Improved Algorithms for Finding Edit Distance Based Motifs

Motif search is an important step in extracting meaningful patterns from biological data. Since the general problem of motif search is intractable, there is a pressing need to develop efficient exact and approximation algorithms to solve this problem. We design novel algorithms for solving the Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input strings with at most d substitutions, insertions and deletions. These algorithms have been evaluated on several challenging instances. Our algorithm solves a moderately hard instance (11,3) in a couple of minutes and the next difficult instance (14,3) in a couple of hours whereas the best previously known algorithm, EMS1, solves (11,3) in a few hours and does not solve (13,4) even after 3 days. This significant improvement is due to a novel and provably efficient neighborhood generation technique introduced in this paper. This efficient approach can be used in other edit distance based applications in Bioinformatics, such as k-spectrum based sequence error correction algorithms. We also use a trie based data structure to efficiently store the candidate motifs in the neighbourhood and to output the motifs in a sorted order