Motif Discovery for Proteins Using Subsequence Clustering

We propose an algorithm for discovering motifs using clustering of subsequences. In our previous approach, we were successful in guiding motif discovery by sampling subsequences and inputting them to an existing motif discovery tool MEME. In this paper, we show that clustering subsequences can also detect motifs without using other motif discovery tools. Generally, motif discovery algorithms do not perform well when the input set consists of nonhomogeneous sequences. Clustering tools have the inherent ability to generate clusters of homogeneous sequences when the input sequences are non-homogeneous. For this reason, we use our clustering algorithm to generate aligned subsequence clusters and then rank them according to their information contents to produce final motifs. The algorithm was tested with PROSITE database and the results suggest that the algorithm is very effective in finding motifs even when input sequences are from different protein families