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Faster Run-Length Compressed Suffix Arrays

Authors Nathaniel K. Brown , Travis Gagie , Giovanni Manzini , Gonzalo Navarro , Marinella Sciortino

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ACM Subject Classification
  • Theory of computation → Pattern matching
Keywords
  • Run-length compressed suffix arrays
  • interpolative coding
  • two-level indexing

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Abstract

We first review how we can store a run-length compressed suffix array (RLCSA) for a text T of length n over an alphabet of size σ whose Burrows-Wheeler Transform (BWT) consists of r runs in O (r log (n / r) + r log σ + σ) bits such that later, given character a and the suffix-array (SA) interval for P, we can find the SA interval for a P in O (log r_a + log log n) time, where r_a is the number of runs of copies of a in the BWT. We then show how to modify the RLCSA such that we find the SA interval for a P in only O (log r_a) time, without increasing its asymptotic space bound. Our key idea is applying a result by Nishimoto and Tabei (ICALP 2021) and then replacing rank queries on sparse bitvectors by a constant number of select queries. We also review two-level indexing and discuss how our faster RLCSA may be useful in improving it. Finally, we briefly discuss how two-level indexing may speed up a recent heuristic for finding maximal exact matches of a pattern with respect to an indexed text.

Cite As Get BibTex

Nathaniel K. Brown, Travis Gagie, Giovanni Manzini, Gonzalo Navarro, and Marinella Sciortino. Faster Run-Length Compressed Suffix Arrays. In From Strings to Graphs, and Back Again: A Festschrift for Roberto Grossi's 60th Birthday. Open Access Series in Informatics (OASIcs), Volume 132, pp. 10:1-10:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.Grossi.10

Author Details

Nathaniel K. Brown
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Travis Gagie
  • Faculty of Computer Science, Dalhousie University, Halifax, Canada
Giovanni Manzini
  • Department of Computer Science, University of Pisa, Italy
Gonzalo Navarro
  • Department of Computer Science, University of Chile, Santiago, Chile
Marinella Sciortino
  • Department of Mathematics and Computer Science, University of Palermo, Italy

Funding

  • Brown, Nathaniel K.: Supported in part by a Johns Hopkins University Computer Science PhD Fellowship and NIH grants R21HG013433 and R01HG011392.
  • Gagie, Travis: Funded in part by NSERC grant RGPIN-07185-2020.
  • Manzini, Giovanni: Partially supported by the NextGeneration EU programme PNRR ECS00000017 Tuscany Health Ecosystem (Spoke 6, CUP: I53C22000780001) and by the project PAN-HUB funded by the Italian Ministry of Health (ID: T4-AN-07, CUP: I53C22001300001).
  • Navarro, Gonzalo: Funded in part by CeBiB under Basal Funds FB0001 and AFB240001, ANID, Chile.
  • Sciortino, Marinella: Partially supported by the project "ACoMPA" (CUP B73C24001050001) funded by the NextGeneration EU programme PNRR ECS00000017 Tuscany Health Ecosystem (Spoke 6) and by MUR PRIN project "PINC" (no. 2022YRB97K).

Acknowledgements

Many thanks to the first author’s CSCI 4419 / 6106 students Anas Alhadi, Nour Allam, Dove Begleiter, Nithin Bharathi Kabilan Karpagavalli, Suchith Sridhar Khajjayam and Hamza Wahed, and to Christina Boucher, Ben Langmead, Jouni Sirén and Mohsen Zakeri.

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