Systematic discovery of cofactor motifs from ChIP-seq data by SIOMICS

• SIOMICS works well on both small and large datasets.
• SIOMICS can identify underrepresented cofactor motifs in ChIP-seq regions.
• SIOMICS runs fast, with <2.5 CPU h for a dataset of 5347 ChIP-seq peaks.
• SIOMICS_Extension detects motifs with different lengths.

Understanding transcriptional regulatory elements and particularly the transcription factor binding sites represents a significant challenge in computational biology. The chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) experiments provide an unprecedented opportunity to study transcription factor binding sites on the genome-wide scale. Here we describe a recently developed tool, SIOMICS, to systematically discover motifs and binding sites of transcription factors and their cofactors from ChIP-seq data. Unlike other tools, SIOMICS explores the co-binding properties of multiple transcription factors in short regions to predict motifs and binding sites. We have previously shown that the original SIOMICS method predicts motifs and binding sites of more cofactors in more accurate and time-effective ways than two popular methods. In this paper, we present the extended SIOMICS method, SIOMICS_Extension, and demonstrate its usage for systematic discovery of cofactor motifs and binding sites. The SIOMICS tool, including SIOMICS and SIOMICS_Extension, are available at http://hulab.ucf.edu/research/projects/SIOMICS/SIOMICS.html.