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Constructing Antidictionaries of Long Texts in Output-Sensitive Space

  • L.A.K. Ayad
  • , G. Badkobeh
  • , G. Fici
  • , A. Héliou
  • , S.P. Pissis

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

A word x that is absent from a word y is called minimal if all its proper factors occur in y. Given a collection of k words y1, … , yk over an alphabet Σ, we are asked to compute the set M{y1,…,yk}ℓ of minimal absent words of length at most ℓ of the collection {y1, … , yk}. The set M{y1,…,yk}ℓ contains all the words x such that x is absent from all the words of the collection while there exist i,j, such that the maximal proper suffix of x is a factor of yi and the maximal proper prefix of x is a factor of yj. In data compression, this corresponds to computing the antidictionary of k documents. In bioinformatics, it corresponds to computing words that are absent from a genome of k chromosomes. Indeed, the set Myℓ of minimal absent words of a word y is equal to M{y1,…,yk}ℓ for any decomposition of y into a collection of words y1, … , yk such that there is an overlap of length at least ℓ − 1 between any two consecutive words in the collection. This computation generally requires Ω(n) space for n = |y| using any of the plenty available O(n) -time algorithms. This is because an Ω(n)-sized text index is constructed over y which can be impractical for large n. We do the identical computation incrementally using output-sensitive space. This goal is reasonable when ∥M{y1,…,yN}ℓ∥=o(n), for all N ∈ [1,k], where ∥S∥ denotes the sum of the lengths of words in set S. For instance, in the human genome, n ≈ 3 × 109 but ∥M{y1,…,yk}12∥≈106. We consider a constant-sized alphabet for stating our results. We show that allMy1ℓ,…,M{y1,…,yk}ℓ can be computed in O(kn+∑N=1k∥M{y1,…,yN}ℓ∥) total time using O(MaxIn+MaxOut) space, where MaxIn is the length of the longest word in {y1, … , yk} and MaxOut=max{∥M{y1,…,yN}ℓ∥:N∈[1,k]}. Proof-of-concept experimental results are also provided confirming our theoretical findings and justifying our contribution.
Original languageEnglish
Pages (from-to)777-797
Number of pages21
JournalTheory of Computing Systems
Volume65
Issue number5
Early online date14 Dec 2020
DOIs
Publication statusPublished - Jul 2021

Bibliographical note

© 2020, The Author(s).

Funding

A preliminary version of this paper was presented at the Data Compression Conference 2019 [] . Gabriele Fici is supported by MIUR-PRIN 2017 project “Algorithms, Data Structures and Combinatorics for Machine Learning”. Open access funding provided by Università degli Studi di Palermo within the CRUI-CARE Agreement.

FundersFunder number
MIUR-PRIN
Ministero dell’Istruzione, dell’Università e della Ricerca2017K7XPAN
Università degli Studi di Palermo

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