False discovery rates in identifying functional DNA motifs

Abstract

There are several methods for scoring a set of upstream DNA sequences against a given motif. Typically, significance of raw scores are based on p-values, measured by statistical hypothesis testing. As an extension, multiple hypothesis testing is adopted in cases where there are multiple motifs to be evaluated in parallel. In this way significant motifs are identified for a given significance level. However, a set of significantly identified motifs can contain false positives. In this work, we introduce a false discovery rate estimation problem for significantly predicted motifs. An explorative method for this problem is presented. We test the method using TRANSFAC and JASPAR motif libraries on several upstream DNA subsets of S.cerevisiae. The results show the effectiveness of the method.