Abstract

We present an efficient parallel algorithm for the following problem: Given an input collection D of n sequences of total length N, a length threshold f and a mismatch threshold κ, report all κ-mismatch maximal common substrings of length at least f over all pairs of strings in D. This problem is motivated by clustering and assembly applications in computational biology, where D is a collection of millions of short DNA sequences. Sequencing errors and massive size of these datasets necessitate efficient parallel approximate sequence matching algorithms. We present a novel distributed memory parallel algorithm that solves this approximate sequence matching problem in O ((N/p log N + occ)logk N) expected time and takes only O(logk+1 N) expected rounds of global communications, under some realistic assumptions, where p is the number of processors and occ is the output size. To our knowledge, this is the first provably sub-quadratic time algorithm for solving this problem. We demonstrate the performance and scalability of our algorithm using large high throughput sequencing data sets.

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