Motto: Representing motifs in consensus sequences with minimum information loss

Abstract

Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, representing motifs by wildcard-style consensus sequences is compact and sufficient for interpreting the motif information and search for motif match. Based on mutual information theory and Jenson-Shannon Divergence, we propose a mathematical framework to minimize the information loss in converting PWMs to consensus sequences. We name this representation as sequence Motto and have implemented an efficient algorithm with flexible options for converting motif PWMs into Motto from nucleotides, amino acids, and customized alphabets. Here we show that this representation provides a simple and efficient way to identify the binding sites of 1156 common TFs in the human genome. The effectiveness of the method was benchmarked by comparing sequence matches found by Motto with PWM scanning results found by FIMO. On average, our method achieves 0.81 area under the precision-recall curve, significantly (p-value < 0.01) outperforming all existing methods, including maximal positional weight, Douglas and minimal mean square error. We believe this representation provides a distilled summary of a motif, as well as the statistical justification.

Footnotes

* http://wanglab.ucsd.edu/star/motto