Graphical approach to weak motif recognition in noisy data sets

Abstract

Accurate recognition of motifs in biological sequences has become a central problem in computational biology. Though previous approaches have shown reasonable performances in detecting motifs having clear consensus, they are inapplicable to the recognition of weak motifs in noisy datasets, where only a fraction of the sequences may contain motif instances. This paper presents a graphical approach to deal with the real biological sequences, which are noisy in nature, and find potential weak motifs in the higher eukaryotic datasets. We examine our approach on synthetic datasets embedded with the degenerate motifs and show that it outperforms the earlier techniques. Moreover, the present approach is able to find the wet-lab proven motifs and other unreported significant consensus in real biological datasets. © Springer-Verlag Berlin Heidelberg 2006.