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MONI Can Find k-MEMs

Authors Igor Tatarnikov , Ardavan Shahrabi Farahani, Sana Kashgouli, Travis Gagie

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Author Details

Igor Tatarnikov
  • Dalhousie University, Halifax, Canada
Ardavan Shahrabi Farahani
  • Dalhousie University, Halifax, Canada
Sana Kashgouli
  • Dalhousie University, Halifax, Canada
Travis Gagie
  • Dalhousie University, Halifax, Canada

Funding

This research was supported by National Institutes of Health (NIH) NIAID (grant no. HG011392), the National Science Foundation NSF IIBR (grant no. 2029552), a Natural Science and Engineering Research Council (NSERC) Discovery Grant (grant no. RGPIN-07185-2020) and the Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) through the Centre for Biotechnology and Bioengineering (grant CeBiB FB0001).

Acknowledgements

The authors thank Christina Boucher, Ben Langmead, Manuel Mattheisen and Massimiliano Rossi for helpful discussions.

Cite AsGet BibTex

Igor Tatarnikov, Ardavan Shahrabi Farahani, Sana Kashgouli, and Travis Gagie. MONI Can Find k-MEMs. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 26:1-26:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CPM.2023.26

Abstract

Suppose we are asked to index a text T [0..n - 1] such that, given a pattern P [0..m - 1], we can quickly report the maximal substrings of P that each occur in T at least k times. We first show how we can add O (r log n) bits to Rossi et al.’s recent MONI index, where r is the number of runs in the Burrows-Wheeler Transform of T, such that it supports such queries in O (k m log n) time. We then show how, if we are given k at construction time, we can reduce the query time to O (m log n).

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
Keywords
  • Compact data structures
  • Burrows-Wheeler Transform
  • run-length compression
  • maximal exact matches

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References

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