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Parallel metagenomic sequence clustering via sketching and maximal quasi-clique enumeration on map-reduce clouds
| Venue: | in Proc. IEEE Int. Parallel and Distributed Processing Symposium, 2011 |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Yang_parallelmetagenomic,
author = {Xiao Yang and Jaroslaw Zola and Srinivas Aluru},
title = {Parallel metagenomic sequence clustering via sketching and maximal quasi-clique enumeration on map-reduce clouds},
booktitle = {in Proc. IEEE Int. Parallel and Distributed Processing Symposium, 2011},
year = {},
pages = {1223--1233}
}
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Abstract
Abstract—Taxonomic clustering of species is an impor-tant and frequently arising problem in metagenomics. High-throughput next generation sequencing is facilitating the creation of large metagenomic samples, while at the same time making the clustering problem harder due to the short sequence length supported and unknown species sampled. In this paper, we present a parallel algorithm for hierarchical tax-onomic clustering of large metagenomic samples with support for overlapping clusters. We adapt the sketching techniques originally developed for web document clustering to deduce significant similarities between pairs of sequences without resorting to expensive all vs. all alignments. We formulate the metagenomics classification problem as that of maximal quasi-clique enumeration in the resulting similarity graph, at multiple levels of the hierarchy as prescribed by different similarity thresholds. We cast execution of the underlying algorithmic steps as applications of the map-reduce framework to achieve a cloud based implementation. Apart from solving an important problem in metagenomics, this work demonstrates the appli-cability of map-reduce framework in relatively complicated algorithmic settings. Keywords- metagenomics; sequence clustering; sketching; quasi clique enumeration; cloud computing; MapReduce; next generation sequencing. I.
Keyphrases
maximal quasi-clique enumeration map-reduce cloud parallel metagenomic sequence large metagenomic sample map-reduce framework short sequence length next generation abstract taxonomic clustering algorithmic setting different similarity threshold clique enumeration multiple level important problem resulting similarity graph sequence clustering keywords metagenomics high-throughput next generation sequencing clustering problem harder unknown specie significant similarity web document underlying algorithmic step parallel algorithm cloud computing metagenomics classification problem hierarchical tax-onomic clustering
