Computing the longest common prefix array based on the Burrows–Wheeler transform

Beller, Timo; Gog, Simon; Ohlebusch, Enno; Schnattinger, Thomas

doi:10.1016/j.jda.2012.07.007

Cited by 57 publications

(63 citation statements)

References 18 publications

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“…The resulting FM-index requires nH 0 (T ) + o(n log σ) bits of space and supports LF-mapping in average t LF = O(H 0 (T )) = O(log σ) time. We build the B lcp bit-vector directly from the wavelet tree in O(nσ) time by adapting the algorithm of [2]. For Lemma 2.5, we use LF-mapping instead of explicitly computing j = SA −1 [i] for each step.…”

Section: Methodsmentioning

confidence: 99%

Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data

Välimäki

Rivals

2013

Bioinformatics Research and Applications

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Philippe et al. (2011) proposed a data structure called Gk arrays for indexing and querying large collections of high-throughput sequencing data in main-memory. The data structure supports versatile queries for counting, locating, and analysing the coverage profile of k-mers in short-read data. The main drawback of the Gk arrays is its space-consumption, which can easily reach tens of gigabytes of mainmemory even for moderate size inputs. We propose a compressed variant of Gk arrays that supports the same set of queries, but in both near-optimal time and space. In practice, the compressed Gk arrays scale up to much larger inputs with highly competitive query * Funded by Academy of Finland CoE in Cancer Genetics Research (No 250345). † Funded by the ANR Colib'read, MASTODONS, PICS.

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Section: Methodsmentioning

confidence: 99%

Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data

Välimäki

Rivals

2013

Bioinformatics Research and Applications

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“…There have been many articles [3,4,5,7,8] about building the Burrows-Wheeler transform (BWT) [9] and the longest common prefix (LCP) array. For ✩ A preliminary version of this work appeared in IWOCA 2015 [2].…”

Section: Introductionmentioning

confidence: 99%

“…Franceschini and Muthukrishnan [20] presented a suffix array construction algorithm that runs in O(n log n) time using constant additional space. The LCP array can be computed in linear time together with SA during the suffix sorting [21,22] or independently given T and SA as input [5,6,7] or given the BWT [23,8]. Table 1 summarizes the most closely related algorithms' bounds.…”

Section: Introductionmentioning

confidence: 99%

Burrows–Wheeler transform and LCP array construction in constant space

Louza

Gagie

Telles

2017

Journal of Discrete Algorithms

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In this article we extend the elegant in-place Burrows-Wheeler transform (BWT) algorithm proposed by Crochemore et al. (Crochemore et al., 2015). Our extension is twofold: we first show how to compute simultaneously the longest common prefix (LCP) array as well as the BWT, using constant additional space; we then show how to build the LCP array directly in compressed representation using Elias coding, still using constant additional space and with no asymptotic slowdown. Furthermore, we provide a time/space tradeoff for our algorithm when additional memory is allowed. Our algorithm runs in quadratic time, as does Crochemore et al.'s, and is supported by interesting properties of the BWT and of the LCP array, contributing to our understanding of the time/space tradeoff curve for building indexing structures.

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“…Some examples where this problem arises are indexes for supporting indexed pattern matching on strings [35,36,28,29,52], indexes for solving computational biology problems on sequences [63,11], simulation of inverted indexes over natural language text collections [14,2], representation Our contribution. In this article we introduce the wavelet matrix.…”

Section: Introductionmentioning

confidence: 99%

The wavelet matrix: An efficient wavelet tree for large alphabets

Claude

Navarro

Ordóñez

2015

Information Systems

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The wavelet tree is a flexible data structure that permits representing sequences S[1, n] of symbols over an alphabet of size σ, within compressed space and supporting a wide range of operations on S. When σ is significant compared to n, current wavelet tree representations incur in noticeable space or time overheads. In this article we introduce the wavelet matrix, an alternative representation for large alphabets that retains all the properties of wavelet trees but is significantly faster. We also show how the wavelet matrix can be compressed up to the zeroorder entropy of the sequence without sacrificing, and actually improving, its time performance. Our experimental results show that the wavelet matrix outperforms all the wavelet tree variants along the space/time tradeoff map.

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Computing the longest common prefix array based on the Burrows–Wheeler transform

Cited by 57 publications

References 18 publications

Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data

Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data

Burrows–Wheeler transform and LCP array construction in constant space

The wavelet matrix: An efficient wavelet tree for large alphabets

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