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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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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