAP). Our method is based on the hypothesis that we can find surface patches on 3D structures of potential allergens with amino acid compositions similar to an epitope in a known allergen. We applied the Cross-React method to a diverse set of seven allergens, and successfully identified several cross-reactive allergens with high to moderate sequence identity which have also been experimentally shown to cross-react. Based on these findings, we suggest that Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity.'),
(80497, 2017, 'bio', 'ontologyX: a suite of R packages for working with ontological data', 'Greene, Daniel|Richardson, Sylvia|Turro, Ernest', 'MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Biological Ontologies|Software', 'Journal Article', '10.1093/bioinformatics/btw763', '28062448', 'Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines.'),
(80498, 2017, 'bio', 'Context-sensitive network-based disease genetics prediction and its implications in drug discovery', 'Chen, Yang|Xu, Rong', 'MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom', 9, 'Algorithms|Antiparkinson Agents|Disease|Drug Discovery|Gene Regulatory Networks|Genes|Humans|Parkinson Disease|Phenotype', 'Journal Article', '10.1093/bioinformatics/btw737', '28062449', 'Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery.'),
(80499, 2017, 'bio', 'The structural bioinformatics library: modeling in biomolecular science and beyond', 'Cazals, Frxc3xa9dxc3xa9ric|Dreyfus, Tom', 'MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom', 14, 'Algorithms|Computational Biology|Gene Library|Licensure|Models, Molecular|Software', 'Journal Article', '10.1093/bioinformatics/btw752', '28062450', 'Software in structural bioinformatics has mainly been application driven. To favor practitioners seeking off-the-shelf applications, but also developers seeking advanced building blocks to develop novel applications, we undertook the design of the Structural Bioinformatics Library ( SBL , http://sbl.inria.fr ), a generic Cxe2x80x89++/python cross-platform software library targeting complex problems in structural bioinformatics. Its tenet is based on a modular design offering a rich and versatile framework allowing the development of novel applications requiring well specified complex operations, without compromising robustness and performances.'),
(80500, 2017, 'bio', 'RECKONER: read error corrector based on KMC', 'Dlugosz, Maciej|Deorowicz, Sebastian', 'MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Eukaryota|Genome Size|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btw746', '28062451', 'Presence of sequencing errors in data produced by next-generation sequencers affects quality of downstream analyzes. Accuracy of them can be improved by performing error correction of sequencing reads. We introduce a new correction algorithm capable of processing eukaryotic close to 500 Mbp-genome-size, high error-rated data using less than 4xe2x80x89GB of RAM in about 35xe2x80x89min on 16-core computer.'),
(80501, 2017, 'bio', 'Automated neuron tracing using probability hypothesis density filtering', 'Radojevic, Miroslav|Meijering, Erik', 'MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.|MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.', 'United Kingdom|United Kingdom', 0, 'Animals|Bayes Theorem|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Fluorescence|Neuroanatomical Tract-Tracing Techniques|Neurons|Software', 'Journal Article', '10.1093/bioinformatics/btw751', '28065895', 'The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.'),
(80502, 2017, 'bio', 'GE-mini: a mobile APP for large-scale gene expression visualization', 'Tang, Zefang|Li, Chenwei|Zhang, Karena|Yang, Mingyu|Hu, Xueda', 'College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.', 'China|China|China|China|China', 0, 'Gene Expression Regulation, Neoplastic|Humans|Mobile Applications|Neoplasms|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btw775', '28065896', 'The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects produced large-scale RNA sequencing data, which provides an opportunity for performing integrated expression analysis for all genes across tens of thousands of tumor and normal tissue specimens. Rapid access to and easy visualization of such valuable data could facilitate research in a wide biological area. Here, we present the GE-mini APP for smart phones, a mobile visualization tool for integrated gene expression data based on both TCGA and GTEx. This gene-centric expression viewer provides a convenient method for displaying expression profiles of all available tumor and tissue types, while allowing drilling down to detailed views for specific tissue types.'),
(80503, 2017, 'bio', 'TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data', 'Motamedian, Ehsan|Mohammadi, Maryam|Shojaosadati, Seyed Abbas|Heydari, Mona', 'College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.|College of Life Sciences and BIOPIC, Peking University, Beijing, China.', 'China|China|China|China', 0, 'Algorithms|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genome|Metabolic Networks and Pathways|Models, Biological|Saccharomyces cerevisiae|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btw772', '28065897', 'Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations.'),
(80504, 2017, 'bio', 'MetaSpark: a spark-based distributed processing tool to recruit metagenomic reads to reference genomes', 'Zhou, Wei|Li, Ruilin|Yuan, Shuo|Liu, ChangChun|Yao, Shaowen|Luo, Jing|Niu, Beifang', 'School of Software, Yunnan University, Kunming, China.|Computer Network Information Center of Chinese Academy of Sciences, Beijing, China.|School of Software, Yunnan University, Kunming, China.|School of Software, Yunnan University, Kunming, China.|School of Software, Yunnan University, Kunming, China.|School of Life Sciences and State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China.|Computer Network Information Center of Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China', 17, 'Algorithms|Genome|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Reference Standards|Software', 'Evaluation Studies', '10.1093/bioinformatics/btw750', '28065898', 'With the advent of next-generation sequencing, traditional bioinformatics tools are challenged by massive raw metagenomic datasets. One of the bottlenecks of metagenomic studies is lack of large-scale and cloud computing suitable data analysis tools. In this paper, we proposed a Spark based tool, called MetaSpark, to recruit metagenomic reads to reference genomes. MetaSpark benefits from the distributed data set (RDD) of Spark, which makes it able to cache data set in memory across cluster nodes and scale well with the datasets. Compared with previous metagenomics recruitment tools, MetaSpark recruited significantly more reads than many programs such as SOAP2, BWA and LAST and increased recruited reads by xe2x88xbc4% compared with FR-HIT when there were 1 million reads and 0.75xe2x80x89GB references. Different test cases demonstrate MetaSparks scalability and overall high performance.'),
(80505, 2017, 'bio', 'Statistical inference of protein structural alignments using information and compression', 'Collier, James H|Allison, Lloyd|Lesk, Arthur M|Stuckey, Peter J|Garcia de la Banda, Maria|Konagurthu, Arun S', 'Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.|Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.|Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.|Department of Computing and Information Systems, University of Melbourne, Parkville, VIC 3010, Australia.|Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.|Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.', 'Australia|Australia|United States|Australia|Australia|Australia', 0, 'Algorithms|Bayes Theorem|Data Compression|Models, Statistical|Proteins|Reproducibility of Results|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw757', '28065899', 'Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates). Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a statistical framework for the precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power-the amount of lossless compression achieved to explain the protein coordinates using that alignment.'),
(80506, 2017, 'bio', 'Exploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits', 'So, Hon-Cheong|Sham, Pak C', 'School of Biomedical Sciences, Chinese University of Hong Kong, Shatin, Hong Kong.|Department of Psychiatry, University of Hong Kong, PokFuLam, Hong Kong.', 'Hong Kong|Hong Kong', 0, 'Bipolar Disorder|Coronary Artery Disease|Depressive Disorder, Major|Diabetes Mellitus, Type 2|Genetic Predisposition to Disease|Genome-Wide Association Study|Genomics|Humans|Multifactorial Inheritance|Polymorphism, Single Nucleotide|Risk|Schizophrenia', 'Journal Article', '10.1093/bioinformatics/btw745', '28065900', 'It is hoped that advances in our knowledge in disease genomics will contribute to personalized medicine such as individualized preventive strategies or early diagnoses of diseases. With the growth of genome-wide association studies (GWAS) in the past decade, how far have we reached this goal? In this study we explored the predictive ability of polygenic risk scores (PRSs) derived from GWAS for a range of complex disease and traits.'),
(80507, 2017, 'bio', 'RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination', 'Mirzaei, Sajad|Wu, Yufeng', 'Department of Psychiatry, University of Hong Kong, PokFuLam, Hong Kong.|Department of Psychiatry, University of Hong Kong, PokFuLam, Hong Kong.', 'Hong Kong|Hong Kong', 5, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Genealogy and Heraldry|Genetics, Population|Haplotypes|Humans|Models, Genetic|Mutation|Phylogeny|Polymorphism, Single Nucleotide|Recombination, Genetic|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btw735', '28065901', ': Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination.'),
(80508, 2017, 'bio', 'PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS', 'Chen, Feng|Wang, Shuang|Jiang, Xiaoqian|Ding, Sijie|Lu, Yao|Kim, Jihoon|Sahinalp, S Cenk|Shimizu, Chisato|Burns, Jane C|Wright, Victoria J|Png, Eileen|Hibberd, Martin L|Lloyd, David D|Yang, Hai|Telenti, Amalio|Bloss, Cinnamon S|Fox, Dov|Lauter, Kristin|Ohno-Machado, Lucila', 'Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.|Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.|Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.|Section of Pediatrics, Imperial College London, London, UK.|Genome Institute of Singapore, ASTAR, Singapore, Singapore.|Genome Institute of Singapore, ASTAR, Singapore, Singapore.|Deparment of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.|J. Craig Venter Institute, La Jolla, CA, USA.|Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.|School of Law, University of San Diego, San Diego, CA, USA.|Cryptography Group, Microsoft Research, San Diego, CA, USA.|Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|India|United Kingdom|United Kingdom|United Kingdom|Singapore|Singapore|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Unit', 0, 'Computer Security|Genetic Association Studies|Genomics|Humans|Mucocutaneous Lymph Node Syndrome|Privacy|Rare Diseases|Software', 'Journal Article', '10.1093/bioinformatics/btw758', '28065902', 'We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information.'),
(80509, 2017, 'bio', 'caspo: a toolbox for automated reasoning on the response of logical signaling networks families', 'Videla, Santiago|Saez-Rodriguez, Julio|Guziolowski, Carito|Siegel, Anne', 'LBSI, Fundación Instituto Leloir (IIBBA-CONICET), Buenos Aires, C1405BWE, Argentina.|RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen D-52074, Germany.|IRCCyN UMR CNRS 6597, École Centrale de Nantes, Nantes 44321, France.|CNRS, UMR 6074-IRISA, 35042 Rennes, France.', 'Argentina|Germany|France|France', 0, 'Hepatocytes|Humans|Models, Biological|Phosphoproteins|Signal Transduction|Software|Systems Biology|Workflow', 'Journal Article', '10.1093/bioinformatics/btw738', '28065903', 'We introduce the caspo toolbox, a python package implementing a workflow for reasoning on logical networks families. Our software allows researchers to (i) a family of logical networks derived from a given topology and explaining the experimental response to various perturbations, (ii) all logical networks in a given family by their input-output behaviors, (iii) the response of the system to every possible perturbation based on the ensemble of predictions, (iv) new experimental perturbations to discriminate among a family of logical networks, and (v) a family of logical networks by finding all interventions strategies forcing a set of targets into a desired steady state.'),
(80510, 2017, 'bio', 'KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis', 'Hastreiter, Maximilian|Jeske, Tim|Hoser, Jonathan|Kluge, Michael|Ahomaa, Kaarin|Friedl, Marie-Sophie|Kopetzky, Sebastian J|Quell, Jan-Dominik|Mewes, H-Werner|Küffner, Robert', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'High-Throughput Nucleotide Sequencing|Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx003', '28069593', 'Analysis of Next Generation Sequencing (NGS) data requires the processing of large datasets by chaining various tools with complex input and output formats. In order to automate data analysis, we propose to standardize NGS tasks into modular workflows. This simplifies reliable handling and processing of NGS data, and corresponding solutions become substantially more reproducible and easier to maintain. Here, we present a documented, linux-based, toolbox of 42 processing modules that are combined to construct workflows facilitating a variety of tasks such as DNAseq and RNAseq analysis. We also describe important technical extensions. The high throughput executor (HTE) helps to increase the reliability and to reduce manual interventions when processing complex datasets. We also provide a dedicated binary manager that assists users in obtaining the modules executables and keeping them up to date. As basis for this actively developed toolbox we use the workflow management software KNIME.'),
(80511, 2017, 'bio', 'SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network', 'Liu, Yuanlong|Brossard, Myriam|Roqueiro, Damian|Margaritte-Jeannin, Patricia|Sarnowski, Chloé|Bouzigon, Emmanuelle|Demenais, Florence', 'INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.|INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.|Department of Biosystems Science and Engineering, ETH Zurich, Machine Learning and Computational Biology Lab, Switzerland.|INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.|INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.|INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.|INSERM, Genetic Variation and Human Diseases Unit, UMR-946, Paris, France.', 'France|France|Switzerland|France|France|France|France', 6, 'Algorithms|Asthma|Computational Biology|Gene Regulatory Networks|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx004', '28069594', 'Apart from single marker-based tests classically used in genome-wide association studies (GWAS), network-assisted analysis has become a promising approach to identify a set of genes associated with disease. To date, most network-assisted methods aim at finding genes connected in a background network, whatever the density or strength of their connections. This can hamper the findings as sparse connections are non-robust against noise from either the GWAS results or the network resource.'),
(80512, 2017, 'bio', 'Fast and accurate phylogeny reconstruction using filtered spaced-word matches', 'Leimeister, Chris-Andrxc3xa9|Sohrabi-Jahromi, Salma|Morgenstern, Burkhard', 'Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077?Gxc3xb6ttingen, Germany.|Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077?Gxc3xb6ttingen, Germany.|Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077xe2x80x89Gxc3xb6ttingen, Germany.', 'Germany|Germany|Germany', 23, 'Algorithms|Base Sequence|Computer Simulation|Genome, Bacterial|Genome, Plant|Genomics|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btw776', '28073754', 'Word-based or alignment-free algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods.'),
(80513, 2017, 'bio', 'Advanced Boolean modeling of biological networks applied to systems pharmacology', 'Irurzun-Arana, Itziar|Pastor, José Martín|Trocóniz, Iñaki F|Gómez-Mantilla, José David', 'Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077xe2x80x89Gxc3xb6ttingen, Germany.|Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077xe2x80x89Gxc3xb6ttingen, Germany.|Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077xe2x80x89Gxc3xb6ttingen, Germany.|Department of Bioinformatics, University of Gxc3xb6ttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077xe2x80x89Gxc3xb6ttingen, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Disease|Drug Discovery|Models, Theoretical|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btw747', '28073755', 'Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.'),
(80514, 2017, 'bio', 'Feature selection using a one dimensional naxc3xafve Bayes classifier increases the accuracy of support vector machine classification of CDR3 repertoires', 'Cinelli, Mattia|Sun, Yuxin|Best, Katharine|Heather, James M|Reich-Zeliger, Shlomit|Shifrut, Eric|Friedman, Nir|Shawe-Taylor, John|Chain, Benny', 'Division of Infection and Immunity.|Department of Computer Science.|Division of Infection and Immunity.|Division of Infection and Immunity.|Department of Immunology, Weizmann Institute, Rehovot, Israel.|Department of Immunology, Weizmann Institute, Rehovot, Israel.|Department of Immunology, Weizmann Institute, Rehovot, Israel.|Department of Computer Science.|Division of Infection and Immunity.', '-|-|-|-|Israel|Israel|Israel|-|-', 28, 'Amino Acids|Animals|Bayes Theorem|CD4-Positive T-Lymphocytes|Complementarity Determining Regions|Databases, Genetic|Humans|Mice, Inbred C57BL|Receptors, Antigen, T-Cell, alpha-beta|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btw771', '28073756', 'Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor xcexb2 chain complementarity determining region 3 (CDR3xcexb2) sequences following immunization with ovalbumin administered with complete Freunds adjuvant (CFA) or CFA alone.'),
(80515, 2017, 'bio', 'Rebuttal to the Letter to the Editor in response to the paper: proper evaluation of alignment-free network comparison methods', 'Yaveroglu, Ömer Nebil|Malod-Dognin, Noël|Milenkovic, Tijana|Pržulj, Nataša', 'Calit2, University of California, Irvine, CA, USA.|Department of Computer Science, University College London, London, UK.|Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA.|Department of Computer Science, University College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btw388', '28073757', '-');
