Motif Discovery from Large Number of Sequences: A Case Study with Disease Resistance Genes in Arabidopsos thaliana

Motif discovery from a set of sequences is a very important problem in biology. Although a lot of research has been done on computational techniques for (sequence) motif discovery, discovering motifs in a large number of sequences still remains challenging. We propose a novel computational framework that combines multiple computational techniques such as pairwise sequence comparison, clustering, HMM based sequence search, motif finding, and block comparisons. We tested this computational framework in its ability to extract motifs from disease resistance genes and candidates in Arabidopsis thaliana genome and discovered all known motifs relating to disease resistance. When the same set of sequences was submitted to MEME and Pratt (motif discovery tools) as a whole without clustering, they failed to detect disease resistance gene motifs. The crucial component in this framework is clustering. Among the benefits of clustering is computational efficiency since the set of sequences are divided into smaller groups using a clustering algorithm.