Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene...

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Main Authors: Zhai Chengxiang, Chee Brant, Ling Xu, Sarma Moushumi, He Xin, Schatz Bruce
Format: Article
Language:English
Published: BMC 2010-05-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/11/272
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spelling doaj-5b810ff5be99433b95a103425a3ddf082020-11-24T20:54:15ZengBMCBMC Bioinformatics1471-21052010-05-0111127210.1186/1471-2105-11-272Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture modelZhai ChengxiangChee BrantLing XuSarma MoushumiHe XinSchatz Bruce<p>Abstract</p> <p>Background</p> <p>Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.</p> <p>Results</p> <p>We propose a statistical method that uses the primary literature, i.e. free-text, as the source to perform overrepresentation analysis. The method is based on a statistical framework of mixture model and addresses the methodological flaws in several existing programs. We implemented this method within a literature mining system, BeeSpace, taking advantage of its analysis environment and added features that facilitate the interactive analysis of gene sets. Through experimentation with several datasets, we showed that our program can effectively summarize the important conceptual themes of large gene sets, even when traditional GO-based analysis does not yield informative results.</p> <p>Conclusions</p> <p>We conclude that the current work will provide biologists with a tool that effectively complements the existing ones for overrepresentation analysis from genomic experiments. Our program, Genelist Analyzer, is freely available at: <url>http://workerbee.igb.uiuc.edu:8080/BeeSpace/Search.jsp</url></p> http://www.biomedcentral.com/1471-2105/11/272
collection DOAJ
language English
format Article
sources DOAJ
author Zhai Chengxiang
Chee Brant
Ling Xu
Sarma Moushumi
He Xin
Schatz Bruce
spellingShingle Zhai Chengxiang
Chee Brant
Ling Xu
Sarma Moushumi
He Xin
Schatz Bruce
Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
BMC Bioinformatics
author_facet Zhai Chengxiang
Chee Brant
Ling Xu
Sarma Moushumi
He Xin
Schatz Bruce
author_sort Zhai Chengxiang
title Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
title_short Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
title_full Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
title_fullStr Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
title_full_unstemmed Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
title_sort identifying overrepresented concepts in gene lists from literature: a statistical approach based on poisson mixture model
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2010-05-01
description <p>Abstract</p> <p>Background</p> <p>Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.</p> <p>Results</p> <p>We propose a statistical method that uses the primary literature, i.e. free-text, as the source to perform overrepresentation analysis. The method is based on a statistical framework of mixture model and addresses the methodological flaws in several existing programs. We implemented this method within a literature mining system, BeeSpace, taking advantage of its analysis environment and added features that facilitate the interactive analysis of gene sets. Through experimentation with several datasets, we showed that our program can effectively summarize the important conceptual themes of large gene sets, even when traditional GO-based analysis does not yield informative results.</p> <p>Conclusions</p> <p>We conclude that the current work will provide biologists with a tool that effectively complements the existing ones for overrepresentation analysis from genomic experiments. Our program, Genelist Analyzer, is freely available at: <url>http://workerbee.igb.uiuc.edu:8080/BeeSpace/Search.jsp</url></p>
url http://www.biomedcentral.com/1471-2105/11/272
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