FMGA: Finding Motifs by Genetic Algorithm

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BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Year:
2004

Citing 0
Cited 23

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Abstract

In the era of post-genomics, almost all the genes havebeen sequenced and enormous amounts of data havebeen generated. Hence, to mine useful information fromthese data is a very important topic. In this paper wepropose a new approach for finding potential motifs inthe regions located from the -2000 bp upstream to+1000 bp downstream of transcription start site (TSS).This new approach is developed based on the geneticalgorithm (GA). The mutation in the GA is performed byusing position weight matrices to reserve the completelyconserved positions. The crossover is implemented withspecial-designed gap penalties to produce the optimalchild pattern. We also present a rearrangement methodbased on position weight matrices to avoid the presenceof a very stable local minimum, which may make it quitedifficult for the other operators to generate the optimalpattern. Our approach shows superior results bycomparing with Multiple Em for Motif Elicitation(MEME) and Gibbs Sampler, which are two popularalgorithms for finding motifs.