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QuickGO - Gene ontology annotation 2017
QuickGO - Gene ontology annotation 2017
Published: 2017/08/03
Channel: European Bioinformatics Institute - EMBL-EBI
SGD Help: Gene Ontology (GO)
SGD Help: Gene Ontology (GO)
Published: 2015/04/03
Channel: Saccharomyces Genome Database
Gene ontology
Gene ontology
Published: 2015/10/23
Channel: Audiopedia
TAIR Gene Ontology (GO) Annotations
TAIR Gene Ontology (GO) Annotations
Published: 2016/03/25
Channel: TAIRinfo
Using DAVID for Functional Enrichment Analysis in a Set of Genes (Part 1)
Using DAVID for Functional Enrichment Analysis in a Set of Genes (Part 1)
Published: 2013/05/03
Channel: NIAID Bioinformatics
Quantifying GO Term Annotations
Quantifying GO Term Annotations
Published: 2014/03/14
Channel: ProteomeSoftwareInc
Introduction to Biomedical Ontologies #1:  What is an Ontology?
Introduction to Biomedical Ontologies #1: What is an Ontology?
Published: 2009/07/30
Channel: JenniferAtRGD
Genome annotation
Genome annotation
Published: 2015/05/29
Channel: Shomu's Biology
ICBO2016: Plant Phenomes to Genomes: Integrating the Gene Ontology with the Plant Ontology
ICBO2016: Plant Phenomes to Genomes: Integrating the Gene Ontology with the Plant Ontology
Published: 2016/08/09
Channel: ICBO Conference
GeneOntology
GeneOntology
Published: 2013/11/14
Channel: Rengul Atalay
DAVID (Functional Annotation Tool) Tutorial
DAVID (Functional Annotation Tool) Tutorial
Published: 2016/05/04
Channel: Melinda Song
GO Enrichment Analysis (DGE Analysis on Genestack Platform
GO Enrichment Analysis (DGE Analysis on Genestack Platform
Published: 2015/11/05
Channel: Genestack
Using DAVID to find Enriched Gene Sets
Using DAVID to find Enriched Gene Sets
Published: 2011/09/16
Channel: srlab
EnrichNet - Network-based gene set enrichment analysis
EnrichNet - Network-based gene set enrichment analysis
Published: 2012/01/03
Channel: settembrini42
How to analyze RNA-Seq data? Find differentially expressed genes in your research.
How to analyze RNA-Seq data? Find differentially expressed genes in your research.
Published: 2016/10/06
Channel: Candice Chu
Lecture 23 - Enrichment Analysis Part 1
Lecture 23 - Enrichment Analysis Part 1
Published: 2016/02/10
Channel: Avi Ma'ayan
YeastMine: Creating and Using Gene Lists
YeastMine: Creating and Using Gene Lists
Published: 2014/04/29
Channel: Saccharomyces Genome Database
Ontology of Aging and Death
Ontology of Aging and Death
Published: 2013/09/25
Channel: Barry Smith
Gene Ontology Tables; Genomic annotation with Bioconductor
Gene Ontology Tables; Genomic annotation with Bioconductor
Published: 2017/03/04
Channel: Ramy Elgendy
Using Revigo to visualize DAVID data
Using Revigo to visualize DAVID data
Published: 2011/09/16
Channel: srlab
Gene Ontology Example
Gene Ontology Example
Published: 2011/12/16
Channel: 張栢彰
Coexpressed gene network (Gene Ontology)
Coexpressed gene network (Gene Ontology)
Published: 2015/05/07
Channel: COXPRESdb
DNASTAR - Using the Gene Ontology View
DNASTAR - Using the Gene Ontology View
Published: 2011/12/14
Channel: DNASTARInc
SGD Help: What is GO?
SGD Help: What is GO?
Published: 2015/09/10
Channel: Saccharomyces Genome Database
Finding related genes in FlyBase: The Gene Ontology
Finding related genes in FlyBase: The Gene Ontology
Published: 2016/12/20
Channel: FlyBase TV
DNASTAR - Using Gene Ontology Information
DNASTAR - Using Gene Ontology Information
Published: 2016/05/11
Channel: DNASTARInc
WEBINAR: Enriching protein corona fingerprints using gene ontology
WEBINAR: Enriching protein corona fingerprints using gene ontology
Published: 2015/10/30
Channel: Douglas Connect GmbH
Enrichment course - Introduction
Enrichment course - Introduction
Published: 2013/11/12
Channel: Csaba Ortutay
L24v04 gene ontology
L24v04 gene ontology
Published: 2014/04/09
Channel: Martin Steffen
DNASTAR - Gene Ontology Workflow
DNASTAR - Gene Ontology Workflow
Published: 2012/06/29
Channel: DNASTARInc
Rice Platform:  Gene Ontology Analysis
Rice Platform: Gene Ontology Analysis
Published: 2011/12/26
Channel: 張栢彰
マイクロアレイデータのGene Ontologyによるアノテーション
マイクロアレイデータのGene Ontologyによるアノテーション
Published: 2009/08/07
Channel: togotv
Medical vocabulary: What does Gene Ontology mean
Medical vocabulary: What does Gene Ontology mean
Published: 2016/01/20
Channel: botcaster inc. bot
4.lancement analyse Gene Ontologie avec Bingo Cytoscape
4.lancement analyse Gene Ontologie avec Bingo Cytoscape
Published: 2013/03/06
Channel: biotech gphy
How to perform Gene Set Enrichment Analysis (GSEA) with Blast2GO
How to perform Gene Set Enrichment Analysis (GSEA) with Blast2GO
Published: 2016/09/08
Channel: Blast2GO
DAVID Analysis, Gene Functional Classification
DAVID Analysis, Gene Functional Classification
Published: 2008/02/29
Channel: Mikhail Dozmorov
DNASTAR - Gene Ontology View
DNASTAR - Gene Ontology View
Published: 2012/06/12
Channel: DNASTARInc
How to use BioMart and GO-Slim in Blast2GO
How to use BioMart and GO-Slim in Blast2GO
Published: 2016/08/02
Channel: Blast2GO
Application of Latent Semantic Analysis to Clustering of Cardiovascular Gene Ontology
Application of Latent Semantic Analysis to Clustering of Cardiovascular Gene Ontology
Published: 2017/08/23
Channel: C. N. Charles Wang
5.Analyse Gene Ontologie Cytoscape
5.Analyse Gene Ontologie Cytoscape
Published: 2013/03/06
Channel: biotech gphy
GO Analysis in iVariantGuide
GO Analysis in iVariantGuide
Published: 2017/03/02
Channel: Advaita BioInformatics
Gene Ontologyを使って特定遺伝子の機能情報を検索する
Gene Ontologyを使って特定遺伝子の機能情報を検索する
Published: 2009/07/29
Channel: togotv
Gene Ontologyを使って特定遺伝子の機能情報を検索する 2011
Gene Ontologyを使って特定遺伝子の機能情報を検索する 2011
Published: 2011/10/28
Channel: togotv
Blast2GO bioinformatic software basic Introduction
Blast2GO bioinformatic software basic Introduction
Published: 2016/04/20
Channel: EDUtainment TV
Enrichment analysis: interpret gene lists like a pro
Enrichment analysis: interpret gene lists like a pro
Published: 2016/10/14
Channel: Online Learning 38
iPathwayGuide - GO Summary
iPathwayGuide - GO Summary
Published: 2014/09/28
Channel: Advaita BioInformatics
ngKLAST: using NCBI Taxonomy, Gene Ontology, Enzyme and SwissProt
ngKLAST: using NCBI Taxonomy, Gene Ontology, Enzyme and SwissProt
Published: 2013/03/25
Channel: korilog56
생명정보개론 제13강 파트1 - Gene Ontology & Subcelluar localization
생명정보개론 제13강 파트1 - Gene Ontology & Subcelluar localization
Published: 2016/05/22
Channel: Sangsoo Kim
How to perform a Fisher
How to perform a Fisher's Exact Test with Blast2GO
Published: 2016/08/03
Channel: Blast2GO
Find genes with an enriched GO term
Find genes with an enriched GO term
Published: 2015/10/14
Channel: EuPathDB
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WIKIPEDIA ARTICLE

From Wikipedia, the free encyclopedia
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Gene ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species.[1] More specifically, the project aims to: 1) maintain and develop its controlled vocabulary of gene and gene product attributes; 2) annotate genes and gene products, and assimilate and disseminate annotation data; and 3) provide tools for easy access to all aspects of the data provided by the project, and to enable functional interpretation of experimental data using the GO, for example via enrichment analysis.

GO is part of a larger classification effort, the Open Biomedical Ontologies (OBO).[2]

Although gene nomenclature itself aims to maintain and develop controlled vocabulary of gene and gene products, the Gene Ontology extends the effort by using markup language to make the data (not only of the genes and their products but also of all their attributes) machine readable, and to do so in a way that is unified across all species (whereas gene nomenclature conventions vary by biologic taxon).

Terms and ontology[edit]

From a practical view, an ontology is a representation of something we know about. "Ontologies" consist of a representation of things that are detectable or directly observable, and the relationships between those things. There is no universal standard terminology in biology and related domains, and term usages may be specific to a species, research area or even a particular research group. This makes communication and sharing of data more difficult. The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:

Each GO term within the ontology has a term name, which may be a word or string of words; a unique alphanumeric identifier; a definition with cited sources; and a namespace indicating the domain to which it belongs. Terms may also have synonyms, which are classed as being exactly equivalent to the term name, broader, narrower, or related; references to equivalent concepts in other databases; and comments on term meaning or usage. The GO ontology is structured as a directed acyclic graph, and each term has defined relationships to one or more other terms in the same domain, and sometimes to other domains. The GO vocabulary is designed to be species-neutral, and includes terms applicable to prokaryotes and eukaryotes, single and multicellular organisms.

GO is not static, and additions, corrections and alterations are suggested by, and solicited from, members of the research and annotation communities, as well as by those directly involved in the GO project.[3] For example, an annotator may request a specific term to represent a metabolic pathway, or a section of the ontology may be revised with the help of community experts (e.g.[4]). Suggested edits are reviewed by the ontology editors, and implemented where appropriate.

The GO ontology file is freely available from the GO website[5] in a number of formats, or can be accessed online using the GO browser AmiGO. The Gene Ontology project also provides downloadable mappings of its terms to other classification systems.

Example term[edit]

id: GO:0000016
name: lactase activity
namespace: molecular_function
def: "Catalysis of the reaction: lactose + H2O=D-glucose + D-galactose." [EC:3.2.1.108]
synonym: "lactase-phlorizin hydrolase activity" BROAD [EC:3.2.1.108]
synonym: "lactose galactohydrolase activity" EXACT [EC:3.2.1.108]
xref: EC:3.2.1.108
xref: MetaCyc:LACTASE-RXN
xref: Reactome:20536
is_a: GO:0004553 ! hydrolase activity, hydrolyzing O-glycosyl compounds

Data source:[6]

Annotation[edit]

Genome annotation is the practice of capturing data about a gene product, and GO annotations use terms from the GO ontology to do so. The members of the GO Consortium submit their annotation for integration and dissemination on the GO website, where they can be downloaded directly or viewed online using AmiGO. In addition to the gene product identifier and the relevant GO term, GO annotations have the following data: The reference used to make the annotation (e.g. a journal article; An evidence code denoting the type of evidence upon which the annotation is based; The date and the creator of the annotation

The evidence code comes from a controlled vocabulary of codes covering both manual and automated annotation methods. For example, Traceable Author Statement (TAS) means a curator has read a published scientific paper and the metadata for that annotation bears a citation to that paper; Inferred from Sequence Similarity (ISS) means a human curator has reviewed the output from a sequence similarity search and verified that it is biologically meaningful. Annotations from automated processes (for example, remapping annotations created using another annotation vocabulary) are given the code Inferred from Electronic Annotation (IEA). As of April 1, 2010, over 98% of all GO annotations were inferred computationally, not by curators.[7] As these annotations are not checked by a human, the GO Consortium considers them to be less reliable and includes only a subset in the data available online in AmiGO. Full annotation data sets can be downloaded from the GO website. To support the development of annotation, the GO Consortium provides study camps and mentors to new groups of developers.


Example annotation[edit]

Gene product:    Actin, alpha cardiac muscle 1, UniProtKB:P68032
GO term: heart contraction ; GO:0060047 (biological process)
Evidence code: Inferred from Mutant Phenotype (IMP)
Reference: PMID 17611253
Assigned by: UniProtKB, June 6, 2008

Data source:[8]

Tools[edit]

There are a large number of tools available[9] both online and to download that use the data provided by the GO project. The vast majority of these come from third parties; the GO Consortium develops and supports two tools, AmiGO and OBO-Edit.

AmiGO[10] is a web-based application that allows users to query, browse and visualize ontologies and gene product annotation data. In addition, it also has a BLAST tool,[11] tools allowing analysis of larger data sets,[12][13] and an interface to query the GO database directly.[14]

AmiGO can be used online at the GO website to access the data provided by the GO Consortium, or can be downloaded and installed for local use on any database employing the GO database schema (e.g.[15]). It is free open source software and is available as part of the go-dev software distribution.[16]

OBO-Edit[17] is an open source, platform-independent ontology editor developed and maintained by the Gene Ontology Consortium. It is implemented in Java, and uses a graph-oriented approach to display and edit ontologies. OBO-Edit includes a comprehensive search and filter interface, with the option to render subsets of terms to make them visually distinct; the user interface can also be customized according to user preferences. OBO-Edit also has a reasoner that can infer links that have not been explicitly stated, based on existing relationships and their properties. Although it was developed for biomedical ontologies, OBO-Edit can be used to view, search and edit any ontology. It is freely available to download.[16]

Consortium[edit]

The Gene Ontology Consortium is the set of biological databases and research groups actively involved in the gene ontology project.[18] This includes a number of model organism databases and multi-species protein databases, software development groups, and a dedicated editorial office.

History[edit]

Gene ontology was originally constructed in 1998 by a consortium of researchers studying the genome of three model organisms: Drosophila melanogaster (fruit fly), Mus musculus (mouse), and Saccharomyces cerevisiae (brewer's or baker's yeast).[19] Many other Model Organism Databases have joined the Gene Ontology consortium, contributing not only annotation data, but also contributing to the development of the ontologies and tools to view and apply the data. Until now, most of major databases in plant, animal and microorganism make a contribution towards this project. As of January 2008, GO contains over 24,500 terms applicable to a wide variety of biological organisms. There is a significant body of literature on the development and use of GO, and it has become a standard tool in the bioinformatics arsenal. Their objectives have three aspects: building gene ontology, assigning ontology to gene/gene products and developing software and databases for the first two objects.

See also[edit]

References[edit]

  1. ^ The Gene Ontology Consortium (January 2008). "The Gene Ontology project in 2008". Nucleic Acids Research. 36 (Database issue): D440–4. PMC 2238979Freely accessible. PMID 17984083. doi:10.1093/nar/gkm883. 
  2. ^ Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg LJ, Eilbeck K, Ireland A, Mungall CJ, Leontis N, Rocca-Serra P, Ruttenberg A, Sansone SA, Scheuermann RH, Shah N, Whetzel PL, Lewis S (November 2007). "The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration". Nature Biotechnology. 25 (11): 1251–5. PMC 2814061Freely accessible. PMID 17989687. doi:10.1038/nbt1346. 
  3. ^ Lovering, Ruth C. (2017). "How Does the Scientific Community Contribute to Gene Ontology?". In Dessimoz, C; Skunca, N. The Gene Ontology Handbook. Methods in Molecular Biology. 1446. Springer (New York). pp. 85–93. ISSN 1064-3745. doi:10.1007/978-1-4939-3743-1_7. 
  4. ^ Diehl AD, Lee JA, Scheuermann RH, Blake JA (April 2007). "Ontology development for biological systems: immunology". Bioinformatics. 23 (7): 913–5. PMID 17267433. doi:10.1093/bioinformatics/btm029. 
  5. ^ "Gene Ontology Database". Gene Ontology Consortium. 
  6. ^ The GO Consortium (2009-03-16). "gene_ontology.1_2.obo" (OBO 1.2 flat file). 
  7. ^ du Plessis L, Skunca N, Dessimoz C (November 2011). "The what, where, how and why of gene ontology--a primer for bioinformaticians". Briefings in Bioinformatics. 12 (6): 723–35. PMC 3220872Freely accessible. PMID 21330331. doi:10.1093/bib/bbr002. 
  8. ^ The GO Consortium (2009-03-16). "AmiGO: P68032 Associations". 
  9. ^ Mosquera JL, Sánchez-Pla A (July 2008). "SerbGO: searching for the best GO tool". Nucleic Acids Research. 36 (Web Server issue): W368–71. PMC 2447766Freely accessible. PMID 18480123. doi:10.1093/nar/gkn256. 
  10. ^ Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S (January 2009). AmiGO Hub; Web Presence Working Group. "AmiGO: online access to ontology and annotation data". Bioinformatics. 25 (2): 288–9. PMC 2639003Freely accessible. PMID 19033274. doi:10.1093/bioinformatics/btn615. 
  11. ^ AmiGO BLAST tool
  12. ^ AmiGO Term Enrichment tool; finds significant shared GO terms in an annotation set
  13. ^ AmiGO Slimmer; maps granular annotations up to high-level terms
  14. ^ GOOSE, GO Online SQL Environment; allows direct SQL querying of the GO database
  15. ^ The Plant Ontology Consortium (2009-03-16). "Plant Ontology Consortium". Retrieved 2009-03-16. 
  16. ^ a b "Gene Ontology downloads at SourceForge". Retrieved 2009-03-16. 
  17. ^ Day-Richter J, Harris MA, Haendel M, Lewis S (August 2007). "OBO-Edit--an ontology editor for biologists". Bioinformatics. 23 (16): 2198–200. PMID 17545183. doi:10.1093/bioinformatics/btm112. 
  18. ^ "The GO Consortium". Retrieved 2009-03-16. 
  19. ^ Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (May 2000). "Gene ontology: tool for the unification of biology. The Gene Ontology Consortium". Nature Genetics. 25 (1): 25–9. PMC 3037419Freely accessible. PMID 10802651. doi:10.1038/75556. 
  20. ^ Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A (June 2008). "High-throughput functional annotation and data mining with the Blast2GO suite". Nucleic Acids Research. 36 (10): 3420–35. PMC 2425479Freely accessible. PMID 18445632. doi:10.1093/nar/gkn176. 

External links[edit]

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