KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences

Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor binding sites from promoter regions of potential transcription factor target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling or heuristics for extending seed oligos. Such algorithms succeed in identifying single, relatively well conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites as they are often found in cis-regulatory modules. Results: We propose a new algorithm that combines the benefits of existing motif finding with the ones of Support Vector Machines (SVMs) for modeling degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana we are able to show that the newly developed strategy significantly improves the recognition of transcription factor targets. Availability: The PYTHON source code (open source–licensed under GPL), the data for the experiments and a web-service will be available after acceptance at http://www.fml.tuebingen.mpg.de/raetsch/projects/kirmes .