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Genestrip: exact and efficient read classification for selected groups of organisms.

Daniel Pfeifer1, Markus Graf2, Clas Rurik3

  • 1IT Faculty, Heilbronn University, Max-Planck-Str. 39, 74081, Heilbronn, Baden-Württemberg, Germany. daniel.pfeifer@hs-heilbronn.de.

BMC Bioinformatics
|June 17, 2026
PubMed
Summary

Genestrip offers efficient k-mer database creation and metagenomic analysis, significantly reducing memory usage for high-quality read classification. This tool enables accurate analysis even on standard personal computers.

Keywords:
k-Mer countingHigh performanceHigh precisionHigh recallMetagenomicsRead classificationSmall k-mer database

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Metagenomic analysis faces memory constraints with large k-mer databases.
  • Existing tools like KrakenUniq require substantial memory resources.
  • Efficient k-mer database management is crucial for large-scale genomic studies.

Purpose of the Study:

  • To develop a memory-efficient tool for k-mer database creation and metagenomic analysis.
  • To provide high-quality read classification with reduced computational overhead.
  • To enable flexible and customizable k-mer database generation for specific organism groups.

Main Methods:

  • Genestrip focuses on configurable groups of organisms to build k-mer databases.
  • It assigns the most suitable lowest common ancestor taxon for each k-mer, considering external genomes.
  • The tool avoids k-mer compression, thus preventing false positives from information loss.

Main Results:

  • Genestrip produces databases and results comparable to KrakenUniq but with significantly less memory.
  • Databases can cover thousands of species and are generated on a regular PC within hours.
  • The tool achieves high precision and recall in read classification, improving recall with small, deep databases.

Conclusions:

  • Genestrip enables memory-efficient database creation and analysis with favorable runtimes and high classification quality.
  • Users can create small, deep k-mer databases for specific needs, even on regular PCs.
  • The approach enhances read classification accuracy by avoiding k-mer compression-related errors.