P-value-based regulatory motif discovery using positional weight matrices

Abstract

To analyze gene regulatory networks, the sequence-dependent DNA/RNA binding affinities of proteins and noncoding RNAs are crucial. Often, these are deduced from sets of sequences enriched in factor binding sites. Two classes of computational approaches exist. The first describe binding motifs by sequence patterns and search the patterns with highest statistical significance for enrichment. The second class uses the more powerful position weight matrices (PWMs). Instead of maximizing the statistical significance of enrichment, they maximize a likelihood. Here we present XXmotif (eXhaustive evaluation of matriX motifs), the first PWM-based motif discovery method that can optimize PWMs by directly minimizing their P-values of enrichment. Optimization requires computing millions of enrichment P-values for thousands of PWMs. For a given PWM, the enrichment P-value is calculated efficiently from the match P-values of all possible motif placements in the input sequences using order statistics. The approach can naturally combine P-values for motif enrichment, conservation, and localization. On ChIP-chip/seq, miRNA knock-down, and coexpression data sets from yeast and metazoans, XXmotif outperformed state-of-the-art tools, both in numbers of correctly identified motifs and in the quality of PWMs. In segmentation modules of D. melanogaster, we detect the known key regulators and several new motifs. In human core promoters, XXmotif reports most previously described and eight novel motifs sharply peaked around the transcription start site, among them an Initiator motif similar to the fly and yeast versions. XXmotif's sensitivity, reliability, and usability will help to leverage the quickly accumulating wealth of functional genomics data.