Motif search using Gibbs sampling: Notes on effectiveness in a distributed environment

Motif search is a common problem in bioinformatics where unique DNA sequences (motifs) of a specific length inscribed in long strands signify binding sites for transcription factors. In this paper, we present some important notes on the implementation of motif search using Gibbs sampling algorithm in a distributed computing environment by analyzing visualization on speed and motif scoring of various distributed implementations. For the DNA sequences data, we used an open-source mouse genome fragments with lengths 250, 500, and 1000. We built upon our previous studies (Perera and Ragel, 2013; Chen and Jiang, 2006) by integrating a distributed environment of the motif search workloads (jobs) across 16 CPU cores contained on 2 computer nodes instead of the traditional way of parallelizing on a single computing device with multicore CPUs. Results show that using saving the DNA sequences in list and adding as a parameter argument obtained the fastest execution time compared to implementations by sending file dependencies and in-memory processing.