Journal
GENOME BIOLOGY
Volume 19, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-018-1568-0
Keywords
Metagenomics; Microbiome; Metagenomics classification; Pathogen detection; Infectious disease diagnosis
Funding
- National Institutes of Health [R01-GM083873, R01-HG006677, R01GM118568]
- US Army Research Office [W911NF-14-1-0490]
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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.
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