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Martini: using literature keywords to compare gene sets

Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented i...

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Detalles Bibliográficos
Autores principales: Soldatos, Theodoros G., O’Donoghue, Seán I., Satagopam, Venkata P., Jensen, Lars J., Brown, Nigel P., Barbosa-Silva, Adriano, Schneider, Reinhard
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2800231/
https://www.ncbi.nlm.nih.gov/pubmed/19858102
http://dx.doi.org/10.1093/nar/gkp876
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author Soldatos, Theodoros G.
O’Donoghue, Seán I.
Satagopam, Venkata P.
Jensen, Lars J.
Brown, Nigel P.
Barbosa-Silva, Adriano
Schneider, Reinhard
author_facet Soldatos, Theodoros G.
O’Donoghue, Seán I.
Satagopam, Venkata P.
Jensen, Lars J.
Brown, Nigel P.
Barbosa-Silva, Adriano
Schneider, Reinhard
author_sort Soldatos, Theodoros G.
collection PubMed
description Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented in one gene set. However, such tools often return GO terms that are too generic or too few to be informative. Here, we present Martini, an easy-to-use tool for comparing gene sets. Martini is based, not on GO, but on keywords extracted from Medline abstracts; Martini also supports a much wider range of species than comparable tools. To evaluate Martini we created a benchmark based on the human cell cycle, and we tested several comparable tools (CoPub, FatiGO, Marmite and ProfCom). Martini had the best benchmark performance, delivering a more detailed and accurate description of function. Martini also gave best or equal performance with three other datasets (related to Arabidopsis, melanoma and ovarian cancer), suggesting that Martini represents an advance in the automated comparison of gene sets. In agreement with previous studies, our results further suggest that literature-derived keywords are a richer source of gene-function information than GO annotations. Martini is freely available at http://martini.embl.de.
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spelling pubmed-28002312009-12-31 Martini: using literature keywords to compare gene sets Soldatos, Theodoros G. O’Donoghue, Seán I. Satagopam, Venkata P. Jensen, Lars J. Brown, Nigel P. Barbosa-Silva, Adriano Schneider, Reinhard Nucleic Acids Res Computational Biology Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented in one gene set. However, such tools often return GO terms that are too generic or too few to be informative. Here, we present Martini, an easy-to-use tool for comparing gene sets. Martini is based, not on GO, but on keywords extracted from Medline abstracts; Martini also supports a much wider range of species than comparable tools. To evaluate Martini we created a benchmark based on the human cell cycle, and we tested several comparable tools (CoPub, FatiGO, Marmite and ProfCom). Martini had the best benchmark performance, delivering a more detailed and accurate description of function. Martini also gave best or equal performance with three other datasets (related to Arabidopsis, melanoma and ovarian cancer), suggesting that Martini represents an advance in the automated comparison of gene sets. In agreement with previous studies, our results further suggest that literature-derived keywords are a richer source of gene-function information than GO annotations. Martini is freely available at http://martini.embl.de. Oxford University Press 2010-01 2009-10-25 /pmc/articles/PMC2800231/ /pubmed/19858102 http://dx.doi.org/10.1093/nar/gkp876 Text en © The Author(s) 2009. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Soldatos, Theodoros G.
O’Donoghue, Seán I.
Satagopam, Venkata P.
Jensen, Lars J.
Brown, Nigel P.
Barbosa-Silva, Adriano
Schneider, Reinhard
Martini: using literature keywords to compare gene sets
title Martini: using literature keywords to compare gene sets
title_full Martini: using literature keywords to compare gene sets
title_fullStr Martini: using literature keywords to compare gene sets
title_full_unstemmed Martini: using literature keywords to compare gene sets
title_short Martini: using literature keywords to compare gene sets
title_sort martini: using literature keywords to compare gene sets
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2800231/
https://www.ncbi.nlm.nih.gov/pubmed/19858102
http://dx.doi.org/10.1093/nar/gkp876
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