KrakenUniq: confident and fast metagenomics classification using unique k-mer counts
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 16, 2018
- Source ID
- 10.1186/s13059-018-1568-0
Entities
People
- Daniel N. Baker
- F. P. Breitwieser
- S. L. Salzberg
Organizations
- Army Research Office
- National Human Genome Research Institute
- National Institute of General Medical Sciences