Abstract
In this paper, we demonstrate that the performance of a motif discovery algorithm can be significantly improved by embedding it into a novel framework that effectively guides the motif discovery process. The framework is also general enough to allow any statistical motif discovery algorithm to be used. Motivation for this research comes from the fact that the statistical significance of patterns depends on the background probability which is largely determined by input sequences. Our framework guides motif discovery by inputting subsequences to an existing motif discovery algorithm, rather than using entire sequences. Subsequences are determined by motifs discovered using existing motif discovery and search algorithms. Then this technique is iteratively applied until convergence. A starting set of patterns is discovered by a simple, but effective pattern set generation algorithm. Our framework was implemented using MEME and MAST and tested with 108 PROSITE patterns. The result demonstrates that our framework significantly improves the performance of MEME.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.