Parallel computation in biological sequence analysis

Abstract

A massive volume of biological sequence data is available in over 36 different databases worldwide, including the sequence data generated by the Human Genome project. These databases, which also contain biological and bibliographical information, are growing at an exponential rate. Consequently, the computational demands needed to explore and analyze the data contained in these databases is quickly becoming a great concern. To meet these demands, we must use high performance computing systems, such as parallel computers and distributed networks of workstations. We present two parallel computational methods for analyzing these biological sequences. The first method is used to retrieve sequences that are homologous to a query sequence. The biological information associated with the homologous sequences found in the database may provide important clues to the structure and function of the query sequence. The second method, which helps in the prediction of the function, structure, and evolutionary history of biological sequences, is used to align a number of homologous sequences with each other. These two parallel computational methods were implemented and evaluated on an Intel iPSC/860 parallel computer. The resulting performance demonstrates that parallel computational methods can significantly reduce the computational time needed to analyze the sequences contained in large databases.

Whole Genome Comparison on a Network of Workstations

“…A slightly modified version of the bucket method suggested by Yap, Frieder and Martino ( [14]) which is a combination of the static allocation portion method and dynamic allocation masterworker method is utilized here (though in the future we plan to experiment with dynamic allocation methods). Yap defines the percentage of load imbalance (PLIB) as the time difference between the fastest finishing and slowest finishing workstations.…”

BioBench: A Benchmark Suite of Bioinformatics Applications

“…Most of the published work on performance studies of bioinformatics workloads involves either performance optimization of established algorithms, or analysis of the performance of such algorithms on parallel systems. Yap et al [19] present a detailed study of parallel sequence searching. Catalyurek et al [5] analyze performance of specific applications on a centralized-server, multiclient environment.…”

Improving performance of multiple sequence alignment analysis in multi-client environments

“…A large number of methods have been developed for fast and accurate multiple sequence alignments. Several research projects have focused on the development of heuristics [9,11,13,16,17], and the parallel implementations of MSA algorithms [12,18].…”