Condition-specific target prediction from motifs and expression.

Bioinformatics 2014 Jun 14;30(12):1643-50. Epub 2014 Feb 14.

Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.

Motivation: It is commonplace to predict targets of transcription factors (TFs) by sequence matching with their binding motifs. However, this ignores the particular condition of the cells. Gene expression data can provide condition-specific information, as is, e.g. exploited in Motif Enrichment Analysis.

Results: Here, we introduce a novel tool named condition-specific target prediction (CSTP) to predict condition-specific targets for TFs from expression data measured by either microarray or RNA-seq. Based on the philosophy of guilt by association, CSTP infers the regulators of each studied gene by recovering the regulators of its co-expressed genes. In contrast to the currently used methods, CSTP does not insist on binding sites of TFs in the promoter of the target genes. CSTP was applied to three independent biological processes for evaluation purposes. By analyzing the predictions for the same TF in three biological processes, we confirm that predictions with CSTP are condition-specific. Predictions were further compared with true TF binding sites as determined by ChIP-seq/chip. We find that CSTP predictions overlap with true binding sites to a degree comparable with motif-based predictions, although the two target sets do not coincide.

Availability And Implementation: CSTP is available via a web-based interface at http://cstp.molgen.mpg.de.

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Source
http://dx.doi.org/10.1093/bioinformatics/btu066DOI Listing
June 2014
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