SSTRAP: A computational model for genomic motif discovery

Abstract

Computational methods can potentially provide high-quality prediction of biological molecules such as DNA binding sites and Transcription factors and therefore reduce the time needed for experimental verification and challenges associated with  experimental methods. These biological molecules or motifs have significant biological functions and are essential to understanding the genomic constitution of organisms which provides an insight into how the organism functions and adapt to changes in the environment. This work presents a novel motif prediction algorithm STTRAP (Suffix Tree Transcription Affinity Prediction) using the suffix tree and transcription affinity process based on the biophysical principle. Applying the SSTRAP model to the Chip-Sequence data of 13 functional groups of genes expressed in the intraerythrocytic developmental cycle of Plasmodium falciparum, resulted in discovering relevant motifs.

Keywords: Pattern Discovery, Motifs, STTRAP, Suffix Tree, Transcription factors, DNA Binding Site