Composite Module Analyst: identification of transcription factor binding site combinations using genetic algorithm

• Published in: Nucleic acids research Vol. 34; no. suppl-2; pp. W541 - W545
• Main Authors: Waleev, T, Shtokalo, D, Konovalova, T, Voss, N, Cheremushkin, E, Stegmaier, P, Kel-Margoulis, O, Wingender, E, Kel, A
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-07-2006; Oxford Publishing Limited (England)
• Subjects: Algorithms; Binding Sites; Endothelial Cells - metabolism; Gene Expression Profiling; Humans; Internet; Promoter Regions, Genetic; Sequence Analysis, DNA - methods; Software; Transcription Factors - metabolism; User-Computer Interface
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkl342

Composite Module Analyst (CMA) is a novel software tool aiming to identify promoter-enhancer models based on the composition of transcription factor (TF) binding sites and their pairs. CMA is closely interconnected with the TRANSFAC® database. In particular, CMA uses the positional weight matrix (PWM) library collected in TRANSFAC® and therefore provides the possibility to search for a large variety of different TF binding sites. We model the structure of the long gene regulatory regions by a Boolean function that joins several local modules, each consisting of co-localized TF binding sites. Having as an input a set of co-regulated genes, CMA builds the promoter model and optimizes the parameters of the model automatically by applying a genetic-regression algorithm. We use a multicomponent fitness function of the algorithm which includes several statistical criteria in a weighted linear function. We show examples of successful application of CMA to a microarray data on transcription profiling of TNF-alpha stimulated primary human endothelial cells. The CMA web server is freely accessible at http://www.gene-regulation.com/pub/programs/cma/CMA.html. An advanced version of CMA is also a part of the commercial system ExPlainTM (www.biobase.de) designed for causal analysis of gene expression data.