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Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources
BACKGROUND: Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2585105/ https://www.ncbi.nlm.nih.gov/pubmed/18939968 http://dx.doi.org/10.1186/1471-2164-9-495 |
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author | Blom, Evert Jan Breitling, Rainer Hofstede, Klaas Jan Roerdink, Jos BTM van Hijum, Sacha AFT Kuipers, Oscar P |
author_facet | Blom, Evert Jan Breitling, Rainer Hofstede, Klaas Jan Roerdink, Jos BTM van Hijum, Sacha AFT Kuipers, Oscar P |
author_sort | Blom, Evert Jan |
collection | PubMed |
description | BACKGROUND: Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes. RESULTS: We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website. CONCLUSION: The Prosecutor software and supplementary datasets available at allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied. |
format | Text |
id | pubmed-2585105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-25851052008-11-20 Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources Blom, Evert Jan Breitling, Rainer Hofstede, Klaas Jan Roerdink, Jos BTM van Hijum, Sacha AFT Kuipers, Oscar P BMC Genomics Software BACKGROUND: Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes. RESULTS: We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website. CONCLUSION: The Prosecutor software and supplementary datasets available at allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied. BioMed Central 2008-10-21 /pmc/articles/PMC2585105/ /pubmed/18939968 http://dx.doi.org/10.1186/1471-2164-9-495 Text en Copyright © 2008 Blom et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Blom, Evert Jan Breitling, Rainer Hofstede, Klaas Jan Roerdink, Jos BTM van Hijum, Sacha AFT Kuipers, Oscar P Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title | Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title_full | Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title_fullStr | Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title_full_unstemmed | Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title_short | Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources |
title_sort | prosecutor: parameter-free inference of gene function for prokaryotes using dna microarray data, genomic context and multiple gene annotation sources |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2585105/ https://www.ncbi.nlm.nih.gov/pubmed/18939968 http://dx.doi.org/10.1186/1471-2164-9-495 |
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