1
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Karp PD, Paley S, Caspi R, Kothari A, Krummenacker M, Midford PE, Moore LR, Subhraveti P, Gama-Castro S, Tierrafria VH, Lara P, Muñiz-Rascado L, Bonavides-Martinez C, Santos-Zavaleta A, Mackie A, Sun G, Ahn-Horst TA, Choi H, Covert MW, Collado-Vides J, Paulsen I. The EcoCyc Database (2023). EcoSal Plus 2023; 11:eesp00022023. [PMID: 37220074 PMCID: PMC10729931 DOI: 10.1128/ecosalplus.esp-0002-2023] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/04/2023] [Indexed: 01/28/2024]
Abstract
EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.
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Affiliation(s)
- Peter D. Karp
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Suzanne Paley
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Markus Krummenacker
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Peter E. Midford
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Lisa R. Moore
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Pallavi Subhraveti
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Socorro Gama-Castro
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Victor H. Tierrafria
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Paloma Lara
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Luis Muñiz-Rascado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - César Bonavides-Martinez
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Alberto Santos-Zavaleta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Amanda Mackie
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Travis A. Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Heejo Choi
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Markus W. Covert
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Ian Paulsen
- School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia
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2
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Forés-Martos J, Forte A, García-Martínez J, Pérez-Ortín JE. A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them. Cells 2021; 10:334. [PMID: 33562654 PMCID: PMC7914595 DOI: 10.3390/cells10020334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 12/01/2022] Open
Abstract
The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.
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Affiliation(s)
- Jaume Forés-Martos
- Instituto de Biotecnología y Biomedicina (Biotecmed), Universitat de València, C/Dr. Moliner 50, E46100 Burjassot, Spain;
| | - Anabel Forte
- Departamento de Estadística e Investigación Operativa, Facultad de Matemáticas, Universitat de València, C/Dr. Moliner 50, E46100 Burjassot, Spain;
| | - José García-Martínez
- Instituto de Biotecnología y Biomedicina (Biotecmed), Universitat de València, C/Dr. Moliner 50, E46100 Burjassot, Spain;
| | - José E. Pérez-Ortín
- Instituto de Biotecnología y Biomedicina (Biotecmed), Universitat de València, C/Dr. Moliner 50, E46100 Burjassot, Spain;
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3
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Karp PD, Ong WK, Paley S, Billington R, Caspi R, Fulcher C, Kothari A, Krummenacker M, Latendresse M, Midford PE, Subhraveti P, Gama-Castro S, Muñiz-Rascado L, Bonavides-Martinez C, Santos-Zavaleta A, Mackie A, Collado-Vides J, Keseler IM, Paulsen I. The EcoCyc Database. EcoSal Plus 2018; 8:10.1128/ecosalplus.ESP-0006-2018. [PMID: 30406744 PMCID: PMC6504970 DOI: 10.1128/ecosalplus.esp-0006-2018] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Indexed: 01/28/2023]
Abstract
EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Wai Kit Ong
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Suzanne Paley
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Carol Fulcher
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Mario Latendresse
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Peter E Midford
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | - Socorro Gama-Castro
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Luis Muñiz-Rascado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - César Bonavides-Martinez
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Alberto Santos-Zavaleta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Amanda Mackie
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Ingrid M Keseler
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Ian Paulsen
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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4
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Karp PD, Weaver D, Paley S, Fulcher C, Kubo A, Kothari A, Krummenacker M, Subhraveti P, Weerasinghe D, Gama-Castro S, Huerta AM, Muñiz-Rascado L, Bonavides-Martinez C, Weiss V, Peralta-Gil M, Santos-Zavaleta A, Schröder I, Mackie A, Gunsalus R, Collado-Vides J, Keseler IM, Paulsen I. The EcoCyc Database. EcoSal Plus 2014; 6:10.1128/ecosalplus.ESP-0009-2013. [PMID: 26442933 PMCID: PMC4243172 DOI: 10.1128/ecosalplus.esp-0009-2013] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Indexed: 11/20/2022]
Abstract
EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review provides a detailed description of the data content of EcoCyc and of the procedures by which this content is generated.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Daniel Weaver
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Suzanne Paley
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Carol Fulcher
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Aya Kubo
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | | | | | | | - Socorro Gama-Castro
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Araceli M Huerta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Luis Muñiz-Rascado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - César Bonavides-Martinez
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Verena Weiss
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Martin Peralta-Gil
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Alberto Santos-Zavaleta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Imke Schröder
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095
- UCLA Institute of Genomics and Proteomics, University of California, Los Angeles, CA 90095
| | - Amanda Mackie
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Robert Gunsalus
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, A.P. 565-A, Cuernavaca, Morelos 62100, México
| | - Ingrid M Keseler
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025
| | - Ian Paulsen
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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5
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Hu JC, Sherlock G, Siegele DA, Aleksander SA, Ball CA, Demeter J, Gouni S, Holland TA, Karp PD, Lewis JE, Liles NM, McIntosh BK, Mi H, Muruganujan A, Wymore F, Thomas PD, Altman T. PortEco: a resource for exploring bacterial biology through high-throughput data and analysis tools. Nucleic Acids Res 2013; 42:D677-84. [PMID: 24285306 PMCID: PMC3965092 DOI: 10.1093/nar/gkt1203] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
PortEco (http://porteco.org) aims to collect, curate and provide data and analysis tools to support basic biological research in Escherichia coli (and eventually other bacterial systems). PortEco is implemented as a ‘virtual’ model organism database that provides a single unified interface to the user, while integrating information from a variety of sources. The main focus of PortEco is to enable broad use of the growing number of high-throughput experiments available for E. coli, and to leverage community annotation through the EcoliWiki and GONUTS systems. Currently, PortEco includes curated data from hundreds of genome-wide RNA expression studies, from high-throughput phenotyping of single-gene knockouts under hundreds of annotated conditions, from chromatin immunoprecipitation experiments for tens of different DNA-binding factors and from ribosome profiling experiments that yield insights into protein expression. Conditions have been annotated with a consistent vocabulary, and data have been consistently normalized to enable users to find, compare and interpret relevant experiments. PortEco includes tools for data analysis, including clustering, enrichment analysis and exploration via genome browsers. PortEco search and data analysis tools are extensively linked to the curated gene, metabolic pathway and regulation content at its sister site, EcoCyc.
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Affiliation(s)
- James C Hu
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA, Department of Genetics, Stanford University, Stanford, CA 94305, USA, Department of Biology, Texas A&M University, College Station, TX, 77843, USA, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA and Deptartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA
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6
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Deb B. An ontological analysis of some biological ontologies. Front Genet 2012; 3:269. [PMID: 23226158 PMCID: PMC3509948 DOI: 10.3389/fgene.2012.00269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2012] [Accepted: 11/06/2012] [Indexed: 11/28/2022] Open
Affiliation(s)
- Briti Deb
- Bioinformatics Center, IISc Bangalore, India
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7
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Abstract
EcoGene (http://ecogene.org) is a database and website devoted to continuously improving the structural and functional annotation of Escherichia coli K-12, one of the most well understood model organisms, represented by the MG1655(Seq) genome sequence and annotations. Major improvements to EcoGene in the past decade include (i) graphic presentations of genome map features; (ii) ability to design Boolean queries and Venn diagrams from EcoArray, EcoTopics or user-provided GeneSets; (iii) the genome-wide clone and deletion primer design tool, PrimerPairs; (iv) sequence searches using a customized EcoBLAST; (v) a Cross Reference table of synonymous gene and protein identifiers; (vi) proteome-wide indexing with GO terms; (vii) EcoTools access to >2000 complete bacterial genomes in EcoGene-RefSeq; (viii) establishment of a MySql relational database; and (ix) use of web content management systems. The biomedical literature is surveyed daily to provide citation and gene function updates. As of September 2012, the review of 37 397 abstracts and articles led to creation of 98 425 PubMed-Gene links and 5415 PubMed-Topic links. Annotation updates to Genbank U00096 are transmitted from EcoGene to NCBI. Experimental verifications include confirmation of a CTG start codon, pseudogene restoration and quality assurance of the Keio strain collection.
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Affiliation(s)
- Jindan Zhou
- Department of Biochemistry and Molecular Biology, The Miller School of Medicine, University of Miami, Miami, FL 33143, USA
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8
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Dimmer EC, Huntley RP, Alam-Faruque Y, Sawford T, O'Donovan C, Martin MJ, Bely B, Browne P, Mun Chan W, Eberhardt R, Gardner M, Laiho K, Legge D, Magrane M, Pichler K, Poggioli D, Sehra H, Auchincloss A, Axelsen K, Blatter MC, Boutet E, Braconi-Quintaje S, Breuza L, Bridge A, Coudert E, Estreicher A, Famiglietti L, Ferro-Rojas S, Feuermann M, Gos A, Gruaz-Gumowski N, Hinz U, Hulo C, James J, Jimenez S, Jungo F, Keller G, Lemercier P, Lieberherr D, Masson P, Moinat M, Pedruzzi I, Poux S, Rivoire C, Roechert B, Schneider M, Stutz A, Sundaram S, Tognolli M, Bougueleret L, Argoud-Puy G, Cusin I, Duek-Roggli P, Xenarios I, Apweiler R. The UniProt-GO Annotation database in 2011. Nucleic Acids Res 2011; 40:D565-70. [PMID: 22123736 PMCID: PMC3245010 DOI: 10.1093/nar/gkr1048] [Citation(s) in RCA: 310] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.
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Affiliation(s)
- Emily C Dimmer
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
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9
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McIntosh BK, Renfro DP, Knapp GS, Lairikyengbam CR, Liles NM, Niu L, Supak AM, Venkatraman A, Zweifel AE, Siegele DA, Hu JC. EcoliWiki: a wiki-based community resource for Escherichia coli. Nucleic Acids Res 2011; 40:D1270-7. [PMID: 22064863 PMCID: PMC3245172 DOI: 10.1093/nar/gkr880] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
EcoliWiki is the community annotation component of the PortEco (http://porteco.org; formerly EcoliHub) project, an online data resource that integrates information on laboratory strains of Escherichia coli, its phages, plasmids and mobile genetic elements. As one of the early adopters of the wiki approach to model organism databases, EcoliWiki was designed to not only facilitate community-driven sharing of biological knowledge about E. coli as a model organism, but also to be interoperable with other data resources. EcoliWiki content currently covers genes from five laboratory E. coli strains, 21 bacteriophage genomes, F plasmid and eight transposons. EcoliWiki integrates the Mediawiki wiki platform with other open-source software tools and in-house software development to extend how wikis can be used for model organism databases. EcoliWiki can be accessed online at http://ecoliwiki.net.
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Affiliation(s)
- Brenley K McIntosh
- Department of Biochemistry and Biophysics, Texas Agrilife Research, Texas A&M University College Station, TX 77843, USA
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10
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Unifying themes in microbial associations with animal and plant hosts described using the gene ontology. Microbiol Mol Biol Rev 2011; 74:479-503. [PMID: 21119014 DOI: 10.1128/mmbr.00017-10] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, however, are hampered by inconsistent descriptive terms in the literature for functionally similar genes. Bioinformatic approaches that use homology-based tools are limited to identifying functionally similar genes based on similarities in their sequences. An effective solution to these limitations is provided by the Gene Ontology (GO), which provides a standardized language to describe gene products from all organisms. The GO comprises three ontologies that enable one to describe the molecular function(s) of gene products, the biological processes to which they contribute, and their cellular locations. Beginning in 2004, the Plant-Associated Microbe Gene Ontology (PAMGO) interest group collaborated with the GO consortium to extend the GO to accommodate terms for describing gene products associated with microbe-host interactions. Currently, over 900 terms that describe biological processes common to diverse plant- and animal-associated microbes are incorporated into the GO database. Here we review some unifying themes common to diverse host-microbe associations and illustrate how the new GO terms facilitate a standardized description of the gene products involved. We also highlight areas where new terms need to be developed, an ongoing process that should involve the whole community.
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11
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du Plessis L, Skunca N, Dessimoz C. The what, where, how and why of gene ontology--a primer for bioinformaticians. Brief Bioinform 2011; 12:723-35. [PMID: 21330331 PMCID: PMC3220872 DOI: 10.1093/bib/bbr002] [Citation(s) in RCA: 103] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications.
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Affiliation(s)
- Louis du Plessis
- ETH Zurich, Computer Science, Universitätstr. 6, 8092 Zurich, Switzerland
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12
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Karathia H, Vilaprinyo E, Sorribas A, Alves R. Saccharomyces cerevisiae as a model organism: a comparative study. PLoS One 2011; 6:e16015. [PMID: 21311596 PMCID: PMC3032731 DOI: 10.1371/journal.pone.0016015] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2010] [Accepted: 12/03/2010] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Model organisms are used for research because they provide a framework on which to develop and optimize methods that facilitate and standardize analysis. Such organisms should be representative of the living beings for which they are to serve as proxy. However, in practice, a model organism is often selected ad hoc, and without considering its representativeness, because a systematic and rational method to include this consideration in the selection process is still lacking. METHODOLOGY/PRINCIPAL FINDINGS In this work we propose such a method and apply it in a pilot study of strengths and limitations of Saccharomyces cerevisiae as a model organism. The method relies on the functional classification of proteins into different biological pathways and processes and on full proteome comparisons between the putative model organism and other organisms for which we would like to extrapolate results. Here we compare S. cerevisiae to 704 other organisms from various phyla. For each organism, our results identify the pathways and processes for which S. cerevisiae is predicted to be a good model to extrapolate from. We find that animals in general and Homo sapiens in particular are some of the non-fungal organisms for which S. cerevisiae is likely to be a good model in which to study a significant fraction of common biological processes. We validate our approach by correctly predicting which organisms are phenotypically more distant from S. cerevisiae with respect to several different biological processes. CONCLUSIONS/SIGNIFICANCE The method we propose could be used to choose appropriate substitute model organisms for the study of biological processes in other species that are harder to study. For example, one could identify appropriate models to study either pathologies in humans or specific biological processes in species with a long development time, such as plants.
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Affiliation(s)
- Hiren Karathia
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida & IRBLleida, Lleida, Spain
| | - Ester Vilaprinyo
- Evaluation and Clinical Epidemiology Department, Hospital del Mar-IMIM, Barcelona, Spain
| | - Albert Sorribas
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida & IRBLleida, Lleida, Spain
| | - Rui Alves
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida & IRBLleida, Lleida, Spain
- * E-mail:
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Keseler IM, Collado-Vides J, Santos-Zavaleta A, Peralta-Gil M, Gama-Castro S, Muñiz-Rascado L, Bonavides-Martinez C, Paley S, Krummenacker M, Altman T, Kaipa P, Spaulding A, Pacheco J, Latendresse M, Fulcher C, Sarker M, Shearer AG, Mackie A, Paulsen I, Gunsalus RP, Karp PD. EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res 2010; 39:D583-90. [PMID: 21097882 PMCID: PMC3013716 DOI: 10.1093/nar/gkq1143] [Citation(s) in RCA: 342] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
EcoCyc (http://EcoCyc.org) is a comprehensive model organism database for Escherichia coli K-12 MG1655. From the scientific literature, EcoCyc captures the functions of individual E. coli gene products; their regulation at the transcriptional, post-transcriptional and protein level; and their organization into operons, complexes and pathways. EcoCyc users can search and browse the information in multiple ways. Recent improvements to the EcoCyc Web interface include combined gene/protein pages and a Regulation Summary Diagram displaying a graphical overview of all known regulatory inputs to gene expression and protein activity. The graphical representation of signal transduction pathways has been updated, and the cellular and regulatory overviews were enhanced with new functionality. A specialized undergraduate teaching resource using EcoCyc is being developed.
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Affiliation(s)
- Ingrid M Keseler
- SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, USA.
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