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

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.