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Exact Sketch-Based Read Mapping

Authors Tizian Schulz , Paul Medvedev

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Author Details

Tizian Schulz
  • Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany
  • Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Bielefeld University, Germany
  • Graduate School "Digital Infrastructure for the Life Sciences" (DILS), Bielefeld University, Germany
Paul Medvedev
  • Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA
  • Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
  • Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA

Funding

This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A).
  • Schulz, Tizian: This research is funded in part by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie agreement [872539].
  • Medvedev, Paul: This material is based upon work supported by the National Science Foundation under grant nos. 2138585. Research reported in this publication was also supported by the National Institutes of Health under Grant NIH R01GM146462 (to P.M.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Acknowledgements

We would like to thank K. Sahlin for helpful early feedback.

Cite AsGet BibTex

Tizian Schulz and Paul Medvedev. Exact Sketch-Based Read Mapping. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.WABI.2023.14

Abstract

Given a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a "similar sequence". Traditionally, "similar sequence" was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold. In this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in {O}(|t| + |p| + 𝓁²) time and Θ(𝓁²) space, where |t| is the number of k-mers inside the sketch of the reference, |p| is the number of k-mers inside the read’s sketch and 𝓁 is the number of times that k-mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • Sequence Sketching
  • Long-read Mapping
  • Exact Algorithm
  • Dynamic Programming

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Supplementary Materials

References

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