Efficient Computation of Sequence Mappability

• Published in: Algorithmica Vol. 84; no. 5; pp. 1418 - 1440
• Main Authors: Charalampopoulos, Panagiotis, Iliopoulos, Costas S., Kociumaka, Tomasz, Pissis, Solon P., Radoszewski, Jakub, Straszyński, Juliusz
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2022; Springer Nature B.V; Springer Verlag
• Subjects: Algorithm Analysis and Problem Complexity; Algorithms; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Mathematics of Computing; Theory of Computation; errata tree; Sequence mappability; Hamming distance
• ISSN: 0178-4617; 1432-0541; 1432-0541
• DOI: 10.1007/s00453-022-00934-y

Sequence mappability is an important task in genome resequencing. In the ( k ,  m )-mappability problem, for a given sequence T of length n , the goal is to compute a table whose i th entry is the number of indices j ≠ i such that the length- m substrings of T starting at positions i and j have at most k mismatches. Previous works on this problem focused on heuristics computing a rough approximation of the result or on the case of k = 1 . We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that, for k = O ( 1 ) , works in O ( n ) space and, with high probability, in O ( n · min { m k , log k n } ) time. Our algorithm requires a careful adaptation of the k -errata trees of Cole et al. [STOC 2004] to avoid multiple counting of pairs of substrings. Our technique can also be applied to solve the all-pairs Hamming distance problem introduced by Crochemore et al. [WABI 2017]. We further develop O ( n 2 ) -time algorithms to compute all ( k ,  m )-mappability tables for a fixed m and all k ∈ { 0 , … , m } or a fixed k and all m ∈ { k , … , n } . Finally, we show that, for k , m = Θ ( log n ) , the ( k ,  m )-mappability problem cannot be solved in strongly subquadratic time unless the Strong Exponential Time Hypothesis fails. This is an improved and extended version of a paper presented at SPIRE 2018.