Research article
Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
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* Corresponding author: Bruce Schatz schatz@illinois.edu
1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2 Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
BMC Bioinformatics 2010, 11:272 doi:10.1186/1471-2105-11-272
Published: 20 May 2010Abstract
Background
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.
Results
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.
Conclusions
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: http://workerbee.igb.uiuc.edu:8080/BeeSpace/Search.jsp webcite