Accelerating comparative genomics using parallel computing

In the past decade there has been an increase in the number of completely sequenced genomes due to the race of multibillion-dollar genome-sequencing projects. The enormous biological sequence data thus flooding into the sequence databases necessitates the development of efficient tools for comparative genome sequence analysis. The information deduced by such analysis has various applications viz. structural and functional annotation of novel genes and proteins, finding gene order in the genome, gene fusion studies, constructing metabolic pathways etc. Such study also proves invaluable for pharmaceutical industries, such as in silico drug target identification and new drug discovery. There are various sequence analysis tools available for mining such useful information of which FASTA and Smith-Waterman algorithms are widely used. However, analyzing large datasets of genome sequences using the above codes seems to be impractical on uniprocessor machines. Hence there is a need for improving the performance of the above popular sequence analysis tools on parallel cluster computers. Performance of the Smith-Waterman (SSEARCH) and FASTA programs were studied on PARAM 10000, a parallel cluster of workstations designed and developed in-house. FASTA and SSEARCH programs, which are available from the University of Virginia, were ported on PARAM and were optimized. In this era of high performance computing, where the paradigm is shifting from conventional supercomputers to the cost-effective general-purpose cluster of workstations and PCs, this study finds extreme relevance. Good performance of sequence analysis tools on a cluster of workstations was demonstrated, which is important for accelerating identification of novel genes and drug targets by screening large databases.