DOCSLIB.ORG

GI-Edition Proceedings

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
GI-Edition Proceedings Gesellschaft für Informatik e.V. (GI) GI-Edition publishes this series in order to make available to a broad public recent findings in informatics (i.e. computer science and informa- Lecture Notes tion systems), to document conferences that are organized in co- operation with GI and to publish the annual GI Award dissertation. in Informatics Broken down into • seminar • proceedings Dietmar Schomburg, • dissertations • thematics Andreas Grote (Eds.) current topics are dealt with from the vantage point of research and development, teaching and further training in theory and prac- tice. The Editorial Committee uses an intensive review process in order to ensure high quality contributions. German Conference on The volumes are published in German or English. Bioinformatics 2010 Information: http://www.gi-ev.de/service/publikationen/lni/ September 20 - 22, 2010 D. Schomburg, A. Grote (Eds.): GCB 2010 Grote (Eds.): A. Schomburg, D. Braunschweig, Germany ISSN 1617-5468 ISBN 978-3-88579-267-3 This volume contains papers presented at the 25th German Conference on Bioin- formatics held at the Technische Universität Carolo-Wilhelmina in Braunschweig, Germany, September 20-22, 2010. The German Conference on Bioinformatics is 173 an annual, international conference, which provides a forum for the presentation Proceedings of current research in bioinformatics and computational biology. It is organized on behalf of the Special Interest Group on Informatics in Biology of the German Society of Computer Science (GI) and the German Society of Chemical Technique and Biotechnology (Dechema) in cooperation with the German Society for Bio- chemistry and Molecular Biology (GBM). Dietmar Schomburg, Andreas Grote (Editors) German Conference on Bioinformatics 2010 September 20-22, 2010 Technische Universität Carolo Wilhelmina zu Braunschweig, Germany Gesellschaft für Informatik e.V. (GI) Lecture Notes in Informatics (LNI) - Proceedings Series of the Gesellschaft für Informatik (GI) Volume P-173 ISBN 978-3-88579-267-3 ISSN 1617-5468 Volume Editors Prof. Dr. Dietmar Schomburg Technische Universität Carolo-Wilhelmina zu Braunschweig Email: [email protected] Dr. Andreas Grote Technische Universität Carolo-Wilhelmina zu Braunschweig Email: [email protected] Series Editorial Board Heinrich C. Mayr, Universität Klagenfurt, Austria (Chairman, [email protected]) Hinrich Bonin, Leuphana-Universität Lüneburg, Germany Dieter Fellner, Technische Universität Darmstadt, Germany Ulrich Flegel, SAP Research, Germany Ulrich Frank, Universität Duisburg-Essen, Germany Johann-Christoph Freytag, Humboldt-Universität Berlin, Germany Thomas Roth-Berghofer, DFKI Michael Goedicke, Universität Duisburg-Essen Ralf Hofestädt, Universität Bielefeld Michael Koch, Universität der Bundeswehr, München, Germany Axel Lehmann, Universität der Bundeswehr München, Germany Ernst W. Mayr, Technische Universität München, Germany Sigrid Schubert, Universität Siegen, Germany Martin Warnke, Leuphana-Universität Lüneburg, Germany Dissertations Dorothea Wagner, Universität Karlsruhe, Germany Seminars Reinhard Wilhelm, Universität des Saarlandes, Germany Thematics Andreas Oberweis, Universität Karlsruhe (TH) Gesellschaft für Informatik, Bonn 2010 printed by Köllen Druck+Verlag GmbH, Bonn Preface This volume contains papers presented at the 25th German Conference on Bioinformat- ics held at the Technische Universit¨atCarolo-Wilhelmina in Braunschweig, Germany, September20-22, 2010. The German Conference on Bioinformatics is an annual, in- ternational conference, whichprovides aforum for the presentation of currentresearch in bioinformatics and computational biology.Itisorganized on behalf of the Special Interest Group on Informatics in Biology of the German SocietyofComputer Science (GI) and the German SocietyofChemical Technique and Biotechnology (Dechema) in cooperation with the German Societyfor Biochemistry and Molecular Biology (GBM). Fiveoutstanding scientists were invited to givekeynote lectures to the conference: • Edda Klipp -‘Cellular stress response and regulation of metabolism’ • Thomas Lengauer -‘HIV Bioinformatics: Analyzing viral evolution for the ben- efit of AIDS patients’ • Werner Mewes -‘The data deluge: can simple models explain complex biological systems?’ • Stefan Schuster -‘Road games in metabolism -Abiotechnological perspective’ • Gregory Stephanopoulos -‘After adecade of systems biology,time for arecord card’ Besides the keynotelectures, the scientific program comprised 22 contributed talks presenting 12 regular and 10 short papers. All accepted regular papers are collected in these proceedings. The remaining accepted contributions, i.e. short papers and poster abstracts, are published in aseparate volume. We would liketothank the pro- gram committee members and all reviewers for their evaluations of the contributions. Furthermore, we would like to thank the local organizers for keeping the conference running. The organizers are grateful to all the sponsors and supporting scientific part- ners. Last but not least, we would liketothank all contributors and participants of the GCB 2010. Braunschweig, August 2010 Dietmar Schomburg and Andreas Grote 5 Organizers Conference Chair Dietmar Schomburg, Braunschweig Local Organizers Wolf-Tilo Balke(TU Braunschweig) Andreas Grote (TU Braunschweig) S´andor Fekete (TU Braunschweig) Katharina Hanke(TU Braun- Reinhold Haux (TU Braunschweig) schweig) Dieter Jahn (TU Braunschweig) Adam Podstawka(TU Braun- Frank Klawonn (Ostfalia University schweig) of Applied Sciences) Alexander Riemer (TU Braun- Constantin Bannert (TU Braun- schweig) schweig) Maurice Scheer (TU Braunschweig) Antje Chang (TU Braunschweig) Programm committee Mario Albrecht,Saarbr¨u cken Michael Marschollek, Hannover Wolf-Tilo Balke, Braunschweig Werner Mewes, M¨unchen Tim Beißbarth, G¨ottingen Michael Meyer-Hermann Frankfurt Thomas Dandekar, W¨urzburg Burkhard Morgenstern, G¨ottingen S´andor Fekete, Braunschweig Stefan Posch, Halle Georg F¨ullen, Rostock Matthias Rarey,Hamburg Robert Giegerich, Bielefeld Falk Schreiber, Gatersleben Reinhold Haux, Braunschweig Stefan Schuster, Jena Ralf Hofest¨adt,Bielefeld Gregory Stephanopoulos, Cam- Matthias Heinemann, Z¨urich bridge USA Dieter Jahn, Braunschweig Jens Stoye,Bielefeld Frank Klawonn, Wolfenb¨uttel Andrew Torda, Hamburg Edda Klipp, Berlin Martin Vingron, Berlin Ina Koch,Berlin Christian vonMering, Z¨urich Oliver Kohlbacher, T¨ubingen Edgar Wingender, G¨ottingen Thomas Lengauer, Saarbr¨u cken Andreas Ziegler, L¨ubeck Hans-Peter Lenhof, Saarbr¨u cken 6 Sponsors and supporters Supporting scientific societies Gesellschaft f¨ur Chemische Technik und Biotechnologie e.V. (DECHEMA) http://www.dechema.de Gesellschaft f¨ur Biochemie und Molekularbiologie e.V. (GBM) http://www.gbm-online.de Gesellschaft f¨ur Informatik e.V. (GI) http://www.gi-ev.de Non-profit sponsors Technische Universit¨atBraunschweig http://www.tu-braunschweig.de Commercial sponsors Biobase -Biological Databases http://www.biobase-international.com CLC bio http://www.clcbio.com ConveyComputer http://www.conveycomputer.com genomatix http://www.genomatix.de 7 geneXplain http://www.genexplain.com MEGWARE Computer Cluster http://www.megware.com Thalia http://www.thalia.de Transtec -IT&Solutions http://www.transtec.de 8 Table of Contents Preface 5 Organizers 6 Sponsors and supporters 7 Table of Contents 9 Submissions 11 Andreas R. Gruber, Stephan H. Bernhart, YouZhou, IvoL.Ho- facker RNALfoldz: efficientprediction of thermodynamically stable, local secondary structures ...................................... 11 Florian Battke, Stephan K¨orner,Steffen H¨uttner, KayNieselt Efficientsequence clustering for RNA-seq data without areference genome .21 Florian Bl¨ochl, Maria L. Hartsperger, Volker St¨u mpflen, Fabian J. Theis Uncoveringthe structure of heterogenous biological data: fuzzy graph parti- tioning in thek-partite setting .......................... 31 Sergiy Bogomolov, Martin Mann, Bj¨orn Voß, Andreas Podelski, Rolf Backofen Shape-based barrier estimation for RNAs .................... 41 Thomas Fober, Marco Mernberger, Gerhard Klebe, EykeH¨u llermeier EfficientSimilarityRetrieval of Protein Binding Sites based on Histogram Comparison ..................................... 51 Peter Husemann, Jens Stoye Repeat-aware ComparativeGenome Assembly .................. 61 Katrin Bohl, Lu´ı sF.deFigueiredo, Oliver H¨adicke,Steffen Klamt, Christian Kost, Stefan Schuster, Christoph Kaleta CASOP GS: Computing intervention strategies targeted at production im- provementingenome-scale metabolic networks ................. 71 Jan Grau, Daniel Arend, IvoGrosse, Artemis G. Hatzigeorgiou, Jens Keilwagen, Manolis Maragkakis, Claus Weinholdt, Stefan Posch Predicting miRNA targets utilizing an extended profile HMM ......... 81 9 Arli A. Parikesit, Peter F. Stadler, Sonja J. Prohaska QuantitativeComparison of Genomic-Wide Protein Domain Distributions .. 93 Jan Budczies, Carsten Denkert, Berit M. M¨uller, Scarlet F. Brockm¨oller, Manfred Dietel, Jules L. Griffin, Matej Oresic, Oliver Fiehn METAtarget –extracting keyenzymes of metabolic regulation from high- throughput metabolomics data using KEGG REACTION information ....103 Andreas Gogol-D¨oring, WeiChen Finding Optimal Sets of Enriched Regions in ChIP-Seq Data .........113 Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasnogor Learning pathway-based decision rules to classify microarraycancer samples .123 Index of authors 135 10 Gruber et al. 11 RNALfoldz: efficient prediction of thermodynamically stable, local secondary structures Andreas R.
Load more

Recommended publications
  • The SUPERFAMILY 2.0 Database: a Significant Proteome Update and a New Webserver Arun Prasad Pandurangan 1,*, Jonathan Stahlhacke2, Matt E
    D490–D494 Nucleic Acids Research, 2019, Vol. 47, Database issue Published online 16 November 2018 doi: 10.1093/nar/gky1130 The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver Arun Prasad Pandurangan 1,*, Jonathan Stahlhacke2, Matt E. Oates2, Ben Smithers 2 and Julian Gough1 1MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK and 2Computer Science, University of Bristol, Bristol BS8 1UB, UK Received September 24, 2018; Revised October 23, 2018; Editorial Decision October 23, 2018; Accepted October 25, 2018 ABSTRACT level, most homologous proteins cluster together with high sequence similarity suggesting clear evolutionary relation- Here, we present a major update to the SUPERFAM- ship and functional consistency (3). The SUPERFAMILY ILY database and the webserver. We describe the ad- database provides domain annotations at both Superfamily dition of new SUPERFAMILY 2.0 profile HMM library and Family levels (4). containing a total of 27 623 HMMs. The database SUPERFAMILYprovides various analysis tools to facil- now includes Superfamily domain annotations for itate better analysis and interpretation of the database con- millions of protein sequences taken from the Uni- tent. They include the identification of under- and overrep- versal Protein Recourse Knowledgebase (UniPro- resentation of domains between genomes (5), construction tKB) and the National Center for Biotechnology In- of phylogenetic trees (6), analysis of the domain distribution formation (NCBI). This addition constitutes about 51 of superfamilies and families across the tree of life (7)aswell and 45 million distinct protein sequences obtained as providing ontology based annotations for SUPERFAM- ILY domains and architectures (8,9).
    [Show full text]
  • Tum1 Is Involved in the Metabolism of Sterol Esters in Saccharomyces Cerevisiae Katja Uršič1,4, Mojca Ogrizović1,Dušan Kordiš1, Klaus Natter2 and Uroš Petrovič1,3*
    Uršič et al. BMC Microbiology (2017) 17:181 DOI 10.1186/s12866-017-1088-1 RESEARCHARTICLE Open Access Tum1 is involved in the metabolism of sterol esters in Saccharomyces cerevisiae Katja Uršič1,4, Mojca Ogrizović1,Dušan Kordiš1, Klaus Natter2 and Uroš Petrovič1,3* Abstract Background: The only hitherto known biological role of yeast Saccharomyces cerevisiae Tum1 protein is in the tRNA thiolation pathway. The mammalian homologue of the yeast TUM1 gene, the thiosulfate sulfurtransferase (a.k.a. rhodanese) Tst, has been proposed as an obesity-resistance and antidiabetic gene. To assess the role of Tum1 in cell metabolism and the putative functional connection between lipid metabolism and tRNA modification, we analysed evolutionary conservation of the rhodanese protein superfamily, investigated the role of Tum1 in lipid metabolism, and examined the phenotype of yeast strains expressing the mouse homologue of Tum1, TST. Results: We analysed evolutionary relationships in the rhodanese superfamily and established that its members are widespread in bacteria, archaea and in all major eukaryotic groups. We found that the amount of sterol esters was significantly higher in the deletion strain tum1Δ than in the wild-type strain. Expression of the mouse TST protein in the deletion strain did not rescue this phenotype. Moreover, although Tum1 deficiency in the thiolation pathway was complemented by re-introducing TUM1, it was not complemented by the introduction of the mouse homologue Tst. We further showed that the tRNA thiolation pathway is not involved in the regulation of sterol ester content in S. cerevisiae,asoverexpressionofthetEUUC,tKUUU and tQUUG tRNAs did not rescue the lipid phenotype in the tum1Δ deletion strain, and, additionally, deletion of the key gene for the tRNA thiolation pathway, UBA4, did not affect sterol ester content.
    [Show full text]
  • Smurflite: Combining Simplified Markov Random Fields With
    SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone Noah M. Daniels 1, Raghavendra Hosur 2, Bonnie Berger 2∗, and Lenore J. Cowen 1∗ 1Department of Computer Science, Tufts University, Medford, MA 02155 2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 ABSTRACT are limited in their power to recognize remote homologs because of Motivation: One of the most successful methods to date for their inability to model statistical dependencies between amino-acid recognizing protein sequences that are evolutionarily related has residues that are close in space but far apart in sequence (Lifson and been profile Hidden Markov Models (HMMs). However, these models Sander (1980); Zhu and Braun (1999); Olmea et al. (1999); Cowen do not capture pairwise statistical preferences of residues that are et al. (2002); Steward and Thorton (2002)). hydrogen bonded in beta sheets. These dependencies have been For this reason, many have suggested (White et al. (1994); partially captured in the HMM setting by simulated evolution in the Lathrop and Smith (1996); Thomas et al. (2008); Liu et al. (2009); training phase and can be fully captured by Markov Random Fields Menke et al. (2010); Peng and Xu (2011)) that more powerful (MRFs). However, the MRFs can be computationally prohibitive when Markov Random Fields (MRFs) be used. MRFs employ an auxiliary beta strands are interleaved in complex topologies. dependency graph which allows them to model more complex We introduce SMURFLite, a method that combines both simplified statistical dependencies, including statistical dependencies that Markov Random Fields and simulated evolution to substantially occur between amino-acid residues that are hydrogen bonded in beta improve remote homology detection for beta structures.
    [Show full text]
  • Renaissance SUPERFAMILY in the Decade Since Its Launch, a Repository of Information About Proteins in Genomes Has Developed Into a Primary Reference
    Renaissance SUPERFAMILY In the decade since its launch, a repository of information about proteins in genomes has developed into a primary reference. Dr Julian Gough, its creator, describes current work enhancing the scope, content and functionality of this key service Could you begin by outlining the reasons for How do HMMs feed into the service? creating SUPERFAMILY? HMMs are profi les which represent multiple SUPERFAMILY was originally created to better sequence alignments of homologous proteins understand molecular evolution, initially by in a rigorous statistical framework. They enabling comparison of the repertoire of proteins can be used to classify sequences based on and domains across the genomes of different homology and to create sequence alignments. species. The starting point was the Structural We use sequences of domains of known Classifi cation of Proteins (SCOP), and the most structure via iterative search procedures on basic purpose of SUPERFAMILY is to detect and large background sequence databases to classify these domains with known structural build alignments and, subsequently, models representatives in the protein sequences of representing domains of known structure at genomes. There are tens of thousands of known the superfamily level. These models are then protein structures, each the result of a costly searched against genome sequences to detect three-dimensional atomic resolution structure and classify the structural domains. determination by experiment, usually X-ray crystallography or nuclear magnetic resonance.
    [Show full text]
  • Specialized Hidden Markov Model Databases for Microbial Genomics
    Comparative and Functional Genomics Comp Funct Genom 2003; 4: 250–254. Published online 1 April 2003 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.280 Conference Review Specialized hidden Markov model databases for microbial genomics Martin Gollery* University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV 89557-0014, USA *Correspondence to: Abstract Martin Gollery, University of Nevada, Reno, 1664 N. Virginia As hidden Markov models (HMMs) become increasingly more important in the Street, Reno, NV analysis of biological sequences, so too have databases of HMMs expanded in size, 89557-0014, USA. number and importance. While the standard paradigm a short while ago was the E-mail: [email protected] analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databases and any number of internally developed targets. This paper looks at some of the readily available HMM (or HMM-like) algorithms and several publicly available Received: 27 January 2003 HMM databases, and outlines methods by which the reader may develop custom Revised: 5 February 2003 HMM targets. Copyright 2003 John Wiley & Sons, Ltd. Accepted: 6 February 2003 Keywords: HMM; Pfam; InterPro; SuperFamily; TLfam; COG; TIGRfams Introduction will be a true homologue. As a result, HMMs have become very popular in the field of bioinformatics Over the last few years, hidden Markov models and a number of HMM databases have been (HMMs) have become one of the pre-eminent developed.
    [Show full text]
  • Dcgo: Database of Domain-Centric Ontologies on Functions, Phenotypes, Diseases and More Hai Fang* and Julian Gough*
    Nucleic Acids Research Advance Access published November 17, 2012 Nucleic Acids Research, 2012, 1–9 doi:10.1093/nar/gks1080 dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more Hai Fang* and Julian Gough* Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK Received August 15, 2012; Revised September 28, 2012; Accepted October 16, 2012 ABSTRACT higher-order knowledge on function (1), phenotype (2) and even human disease (3). We present ‘dcGO’ (http://supfam.org/ The domain-centric Gene Ontology (dcGO) database at SUPERFAMILY/dcGO), a comprehensive ontology http://supfam.org/SUPERFAMILY/dcGO is a compre- Downloaded from database for protein domains. Domains are often hensive ontology resource that contributes to the afore- the functional units of proteins, thus instead of mentioned challenge through a new domain-centric associating ontological terms only with full-length strategy. Our method, dcGO (4), annotates protein proteins, it sometimes makes more sense to asso- domains with ontological terms. Ontologies are hierarch- ically organized controlled vocabularies/terms defined to ciate terms with individual domains. Domain-centric http://nar.oxfordjournals.org/ GO, ‘dcGO’, provides associations between onto- categorize a particular sphere of knowledge (5). For logical terms and protein domains at the superfam- example, ‘Gene Ontology’ (GO) was created to describe ily and family levels. Some functional units consist functions of proteins (6). Ontological labels are already available for full-length proteins, derived from experimen- of more than one domain acting together or acting tal data for that protein. The dcGO approach takes the at an interface between domains; therefore, onto- terms attached to full-length sequences, and combines logical terms associated with pairs of domains, them with the domain composition of the sequences on triplets and longer supra-domains are also a large scale, to statistically infer for each term which provided.
    [Show full text]
  • Interpro: the Integrative Protein Signature Database
    InterPro: the integrative protein signature database Sarah Hunter, Rolf Apweiler, Teresa K Attwood, Amos Bairoch, Alex Bateman, David Binns, Peer Bork, Ujjwal Das, Louise Daugherty, Lauranne Duquenne, et al. To cite this version: Sarah Hunter, Rolf Apweiler, Teresa K Attwood, Amos Bairoch, Alex Bateman, et al.. InterPro: the integrative protein signature database. Nucleic Acids Research, Oxford University Press, 2009, 37 (Database issue), pp.D211-D215. 10.1093/nar/gkn785. hal-01214141 HAL Id: hal-01214141 https://hal.archives-ouvertes.fr/hal-01214141 Submitted on 9 Oct 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Published online 21 October 2008 Nucleic Acids Research, 2009, Vol. 37, Database issue D211–D215 doi:10.1093/nar/gkn785 InterPro: the integrative protein signature database Sarah Hunter1,*, Rolf Apweiler1, Teresa K. Attwood2, Amos Bairoch3, Alex Bateman4, David Binns1, Peer Bork5, Ujjwal Das1, Louise Daugherty1, Lauranne Duquenne6, Robert D. Finn4, Julian Gough7, Daniel Haft8, Nicolas Hulo3, Daniel Kahn6, Elizabeth Kelly9, Aure´ lie Laugraud6, Ivica Letunic5, David Lonsdale1, Rodrigo Lopez1, Martin Madera7, John Maslen1, Craig McAnulla1, Jennifer McDowall1, Jaina Mistry4, Downloaded from Alex Mitchell1,2, Nicola Mulder9, Darren Natale10, Christine Orengo11, Antony F.
    [Show full text]
  • Article Reference
    Article InterPro, progress and status in 2005 MULDER, Nicola J, et al. Abstract InterPro, an integrated documentation resource of protein families, domains and functional sites, was created to integrate the major protein signature databases. Currently, it includes PROSITE, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF and SUPERFAMILY. Signatures are manually integrated into InterPro entries that are curated to provide biological and functional information. Annotation is provided in an abstract, Gene Ontology mapping and links to specialized databases. New features of InterPro include extended protein match views, taxonomic range information and protein 3D structure data. One of the new match views is the InterPro Domain Architecture view, which shows the domain composition of protein matches. Two new entry types were introduced to better describe InterPro entries: these are active site and binding site. PIRSF and the structure-based SUPERFAMILY are the latest member databases to join InterPro, and CATH and PANTHER are soon to be integrated. InterPro release 8.0 contains 11 007 entries, representing 2573 domains, 8166 families, 201 repeats, 26 active sites, 21 binding sites and 20 post-translational [...] Reference MULDER, Nicola J, et al. InterPro, progress and status in 2005. Nucleic Acids Research, 2005, vol. 33, no. Database issue, p. D201-5 PMID : 15608177 DOI : 10.1093/nar/gki106 Available at: http://archive-ouverte.unige.ch/unige:38247 Disclaimer: layout of this document may differ from the published version. 1 / 1 Nucleic Acids Research, 2005, Vol. 33, Database issue D201–D205 doi:10.1093/nar/gki106 InterPro, progress and status in 2005 Nicola J. Mulder1,*, Rolf Apweiler1, Teresa K.
    [Show full text]
  • Assignment of Homology to Genome
    doi:10.1006/jmbi.2001.5080availableonlineathttp://www.idealibrary.comon J. Mol. Biol. (2001) 313, 903±919 AssignmentofHomologytoGenomeSequences using a Library of Hidden Markov Models that Represent all Proteins of Known Structure JulianGough1*,KevinKarplus2,RichardHughey2andCyrusChothia1 1MRC, Laboratory of Molecular Of the sequence comparison methods, pro®le-based methods perform Biology, Hills Road, Cambridge with greater selectively than those that use pairwise comparisons. Of the CB2 2QH, UK pro®le methods, hidden Markov models (HMMs) are apparently the best. The ®rst part of this paper describes calculations that (i) improve 2Department of Computer the performance of HMMs and (ii) determine a good procedure for creat- Engineering, Jack Baskin School ing HMMs for sequences of proteins of known structure. For a family of of Engineering, University of related proteins, more homologues are detected using multiple models California, Santa Cruz built from diverse single seed sequences than from one model built from CA 95064, USA a good alignment of those sequences. A new procedure is described for detecting and correcting those errors that arise at the model-building stage of the procedure. These two improvements greatly increase selectiv- ity and coverage. The second part of the paper describes the construction of a library of HMMs, called SUPERFAMILY, that represent essentially all proteins of known structure. The sequences of the domains in proteins of known structure, that have identities less than 95 %, are used as seeds to build the models. Using the current data, this gives a library with 4894 models. The third part of the paper describes the use of the SUPERFAMILY model library to annotate the sequences of over 50 genomes.
    [Show full text]
  • Package 'Dcgor'
    Package ‘dcGOR’ July 27, 2015 Type Package Title Analysis of Ontologies and Protein Domain Annotations Version 1.0.6 Date 2015-7-26 Author Hai Fang and Julian Gough Maintainer Hai Fang <[email protected]> Depends R (>= 3.1.0), Matrix, igraph, dnet Imports methods Suggests foreach, doMC, ape Description There lacks a package for analysing domain-centric ontologies and annotations, particu- larly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a com- prehensive domain-centric database for annotating protein domains using a panel of ontolo- gies including Gene Ontology. With the package, users are expected to analyse and visualise do- main-centric ontologies and annotations. Supported analyses include but are not lim- ited to: easy access to a wide range of ontologies and their domain-centric annota- tions; able to build customised ontologies and annotations; domain-based enrichment analy- sis and visualisation; construction of a domain (semantic similarity) network according to ontol- ogy annotations; significance analysis for estimating a contact (statistical significance) net- work via Random Walk with Restart; and high-performance parallel computing. The new func- tionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input pro- tein sequences (precisely domain content in the form of architectures) plus to assess the predic- tions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony. URL http://supfam.org/dcGOR, http://cran.r-project.org/package=dcGOR,
    [Show full text]
  • HMMER Web Server Documentation Release 1.0
    HMMER web server Documentation Release 1.0 Rob Finn & Simon Potter EMBL-EBI Oct 16, 2019 Contents 1 Target databases 3 1.1 Sequence databases...........................................3 1.2 Profile HMM databases.........................................4 1.3 Search provenance............................................4 2 Searches 5 2.1 Search query...............................................5 2.2 Query examples.............................................6 2.3 Default search parameters........................................6 2.3.1 phmmer.............................................6 2.3.2 hmmscan............................................6 2.3.3 hmmsearch...........................................6 2.3.4 jackhmmer...........................................6 2.4 Databases.................................................7 2.4.1 Sequence databases.......................................7 2.4.2 HMM databases.........................................7 2.5 Thresholds................................................7 2.5.1 Significance thresholds.....................................7 2.5.2 Reporting thresholds......................................8 2.5.3 Gathering thresholds......................................9 2.5.4 Gene3D and Superfamily thresholds..............................9 3 Advanced search options 11 3.1 Taxonomy Restrictions.......................................... 11 3.1.1 Search.............................................. 11 3.1.2 Pre-defined Taxonomic Tree.................................. 11 3.2 Customisation of results........................................
    [Show full text]
  • Sequences and Topology: Disorder, Modularity, and Post/Pre Translation Modification
    COSTBI-1118; NO. OF PAGES 3 Available online at www.sciencedirect.com Sequences and topology: disorder, modularity, and post/pre translation modification Editorial overview Julian Gough and A Keith Dunker Current Opinion in Structural Biology 2013, 23:xx– yy This review comes from a themed issue on Se- quences and topology 0959-440X/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.sbi.2013.05.001 Julian Gough In this collection of reviews we bring together discussions of intrinsically Department of Computer Science, University disordered proteins and their functional mechanisms operating at different of Bristol, Merchant Venturers Building, loci across the ordered-to-disordered spectrum. Our previous editorial [1] for Woodland Road, Bristol BS8 1UB, UK sequences and topology in 2011 contrasts and compares structured and e-mail: [email protected] disordered proteins but viewed in light of their evolution. Here we move forward, focusing on function across the spectrum of disorder, but still within Julian Gough began his research career in structural bioinformatics, but soon combined the context of evolution. Disordered regions can also participate as modules this with genomics and sequence analysis to in multi-domain proteins; the reviews herein cover both structured [2] and study evolution, also producing the disordered modularity [3]. A growing cohort of researchers suggest that we SUPERFAMILY database of structural need to improve the connections between structure, disorder and their domain assignments to genomes. Gough functions, and several reviews present computational and experimental continues to examine molecular evolution methodologies for doing so. A running theme is the importance to function across all species and applies bioinformatics at multiple levels; most recently to intrinsic of modifications, both post and pre-translation.
    [Show full text]