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Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms
Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The prob...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174934/ https://www.ncbi.nlm.nih.gov/pubmed/21949701 http://dx.doi.org/10.1371/journal.pone.0024289 |
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author | Fontanillo, Celia Nogales-Cadenas, Ruben Pascual-Montano, Alberto De Las Rivas, Javier |
author_facet | Fontanillo, Celia Nogales-Cadenas, Ruben Pascual-Montano, Alberto De Las Rivas, Javier |
author_sort | Fontanillo, Celia |
collection | PubMed |
description | Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es. |
format | Online Article Text |
id | pubmed-3174934 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-31749342011-09-26 Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms Fontanillo, Celia Nogales-Cadenas, Ruben Pascual-Montano, Alberto De Las Rivas, Javier PLoS One Research Article Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es. Public Library of Science 2011-09-16 /pmc/articles/PMC3174934/ /pubmed/21949701 http://dx.doi.org/10.1371/journal.pone.0024289 Text en Fontanillo et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Fontanillo, Celia Nogales-Cadenas, Ruben Pascual-Montano, Alberto De Las Rivas, Javier Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title | Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title_full | Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title_fullStr | Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title_full_unstemmed | Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title_short | Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms |
title_sort | functional analysis beyond enrichment: non-redundant reciprocal linkage of genes and biological terms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174934/ https://www.ncbi.nlm.nih.gov/pubmed/21949701 http://dx.doi.org/10.1371/journal.pone.0024289 |
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