Affiliations: Bioinformatics Team, Scientific and Engineering
Computing Group, Centre for Development of Advanced Computing, Pune University
Campus, Ganeshkhin, Pune - 411007, India. Tel.: +91 20 5694 084; Fax: +91 20
5694 081; E-mail: rajendra@cdac.ernet.in
Abstract: 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.
