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Swiftly Identifying Strongly Unique k-Mers

Authors Jens Zentgraf , Sven Rahmann

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Author Details

Jens Zentgraf
  • Algorithmic Bioinformatics, Department of Computer Science, Saarland University, Saarbrücken, Germany
  • Center for Bioinformatics Saar, Saarland Informatics Campus, Saarbrücken, Germany
  • Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany
Sven Rahmann
  • Algorithmic Bioinformatics, Department of Computer Science, Saarland University, Saarbrücken, Germany
  • Center for Bioinformatics Saar, Saarland Informatics Campus, Saarbrücken, Germany

Acknowledgements

We thank Karl Bringmann for discussions about the FourWay algorithm. We also thank the four reviewers of the initial version of this manuscript for their suggestions to improve its presentation.

Cite AsGet BibTex

Jens Zentgraf and Sven Rahmann. Swiftly Identifying Strongly Unique k-Mers. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.WABI.2024.15

Abstract

Motivation. Short DNA sequences of length k that appear in a single location (e.g., at a single genomic position, in a single species from a larger set of species, etc.) are called unique k-mers. They are useful for placing sequenced DNA fragments at the correct location without computing alignments and without ambiguity. However, they are not necessarily robust: A single basepair change may turn a unique k-mer into a different one that may in fact be present at one or more different locations, which may give confusing or contradictory information when attempting to place a read by its k-mer content. A more robust concept are strongly unique k-mers, i.e., unique k-mers for which no Hamming-distance-1 neighbor with conflicting information exists in all of the considered sequences. Given a set of k-mers, it is therefore of interest to have an efficient method that can distinguish k-mers with a Hamming-distance-1 neighbor in the collection from those that do not. Results. We present engineered algorithms to identify and mark within a set K of (canonical) k-mers all elements that have a Hamming-distance-1 neighbor in the same set. One algorithm is based on recursively running a 4-way comparison on sub-intervals of the sorted set. The other algorithm is based on bucketing and running a pairwise bit-parallel Hamming distance test on small buckets of the sorted set. Both methods consider canonical k-mers (i.e., taking reverse complements into account) and allow for efficient parallelization. The methods have been implemented and applied in practice to sets consisting of several billions of k-mers. An optimized combined approach running with 16 threads on a 16-core workstation, yields wall-clock running times below 20 seconds on the 2.5 billion distinct 31-mers of the human telomere-to-telomere reference genome. Availability. An implementation can be found at https://gitlab.com/rahmannlab/strong-k-mers.

Subject Classification

ACM Subject Classification
  • Applied computing → Molecular sequence analysis
  • Applied computing → Bioinformatics
  • Theory of computation → Parallel algorithms
  • Theory of computation → Sorting and searching
  • Information systems → Nearest-neighbor search
Keywords
  • k-mer
  • Hamming distance
  • strong uniqueness
  • parallelization
  • algorithm engineering

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