Comparative Analysis of Similarity Check Mechanism for Motif Extraction

In this work, a comparative analysis of the similarity check mechanism used in the most effective algorithm for mining simple motifs GEMS (Gene Enrichment Motif Searching) and that used in a popular multi-objective genetic algorithm, MOGAMOD (Multi-Objective Genetic Algorithm for Motif Discovery) was done. In our previous work, we had reported the implementation of GEMS on suffix tree –Suffix Tree Gene Enrichment Motif Searching (STGEMS) and shown the linear asymptotic runtime achieved. Here, we attempt to empirically proof the high sensitivity of the resulting algorithm, STGEMS in mining motifs from challenging sequences like we have in Plasmodium falciparum. The results obtained validates the high sensitivity of the similarity check mechanism employed in GEMS and also shows that a careful deployment of this mechanism in the multi-objective genetic algorithm, improved the sensiti

SSTRAP: A computational model for genomic motif discovery

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 Sit