Sequential and parallel algorithms for all-pair k-mismatch maximal common substrings

Identifying long pairwise maximal common substrings among a large set of sequences is a frequently used construct in computational biology, with applications in DNA sequence clustering and assembly. Due to errors made by sequencers, algorithms that can accommodate a small number of differences are o...

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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 144; pp. 68 - 79
Main Authors: Chockalingam, Sriram P., Thankachan, Sharma V., Aluru, Srinivas
Format: Journal Article
Language:English
Published: Elsevier Inc 01.10.2020
Subjects:
Approximate sequence matching
Hamming distance
Parallel algorithms
String algorithms
Suffix trees
String algorithms
Suffix trees
Approximate sequence matching
Hamming distance
Parallel algorithms
ISSN:0743-7315, 1096-0848
Online Access:Get full text
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Summary:Identifying long pairwise maximal common substrings among a large set of sequences is a frequently used construct in computational biology, with applications in DNA sequence clustering and assembly. Due to errors made by sequencers, algorithms that can accommodate a small number of differences are of particular interest. Formally, let D be a collection of n sequences of total length N, ϕ be a length threshold, and k be a mismatch threshold. The goal is to identify and report all k-mismatch maximal common substrings of length at least ϕ over all pairs of strings in D. Heuristics based on seed-and-extend style filtering techniques are often employed in such applications. However, such methods cannot provide any provably efficient run time guarantees. To this end, we present a sequential algorithm with an expected run time of O(NlogkN+occ), where occ is the output size. We then present a distributed memory parallel algorithm with an expected run time of ONplogN+occlogkN using Ologk+1N expected rounds of global communications, under some realistic assumptions, where p is the number of processors. Finally, we demonstrate the performance and scalability of our algorithms using experiments on large high throughput sequencing data. •First and only known sub-quadratic algorithms for the k-mismatch maximal common substring problem.•Sequential algorithm runs in O(NlogkN+occ) expected time, where occ is the output size.•Our distributed memory parallel algorithm runs in ONplogN+occlogkN expected time using Ologk+1N expected communication rounds, where p is the number of processors.•Parallel algorithm is demonstrated with high throughput genomic datasets ranging in size from 18 million to over 270 million reads, on up to 1024 processor cores.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2020.05.018