oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functiona...

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Bibliographic Details
Published in:Nucleic acids research 2005-01, Vol.33 (10), p.3154-3164
Main Authors: Ho Sui, Shannan J., Mortimer, James R., Arenillas, David J., Brumm, Jochen, Walsh, Christopher J., Kennedy, Brian P., Wasserman, Wyeth W.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Binding Sites
Databases, Nucleic Acid
Gene Expression Profiling
Gene Expression Regulation
Humans
Internet
Mice
NF-kappa B - metabolism
Oligonucleotide Array Sequence Analysis
Promoter Regions, Genetic
Transcription Factors - metabolism
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Summary:Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gki624