An In-place Framework for Exact and Approximate Shortest Unique Substring Queries

Hon, Wing-Kai; Thankachan, Sharma V.; Xu, Bojian

doi:10.1007/978-3-662-48971-0_63

Cited by 10 publications

(12 citation statements)

References 7 publications

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“…The idea of their 78 work is to reduce the position interval-based SUS query 79 problem to the well-known range minimum queries from 80 computational geometry. Hon et al [8], [9] presented a new 81 approach that allows in-place computation of k-mismatch 82 SUS-a new type of SUS queries they proposed. Their in-83 place algorithms still keep the OðnÞ time cost for exact SUS 84 queries (k ¼ 0), while having to spend Oðn 2 Þ time cost for 85 approximate SUS queries where mismatches are consid-86 ered.…”

Section: Prior Workmentioning

confidence: 99%

Parallel Methods for Finding k-Mismatch Shortest Unique Substrings Using GPU

Schultz

2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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k-mismatch shortest unique substring (SUS) queries have been proposed and studied very recently due to its useful 5 applications in the subfield of computational biology. The k-mismatch SUS query over one given position of a string asks for a shortest 6 substring that covers the given position and does not have a duplicate (within a Hamming distance of k) elsewhere in the string. The 7 challenge in SUS query is to collectively find the SUS for every position of a massively long string in a both time-and space-efficient 8 manner. All known efforts and results have been focused on improving and optimizing the time and space efficiency of SUS 9 computation in the sequential CPU model. In this work, we propose the first parallel approach for k-mismatch SUS queries, particularly 10 leveraging on the massive multi-threading architecture of the graphic processing unit (GPU) technology. Experimental study performed 11 on a mid-end GPU using real-world biological data shows that our proposal is consistently faster than the fastest CPU solution by a 12 factor of at least 6 for exact SUS queries (k ¼ 0) and at least 23 for approximate SUS queries over DNA sequences (k > 0), while 13 maintaining nearly the same peak memory usage as the most memory-efficient sequential CPU proposal. Our work provides 14 practitioners a faster tool for SUS finding on massively long strings, and indeed provides the first practical tool for approximate SUS 15 computation, because the any-case quadratical time cost of the state-of-the-art sequential CPU method for approximate SUS queries 16 does not scale well even to modestly long strings.

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Section: Prior Workmentioning

confidence: 99%

Parallel Methods for Finding k-Mismatch Shortest Unique Substrings Using GPU

Schultz

2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…We use the following two sets containing interval and point SUSs, which were defined at the beginning of the introduction: Given an interval [s, t] ⊂ [1, n], [4,5], [5,8], [6,9], [7,11], [10,12], [13,14] [6,9], and rmMUS p T = [6,9].…”

Section: Muss and Sussmentioning

confidence: 99%

Compact Data Structures for Shortest Unique Substring Queries

Mieno

Köppl

Nakashima

et al. 2019

String Processing and Information Retrieval

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Given a string T of length n, a substring u = T [i..j] of T is called a shortest unique substring (SUS) for an interval [s, t] if (a) u occurs exactly once in T , (b) u contains the interval [s, t] (i.e. i ≤ s ≤ t ≤ j), and (c) every substring v of T with |v| < |u| containing [s, t] occurs at least twice in T . Given a query interval [s, t] ⊂ [1, n], the interval SUS problem is to output all the SUSs for the interval [s, t].In this article, we propose a 4n+o(n) bits data structure answering an interval SUS query in output-sensitive O(occ) time, where occ is the number of returned SUSs. Additionally, we focus on the point SUS problem, which is the interval SUS problem for s = t. Here, we propose a ⌈(log 2 3 + 1)n⌉ + o(n) bits data structure answering a point SUS query in the same output-sensitive time.

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“…Let S = acbaaabcbcbcbaab. SUPS for interval [6,7] is the S[3..7] = baaab. SUPS for interval [7,8] In this paper, we tackle the following problem.…”

Section: Mups S Sups S and Our Problemmentioning

confidence: 99%

“…They also showed optimal O(n)-time preprocessing and O(k) query time algorithms which return all SUS s for any given position where k is the number of outputs. Moreover, Hon et al [6] proposed an in-place algorithm which returns a SUS . A more general problem called interval SUS problem, where a query is an interval, was considered by Hu et al [7].…”

Section: Introductionmentioning

confidence: 99%

Shortest Unique Palindromic Substring Queries in Optimal Time

Nakashima

Inoue

Mieno

et al. 2018

Lecture Notes in Computer Science

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A palindrome is a string that reads the same forward and backward. A palindromic substring P of a string S is called a shortest unique palindromic substring (SUPS ) for an interval [s, t] in S, if P occurs exactly once in S, this occurrence of P contains interval [s, t], and every palindromic substring of S which contains interval [s, t] and is shorter than P occurs at least twice in S. The SUPS problem is, given a string S, to preprocess S so that for any subsequent query interval [s, t] all the SUPS s for interval [s, t] can be answered quickly. We present an optimal solution to this problem. Namely, we show how to preprocess a given string S of length n in O(n) time and space so that all SUPS s for any subsequent query interval can be answered in O(α + 1) time, where α is the number of outputs.

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An In-place Framework for Exact and Approximate Shortest Unique Substring Queries

Cited by 10 publications

References 7 publications

Parallel Methods for Finding k-Mismatch Shortest Unique Substrings Using GPU

Parallel Methods for Finding k-Mismatch Shortest Unique Substrings Using GPU

Compact Data Structures for Shortest Unique Substring Queries

Shortest Unique Palindromic Substring Queries in Optimal Time

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