GAMID: using genetic algorithms for the inference of DNA motifs that are represented in only a subset of sequences of interest

In this work, we present GAMID, an extension of GAMI (Genetic Algorithms for Motif Inference), which allows the system to ignore some of the sequences when looking for candidate conserved motifs in noncoding DNA. This ability is useful both when looking for candidate motifs in co-expressed genes (where it is not expected that all genes respond to the same transcription factors) and when looking for candidate motifs in divergent species (where functional elements might appear only in related species). In these cases, we would like to allow the inferred motif to be present in only a subset of the input data. This paper provides background information about the problem, describes our approach, and presents results. By excluding some sequences from the match process, GAMID succeeds at finding known functional elements.