PT  - JOURNAL ARTICLE
AU  - FP Breitwieser
AU  - SL Salzberg
TI  - KrakenHLL: Confident and fast metagenomics classification using unique k-mer counts
AID  - 10.1101/262956
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 262956
4099  - http://biorxiv.org/content/early/2018/06/06/262956.short
4100  - http://biorxiv.org/content/early/2018/06/06/262956.full
AB  - False positive identifications are a significant problem in metagenomic classification. We present KrakenHLL, a novel metagenomic 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, KrakenHLL gives better recall and F1-scores 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 (HLL), KrakenHLL is as fast as Kraken and requires little additional memory.