dMotifGreedy: a novel tool for de novo discovery of DNA motifs with enhanced power of reporting distinct motifs

TL;DR

dMotifGreedy is a new computational tool that improves the detection of multiple distinct DNA motifs by combining local alignment searches with a greedy global optimization approach.

Contribution

It introduces a novel greedy algorithm that enhances the detection of multiple motifs and overcomes local optima issues in de novo DNA motif discovery.

Findings

Competitive performance in true motif detection

Significantly better at detecting multiple motifs

Freely available as a stand-alone program

Abstract

De novo discovery of over-represented DNA motifs is one of the major challenges in computational biology. Although numerous tools have been available for de novo motif discovery, many of these tools are subject to local optima phenomena, which may hinder detection of multiple distinct motifs. A greedy algorithm based tool named dMotifGreedy was developed. dMotifGreedy begins by searching for candidate motifs from pair-wise local alignments of input sequences and then computes an optimal global solution for each candidate motif through a greedy algorithm. dMotifGreedy has competitive performance in detecting a true motif and greatly enhanced performance in detecting multiple distinct true motifs. dMotifGreedy is freely available via a stand-alone program at http://lambchop.ads.uga.edu/dmotifgreedy/download.php.