KrakenUniq: Confident and fast metagenomics classification using unique k-mer counts

F. P. Breitwieser, D. N. Baker, S. L. Salzberg

Research output: Contribution to journalArticlepeer-review

Abstract

False-positive identifications are a significant problem in metagenomics classification. We present KrakenUniq, a novel metagenomics classifier that combines the fast k-mer-based classification of Kraken with an efficient algorithm for assessing the coverage of unique k-mers found in each species in a dataset. On various test datasets, KrakenUniq gives better recall and precision than other methods and effectively classifies and distinguishes pathogens with low abundance from false positives in infectious disease samples. By using the probabilistic cardinality estimator HyperLogLog, KrakenUniq runs as fast as Kraken and requires little additional memory. KrakenUniq is freely available at https://github.com/fbreitwieser/krakenuniq.

Original languageEnglish (US)
Article number198
JournalGenome biology
Volume19
Issue number1
DOIs
StatePublished - Nov 16 2018

Keywords

  • Infectious disease diagnosis
  • Metagenomics
  • Metagenomics classification
  • Microbiome
  • Pathogen detection

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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