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Finimap: Fast and Accurate Single-Species Bacterial Pseudoalignment with Finimizers

Authors Jarno N. Alanko , Elena Biagi , Simon J. Puglisi

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Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Theory of computation → Data structures design and analysis
  • Theory of computation → Pattern matching
  • Theory of computation → Algorithm design techniques
  • Applied computing → Computational genomics
Keywords
  • Pseudoalignment
  • sequence alignment
  • approximate string matching
  • string processing
  • k-mer
  • data structures
  • data compression

Metrics

Abstract

In recent years, pseudoalignment as a means for mapping reads to databases of reference genomes has become a widely-used method in studies of bacterial pathogenesis. A popular pseudoalignment criterion is thresholded union, in which a read is said to pseudoalign to a reference if the reference contains more than a percentage t of the read’s k-mers. Several pseudoalignment indexing tools that implement this and other pseudoalignment criteria are now available, including Bifrost, Themisto, and Fulgor. In this paper, we describe a scheme for single-species bacterial pseudoalignment that, instead of k-mers, uses shortest unique finimizers (Alanko et al., IEEE/ACM TCBB, 2025) as features for determining pseudoalignment. We show that this scheme, which we call Finimap, leads to a significantly lower false-positive rate than other recent "approximate pseudoalignment" methods Kaminari and Raptor, and is also faster.

Cite As Get BibTex

Jarno N. Alanko, Elena Biagi, and Simon J. Puglisi. Finimap: Fast and Accurate Single-Species Bacterial Pseudoalignment with Finimizers. In 26th International Conference on Algorithms for Bioinformatics (WABI 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 390, pp. 27:1-27:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.WABI.2026.27

Author Details

Jarno N. Alanko
  • Department of Computer Science, University of Helsinki, Finland
Elena Biagi
  • Department of Computer Science, University of Helsinki, Finland
Simon J. Puglisi
  • Department of Computer Science, University of Helsinki, Finland

Funding

This work was supported by the Research Council of Finland via grant 339070.
  • Biagi, Elena: The Finnish Cultural Foundation (grant 00250241) and the Finnish Accademy of Science and Letters (Vilho, Yrjö and Kalle Väisälä Fund).

Acknowledgements

The authors wish to thank the Finnish Computing Competence Infrastructure (FCCI) for supporting this project with computational and data storage resources.

Supplementary Materials

References

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