  Discovery of transcription factor binding sites is a much explored and still
exploring area of research in functional genomics. Many computational tools
have been developed for finding motifs and each of them has their own
advantages as well as disadvantages. Most of these algorithms need prior
knowledge about the data to construct background models. However there is not a
single technique that can be considered as best for finding regulatory motifs.
This paper proposes an artificial immune system based algorithm for finding the
transcription factor binding sites or motifs and two new weighted scores for
motif evaluation. The algorithm is enumerative, but sufficient pruning of the
pattern search space has been incorporated using immune system concepts. The
performance of AISMOTIF has been evaluated by comparing it with eight state of
art composite motif discovery algorithms and found that AISMOTIF predicts known
motifs as well as new motifs from the benchmark dataset without any prior
knowledge about the data.
