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An efficient parallel approach for identifying protein families in large-scale metagenomic data sets

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2 Author(s)
Changjun Wu ; Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA ; Kalyanaraman, A.

Metagenomics is the study of environmental microbial communities using state-of-the-art genomic tools. Recent advancements in high-throughput technologies have enabled the accumulation of large volumes of metagenomic data that was until a couple of years back was deemed impractical for generation. A primary bottleneck, however, is in the lack of scalable algorithms and open source software for large-scale data processing. In this paper, we present the design and implementation of a novel parallel approach to identify protein families from large-scale metagenomic data. Given a set of peptide sequences we reduce the problem to one of detecting arbitrarily-sized dense subgraphs from bipartite graphs. Our approach efficiently parallelizes this task on a distributed memory machine through a combination of divide-and-conquer and combinatorial pattern matching heuristic techniques. We present performance and quality results of extensively testing our implementation on 160 K randomly sampled sequences from the CAMERA environmental sequence database using 512 nodes of a BlueGene/L supercomputer.

Published in:

High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for

Date of Conference:

15-21 Nov. 2008