DOCSLIB.ORG

Dcgo: Database of Domain-Centric Ontologies on Functions, Phenotypes, Diseases and More Hai Fang* and Julian Gough*

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. At the time of writing the ontologies in domain is the functional unit responsible for it. The at University Library on November 19, 2012 dcGO include the Gene Ontology (GO); Enzyme method has been formulated in a general way, enabling Commission (EC) numbers; pathways from it to be applied to numerous ontologies. The dcGO UniPathway; human phenotype ontology and database now contains a panel of ontologies from a phenotype ontologies from five model organisms, variety of contexts: functions such as GO (6,7), enzymes including plants; anatomy ontologies from three or- (8), pathways (9) and keywords used by UniProt (10); ganisms; human disease ontology and drugs from phenotype and anatomy ontologies across major model organisms, including mouse (11), worm (12), yeast (13), DrugBank. All ontological terms have probabilistic fly (14), zebrafish (15), Xenopus (16) and Arabidopsis (17); scores for their associations. In addition to associ- human phenotypes (18), diseases (19) and drugs (20). In ations to domains and supra-domains, the onto- addition to complete sets of ontological terms, a collapsed logical terms have been transferred to proteins, subset (slim version) is also provided for each ontology. through homology, providing annotations of >80 The automatically generated slim version of each ontology million sequences covering 2414 complete is based on annotation frequency (4), and it provides the genomes, hundreds of meta-genomes, thousands user with a manageable and more coarse-grained list. This of viruses and so forth. The dcGO database is is analogous to the ‘GO slim’ provided by the Gene updated fortnightly, and its website provides down- Ontology consortium, which has proven useful for enrich- loads, search, browse, phylogenetic context and ment analyses (21). other data-mining facilities. The domain definitions used in dcGO are taken from the structural classification of proteins (SCOP) (22) clas- sified at both the superfamily and family levels. SCOP groups domains at the superfamily level if there is struc- INTRODUCTION ture, sequence and function evidence for a common evo- Scientists are increasingly confronted with the grand chal- lutionary ancestor. Some superfamilies are sub-divided lenge: how to convert sequenced genome information into into families, which often share a higher sequence *To whom correspondence should be addressed. Tel: +44 792 9104281; Fax: +44 117 9545208; Email: [email protected] Correspondence may also be addressed to Julian Gough. Tel: +44 117 3315221; Fax: +44 117 9545208; Email: [email protected] ß The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]. 2 Nucleic Acids Research, 2012 similarity and a related function. In addition to individual matrix, we then use Fisher’s exact test to infer associations domains at these two different levels, dcGO also offers between the rows and columns. On top of this, we take annotations for combinations of domains. We use the advantage of the true path rule of the directed acyclic concept of supra-domains to describe combinations of graph of the GO to determine the optimal level at which two or more successive domains of known structure. In to make an association. We achieve this by comparing the addition to providing ontology for SCOP domains, the significance of each term using two different backgrounds, generality of the method has enabled us to also include one background using all analysable UniProt proteins Pfam (23) domains in dcGO. (being annotatable by the GO), and one background Our domain-centric ontology derived from proteins using only those UniProt proteins annotated to direct with experimental evidence can be turned and used as parents of the term. If a GO term and its parent term a predictor on proteins of unknown function but where are both significantly associated with a domain/ the domain content is known. The ‘dcGO Predictor’ supra-domain using the first background, and if the term provides pre-computed functional annotation [using is not significantly different from the parent term using the SUPERFAMILY hidden Markov models (24)] of all se- second background, then it is desirable to only associate quences in UniProt (25), 2414 completely sequenced the parent term. As a result of these dual constraints, only genomes, thousands of viral genomes and hundreds of the most significant GO term associations to domains/ Downloaded from meta-genomes. The dcGO website also has a facility for supra-domains will be retained. the user to submit their own sequences for function pre- The significance of association is assessed by the method diction. The dcGO Predictor took part in the recent of false discovery rate (FDR) to account for multiple Critical Assessment of Functional Annotation experiment hypothesis tests, whereas the strength of association is (http://biofunctionprediction.org); hence, for comparison measured by a hypergeometric distribution-based score. with other non-domain-centric predictors, we refer the For a domain/supra-domain, the associated GO terms http://nar.oxfordjournals.org/ reader to this independent evaluation of its performance. (i.e. direct annotations) are propagated to all ancestor A key result from the experiment, however, was that terms (i.e. inherited annotations); together they constitute dcGO performs significantly better than the most a complete GO annotation profile. Based on the informa- commonly used method for GO annotation, Basic Local tion content of a GO term (i.e. negative logarithmic trans- Alignment Search Tool searches against UniProt (25). formation of frequency of domains/supra-domains In the main body of the article later we describe the annotated to that term), a search procedure is applied to database contents in detail, doing so separately for GO partition the directed acyclic graph structure of the GO, and for other biomedical ontologies (collectively denoted each partition reflecting the same or similar specificity but at University Library on November 19, 2012 as ‘BO’ hereinafter). Then, we provide an overview of located in distinct paths. With four seeds of increasing various utilities available through the website that may information content, the procedure produces the ‘GO interest users. Finally, we conclude with planned future slim’ that contains GO terms classified into four levels of developments. increasing granularity. These are highly general, general, specific and highly specific (Supplementary Table S1). The use of information content (as a measure of how specific DATABASE CONTENTS and informative a term is) adds great value to the existing Algorithm summary GO hierarchy for the user. The GO was created for annotating proteins, so some parts of GO structure are To fully understand the content of the dcGO database less valid for annotating domains/supra-domains than (Table 1), it is necessary to give a description of the algo- others. Rather than merely relying on the ontology rithm that is used to build it. Without loss of generality, graph depth to define the term specificity, our approach we take the GO as an exclusive example, but in principle, has taken into account actual usage of terms when the applications
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]
  • 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]
  • 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.
    [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]