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REPARATION: ribosome profiling assisted (re-)annotation of bacterial genomes

NUCLEIC ACIDS RESEARCH 2017年第20期45卷 e168-e168页

作者： Ndah, Elvis Jonckheere, Veronique Giess, Adam Valen, Eivind Menschaert, Gerben Van Damme, Petra VIB UGent Ctr Med Biotechnol B-9000 Ghent Belgium Univ Ghent Dept Biochem B-9000 Ghent Belgium Univ Ghent Fac Biosci Engn Dept Math Modelling Stat & Bioinformat Lab Bioinformat & Computat Genom B-9000 Ghent Belgium Univ Bergen Dept Informat Computat Biol Unit N-5020 Bergen Norway Univ Bergen Sars Int Ctr Marine Mol Biol N-5008 Bergen Norway

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence composition and often underestimate the complexity of the proteome. We developed RibosomeE Profiling Assisted (re-)annotation (REPARATION), a de novo machine learning algorithm that takes advantage of experimental protein synthesis evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation (https://***/Biobix/ REPARATION). REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs and >70% of all (variants of) annotated protein coding ORFs were predicted by REPARATION to be translated. Our predictions are supported by matching mass spectrometry proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to intrinsically perform a de novo ORF delineation in bacterial genomes irrespective of the sequence features linked to open reading frames.

关键词： Bacillus subtilis/genetics Computational Biology/methods Escherichia coli K12/genetics Genome Bacterial/genetics molecular sequence annotation/methods Salmonella typhimurium/genetics Algorithms Chromosome Mapping Machine Learning Open Reading Frames/genetics Ribosomes/genetics

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IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data

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BIOINFORMATICS 2015年第17期31卷 2870-2873页

作者： Baez-Ortega, Adrian Lorenzo-Diaz, Fabian Hernandez, Mariano Ignacio Gonzalez-Vila, Carlos Luis Roda-Garcia, Jose Colebrook, Marcos Flores, Carlos Inst Tecnol & Energias Renovables Dept Informat Technol Santa Cruz De Tenerife Spain Univ La Laguna Inst Univ Enfermedades Trop & Salud Publ Canarias Appl Genom Grp G2A Santa Cruz De Tenerife Spain Hosp Univ Nuestra Senora Candelaria Res Unit Santa Cruz De Tenerife Spain Univ La Laguna Dept Ingn Informat & Sistemas Santa Cruz De Tenerife Spain Inst Salud Carlos III CIBER Enfermedades Resp Madrid Spain

We introduce IonGAP, a publicly available Web platform designed for the analysis of whole bacterial genomes using Ion Torrent sequence data. Besides assembly, it integrates a variety of comparative genomics, annotation and bacterial classification routines, based on the widely used FASTQ, BAM and SRA file formats. Benchmarking with different datasets evidenced that IonGAP is a fast, powerful and simple-to-use bioinformatics tool. By releasing this platform, we aim to translate low-cost bacterial genome analysis for microbiological prevention and control in healthcare, agroalimentary and pharmaceutical industry applications.

关键词： Bacteria/classification Bacteria/genetics Computational Biology/methods Databases, Genetic Genome, Bacterial Genome, Bacterial Genomics/methods High-Throughput Nucleotide Sequencing/methods Information Storage and Retrieval molecular sequence annotation/methods sequence Analysis, DNA Software

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Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets

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BIOINFORMATICS 2015年第1期31卷 102-108页

作者： Pomyen, Yotsawat Segura, Marcelo Ebbels, Timothy M. D. Keun, Hector C. Univ London Imperial Coll Sci Technol & Med Sect Computat & Syst Med Dept Surg & Canc London SW7 2AZ England Chulabhorn Res Inst Translat Res Unit Bangkok 10210 Thailand

Motivation: Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account. Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance. However, ORA is usually applied to a set of variables differentiating a single experimental variable and does not take into account correlations. Results: Over-representation of correlation analysis (ORCA) is a novel combination of ORA and correlation analysis that provides a means to test whether more associations exist between two specific groups of variables than expected by chance. The method is exemplified by application to drug sensitivity and microRNA expression data from a panel of cancer cell lines (NCI60). ORCA highlighted a previously reported correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors. We also used this approach to validate microRNA clusters predicted by mRNA correlations. These observations suggest that ORCA has the potential to reveal novel insights from these data, which are not readily apparent using classical ORA.

关键词： Alkylating Agents/pharmacology Biomarkers, Tumor/analysis Computational Biology/methods Data Interpretation, Statistical Databases, Factual Datasets as Topic Enzyme Inhibitors/pharmacology Gene Expression Profiling Genomics Humans MicroRNAs/genetics MicroRNAs/genetics molecular sequence annotation/methods Neoplasms/drug therapy Neoplasms/genetics Tumor Cells, Cultured

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GSDS 2.0: an upgraded gene feature visualization server

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BIOINFORMATICS 2015年第8期31卷 1296-1297页

作者： Hu, Bo Jin, Jinpu Guo, An-Yuan Zhang, He Luo, Jingchu Gao, Ge Peking Univ Coll Life Sci Ctr Bioinformat State Key Lab Prot & Plant Gene Res Beijing 100871 Peoples R China Beijing Normal Univ Coll Life Sci Beijing 100875 Peoples R China Huazhong Univ Sci & Technol Coll Life Sci & Technol Dept Syst Biol Wuhan 430074 Peoples R China

Visualizing genes' structure and annotated features helps biologists to investigate their function and evolution intuitively. The Gene Structure Display Server (GSDS) has been widely used by more than 60 000 users since its first publication in 2007. Here, we reported the upgraded GSDS 2.0 with a newly designed interface, supports for more types of annotation features and formats, as well as an integrated visual editor for editing the generated figure. Moreover, a user-specified phylogenetic tree can be added to facilitate further evolutionary analysis. The full source code is also available for downloading.

关键词： Genes/genetics Genomics/methods Humans Internet molecular sequence annotation/methods Phylogeny Phylogeny Software User-Computer Interface

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CiVi: circular genome visualization with unique features to analyze sequence elements

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BIOINFORMATICS 2015年第17期31卷 2867-2869页

作者： Overmars, Lex van Hijum, Sacha A. F. T. Siezen, Roland J. Francke, Christof Radboud Univ Nijmegen Med Ctr CMBI NL-6525 GA Nijmegen Netherlands Netherlands Bioinformat Ctr NL-6500 HB Nijmegen Netherlands Univ Amsterdam Inst Biodivers & Ecosyst Dynam Amsterdam Netherlands NIZO Food Res BV Ede Netherlands Hgsch Arnhem Nijmegen Bioctr NL-6525 EM Nijmegen Netherlands

We have developed CiVi, a user-friendly web-based tool to create custom circular maps to aid the analysis of microbial genomes and sequence elements. sequence related data such as gene-name, COG class, PFAM domain, GC%, and subcellular location can be comprehensively viewed. Quantitative gene-related data (e.g. expression ratios or read counts) as well as predicted sequence elements (e.g. regulatory sequences) can be uploaded and visualized. CiVi accommodates the analysis of genomic elements by allowing a visual interpretation in the context of: (i) their genome-wide distribution, (ii) provided experimental data and (iii) the local orientation and location with respect to neighboring genes. CiVi thus enables both experts and non-experts to conveniently integrate public genome data with the results of genome analyses in circular genome maps suitable for publication.

关键词： Computational Biology/methods Computer Graphics Databases, Genetic Genes, Bacterial/genetics Genes, Bacterial/genetics Genome, Bacterial Genome, Bacterial Genomics/methods Information Storage and Retrieval molecular sequence annotation/methods Regulatory sequences, Nucleic Acid/genetics Software User-Computer Interface

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cddApp: a Cytoscape app for accessing the NCBI conserved domain database

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BIOINFORMATICS 2015年第1期31卷 134-136页

作者： Morris, John H. Wu, Allan Yamashita, Roxanne A. Marchler-Bauer, Aron Ferrin, Thomas E. Univ Calif San Francisco San Francisco CA 94143 USA NIH Natl Lib Med Natl Ctr Biotechnol Informat Bethesda MD 20894 USA

Motivation: cddApp is a Cytoscape extension that supports the annotation of protein networks with information about domains and specific functional sites from the National Center for Biotechnology Information's conserved domain database (CDD). CDD information is loaded for nodes annotated with NCBI numbers or UniProt identifiers and (optionally) Protein Data Bank structures. cddApp integrates with the Cytoscape apps structureViz2 and enhancedGraphics. Together, these three apps provide powerful tools to annotate nodes with CDD domain and site information and visualize that information in both network and structural contexts.

关键词： Algorithms Bacillus Bacterial Proteins/chemistry Bacterial Proteins/metabolism Computational Biology/instrumentation Conserved sequence Databases, Protein Databases, Protein Humans Metabolic Networks and Pathways molecular sequence annotation/methods Protein Conformation Protein Interaction Mapping sequence Analysis, Protein/methods Software

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EuGene-PP: a next-generation automated annotation pipeline for prokaryotic genomes

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BIOINFORMATICS 2014年第18期30卷 2659-2661页

作者： Sallet, Erika Gouzy, Jerome Schiex, Thomas CNRS INRA UMR441 2594 F-31320 Paris France INRA Unit Math & Informat Appl Toulouse UR 875 F-31326 Castanet Tolosan France

A Summary: It is now easy and increasingly usual to produce oriented RNA-Seq data as a prokaryotic genome is being sequenced. However, this information is usually just used for expression quantification. EuGene-PP is a fully automated pipeline for structural annotation of prokaryotic genomes integrating protein similarities, statistical information and any oriented expression information (RNA-Seq or tiling arrays) through a variety of file formats to produce a qualitatively enriched annotation including coding regions but also (possibly antisense) non-coding genes and transcription start sites.

关键词： Automation Bacteria/genetics Genome, Bacterial/genetics Genome, Bacterial/genetics Genomics/methods molecular sequence annotation/methods sequence Analysis, RNA Software Transcription Initiation Site

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Integrative annotation of Variants from 1092 Humans: Application to Cancer Genomics

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SCIENCE 2013年第6154期342卷 84-+页

作者： Khurana, Ekta Fu, Yao Colonna, Vincenza Mu, Xinmeng Jasmine Kang, Hyun Min Lappalainen, Tuuli Sboner, Andrea Lochovsky, Lucas Chen, Jieming Harmanci, Arif Das, Jishnu Abyzov, Alexej Balasubramanian, Suganthi Beal, Kathryn Chakravarty, Dimple Challis, Daniel Chen, Yuan Clarke, Declan Clarke, Laura Cunningham, Fiona Evani, Uday S. Flicek, Paul Fragoza, Robert Garrison, Erik Gibbs, Richard Gumus, Zeynep H. Herrero, Javier Kitabayashi, Naoki Kong, Yong Lage, Kasper Liluashvili, Vaja Lipkin, Steven M. MacArthur, Daniel G. Marth, Gabor Muzny, Donna Pers, Tune H. Ritchie, Graham R. S. Rosenfeld, Jeffrey A. Sisu, Cristina Wei, Xiaomu Wilson, Michael Xue, Yali Yu, Fuli Dermitzakis, Emmanouil T. Yu, Haiyuan Rubin, Mark A. Tyler-Smith, Chris Gerstein, Mark Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Wellcome Trust Sanger Inst Wellcome Trust Genome Campus Cambridge CB10 1SA England CNR Inst Genet & Biophys I-80131 Naples Italy Univ Michigan Ctr Stat Genet Biostat Ann Arbor MI 48109 USA Univ Geneva Med Sch Dept Genet Med & Dev CH-1211 Geneva Switzerland Univ Geneva iGE3 CH-1211 Geneva Switzerland Swiss Inst Bioinformat CH-1211 Geneva Switzerland Weill Cornell Med Coll Inst Precis Med New York NY 10065 USA Weill Cornell Med Coll Dept Pathol & Lab Med New York NY 10065 USA New York Presbyterian Hosp New York NY 10065 USA Weill Cornell Med Coll HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsau New York NY 10021 USA Yale Univ Integrated Grad Program Phys & Engn Biol New Haven CT 06520 USA Cornell Univ Dept Biol Stat & Computat Biol Ithaca NY 14853 USA Cornell Univ Weill Inst Cell & Mol Biol Ithaca NY 14853 USA European Bioinformat Inst European Mol Biol Lab Wellcome Trust Genome Campus Cambridge CB10 1SD England Baylor Coll Med Human Genome Sequencing Ctr Houston TX 77030 USA Yale Univ Dept Chem New Haven CT 06520 USA Cornell Univ Dept Mol Biol & Genet Ithaca NY 14853 USA Boston Coll Dept Biol Chestnut Hill MA 02467 USA Weill Cornell Med Coll Dept Physiol & Biophys New York NY 10065 USA Yale Univ Keck Biotechnol Resource Lab New Haven CT 06511 USA Massachusetts Gen Hosp MassGen Hosp Children Pediat Surg Res Labs Boston MA USA Massachusetts Gen Hosp Analyt & Translat Genet Unit Boston MA 02114 USA Harvard Med Sch Boston MA USA Tech Univ Denmark Dept Syst Biol Ctr Biol Sequence Anal Lyngby Denmark Univ Copenhagen Ctr Prot Res Copenhagen Denmark Weill Cornell Med Coll Dept Med New York NY 10065 USA Broad Inst Harvard & MIT Program Med & Populat Genet Cambridge MA 02142 USA Childrens Hosp Div Endocrinol Boston MA USA Childrens Hosp Ctr Basic & Translat Obes Res Boston MA USA Broad Inst MIT & Harvar

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants;then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e. g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to similar to 90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

关键词： Binding Sites/genetics Genetic Variation Genome, Human Genomics Humans Kruppel-Like Transcription Factors/metabolism molecular sequence annotation/methods Mutation Neoplasms/genetics Polymorphism, Single Nucleotide Population/genetics RNA, Untranslated/genetics Selection, Genetic

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Discovery of new enzymes and metabolic pathways by using structure and genome context

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NATURE 2013年第7473期502卷 698-+页

作者： Zhao, Suwen Kumar, Ritesh Sakai, Ayano Vetting, Matthew W. Wood, B. McKay Brown, Shoshana Bonanno, Jeffery B. Hillerich, Brandan S. Seidel, Ronald D. Babbitt, Patricia C. Almo, Steven C. Sweedler, Jonathan V. Gerlt, John A. Cronan, John E. Jacobson, Matthew P. Univ Calif San Francisco Dept Pharmaceut Chem San Francisco CA 94143 USA Univ Illinois Inst Genom Biol Urbana IL 61801 USA Albert Einstein Coll Med Dept Biochem Bronx NY 10461 USA Univ Calif San Francisco Dept Bioengn & Therapeut Sci San Francisco CA 94143 USA Univ Illinois Dept Chem Urbana IL 61801 USA Univ Illinois Dept Biochem Urbana IL 61801 USA Univ Illinois Dept Microbiol Urbana IL 61801 USA

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns(1). We and others(2) are developing computation-guided strategies for functional discovery with 'metabolite docking' to experimentally derived(3) or homology-based(4) three-dimensional structures. Bacterial metabolic pathways often are encoded by 'genome neighbourhoods' (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by 'predicting' the intermediates in the glycolytic pathway in Escherichia coli(5). Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses;the intermediates were identified by metabolomics;and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

关键词： Bacteria/enzymology Bacteria/genetics Bacteria/metabolism Bacterial Proteins/chemistry Bacterial Proteins/genetics Bacterial Proteins/metabolism Enzymes/chemistry Enzymes/genetics Enzymes/metabolism Gene Expression Profiling Genes, Bacterial/genetics Genes, Bacterial/genetics Genome, Bacterial/genetics Genome, Bacterial/genetics Glycolysis Kinetics Metabolic Networks and Pathways/genetics Metabolism Metabolomics Models, molecular molecular sequence annotation/methods Multigene Family/genetics Operon Operon Structural Homology, Protein Substrate Specificity

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Detecting and visualizing gene fusions

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methods 2013年第1期59卷 S24-S28页

作者： Supper, Jochen Gugenmus, Claudia Wollnik, Johannes Drueke, Tanja Scherf, Matthias Hahn, Alexander Grote, Korbinian Bretschneider, Nancy Klocke, Bernward Zinser, Christian Cartharius, Kerstin Seifert, Martin Genomatix Software GmbH D-80335 Munich Germany

In recent years, gene fusions have gained significant recognition as biomarkers. They can assist treatment decisions, are seldom found in normal tissue and are detectable through Next-generation sequencing (NGS) of the transcriptome (RNA-seq). To transform the data provided by the sequencer into robust gene fusion detection several analysis steps are needed. Usually the first step is to map the sequenced transcript fragments (RNA-seq) to a reference genome. One standard application of this approach is to estimate expression and detect variants within known genes, e.g. SNPs and indels. In case of gene fusions, however, completely novel gene structures have to be detected. Here, we describe the detection of such gene fusion events based on our comprehensive transcript annotation (ElDorado). To demonstrate the utility of our approach, we extract gene fusion candidates from eight breast cancer cell lines, which we compare to experimentally verified gene fusions. We discuss several gene fusion events, like BCAS3-BCAS4 that was only detected in the breast cancer cell line MCF7. As supporting evidence we show that gene fusions occur more frequently in copy number enriched regions (CNV analysis). In addition, we present the Transcriptome Viewer (TViewer) a tool that allows to interactively visualize gene fusions. Finally, we support detected gene fusions through literature mining based annotations and network analyses. In conclusion, we present a platform that allows detecting gene fusions and supporting them through literature knowledge as well as rich visualization capabilities. This enables scientists to better understand molecular processes, biological functions and disease associations, which will ultimately lead to better biomedical knowledge for the development of biomarkers for diagnostics and therapies. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Cell Line, Tumor Chromosome Mapping/methods DNA Copy Number Variations Gene Expression Profiling High-Throughput Nucleotide Sequencing Humans molecular sequence annotation/methods Oncogene Proteins, Fusion/genetics sequence Analysis, DNA Tumor Markers, Biological/genetics

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