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An Efficient Parallel Approach for Identifying Protein Families from Large-scale Metagenomic Data

Authors:
Changjun Wu  (Washington State University)
Ananth Kalyanaraman  (Washington State University)
Papers Session
Biomedical Informatics
Wednesday,  04:00PM - 04:30PM
Room Ballroom E
Abstract:
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 DNA and peptide sequence data. A primary bottleneck, however, is in the lack of scalable algorithms and software solutions 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 ~160K randomly sampled sequences from the CAMERA environmental sequence database using 512 nodes of a BlueGene/L supercomputer.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
   IEEE Computer Society  /  ACM     2 0   Y E A R S   -   U N L E A S H I N G   T H E   P O W E R   O F   H P C