Motivation: A key aspect of transcriptional regulation is the binding of transcription factors to sequence-specific binding sites that allow them to modulate the expression of nearby genes. Given models of such binding sites, one can scan regulatory regions for putative binding sites and construct a genome-wide regulatory network. In such genome-wide scans, it is crucial to control the amount of false positive predictions. Recently, several works demonstrated the benefits of modeling dependencies between positions within the binding site. Yet, computing the statistical significance of putative binding sites in this scenario remains a challenge. Results: We present a general, accurate and efficient method for computing p-values of putative binding sites that is applicable to a large class of probabilistic binding site and background models. We demonstrate the accuracy of the method on synthetic and real-life data.
Bibliographical noteFunding Information:
We thank Noa Shefi and the anonymous reviewers for useful comments on an earlier version of this manuscript. This work was supported in part by the Israel Science Foundation (ISF), and the Israeli Ministry of Science. Y. Barash was supported by an Eshkol fellowship. G. Elidan and T. Kaplan were supported by Horowitz fellowships. N. Friedman was supported by an Alon fellowship and the Harry & Abe Sherman Senior Lectureship in Computer Science.