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CENTDIST: discovery of co-associated factors by motif distribution

Zhizhuo Zhang,Cheng Wei Chang,W. L. Goh,W. Sung,E. Cheung

Published 2011 in Nucleic Acids Res.

ABSTRACT

Transcription factors (TFs) do not function alone but work together with other TFs (called co-TFs) in a combinatorial fashion to precisely control the transcription of target genes. Mining co-TFs is thus important to understand the mechanism of transcriptional regulation. Although existing methods can identify co-TFs, their accuracy depends heavily on the chosen background model and other parameters such as the enrichment window size and the PWM score cut-off. In this study, we have developed a novel web-based co-motif scanning program called CENTDIST (http://compbio.ddns.comp.nus.edu.sg/~chipseq/centdist/). In comparison to current co-motif scanning programs, CENTDIST does not require the input of any user-specific parameters and background information. Instead, CENTDIST automatically determines the best set of parameters and ranks co-TF motifs based on their distribution around ChIP-seq peaks. We tested CENTDIST on 14 ChIP-seq data sets and found CENTDIST is more accurate than existing methods. In particular, we applied CENTDIST on an Androgen Receptor (AR) ChIP-seq data set from a prostate cancer cell line and correctly predicted all known co-TFs (eight TFs) of AR in the top 20 hits as well as discovering AP4 as a novel co-TF of AR (which was missed by existing methods). Taken together, CENTDIST, which exploits the imbalanced nature of co-TF binding, is a user-friendly, parameter-less and powerful predictive web-based program for understanding the mechanism of transcriptional co-regulation.

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