Mining large-scale repetitive sequences in a MapReduce setting Online publication date: Mon, 22-Feb-2016
by Hongfei Cao; Michael Phinney; Devin Petersohn; Benjamin Merideth; Chi-Ren Shyu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 14, No. 3, 2016
Abstract: Recent research suggests DNA repeats play critical roles in cellular regulatory functions and disease development. The challenge associated with identifying repeats across a collection of genomes arises from the amount of data stored within DNA, and intermediate data generated by alignment- and hash-based approaches are substantial. We present a MapReduce-based method for repeat identification and propose efficient storage and search techniques. Our approach distributes the computation and storage across a cluster of commodity computers, lending a cost-effective, flexible, robust, and scalable solution to the challenge of identifying various types of repetitive sequences across a collection of genomes. In this study, we benchmark our method using a collection of six genomes, totalling approximately 14.2 billion base pairs. We demonstrate a tenfold speedup over previous state-of-the-art approaches and linear scalability. In addition, we conduct a deeper scalability analysis by processing a collection of 39 genomes, approximately 104 billion base pairs.
Online publication date: Mon, 22-Feb-2016
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