Abstract
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.
| Original language | English |
|---|---|
| Pages (from-to) | 403-414 |
| Number of pages | 12 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jul 2007 |
Bibliographical note
© Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Keywords
- evolutionary computation
- population-based data clustering
- motif discovery
- transcription factor binding sites
- muscle specific gene expression
- FACTOR-BINDING SITES
- TRANSCRIPTIONAL REGULATION
- SEQUENCE