KrakenUniq: confident and fast metagenomics classification using unique k-mer counts
By Florian P. Breitwieser, Daniel N. Baker, Steven L. Salzberg. Genome Biology, 2018
412 citations recorded by the source corpus.
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.
Subfields: Food Science, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/1fdcec465ca287517b882494c7d31bf9fe78ffd1
How researchers see this paper
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In Food Science
In Molecular Biology
- Florian P. Breitwieser
- Steven L. Salzberg
- Jennifer Lu
- Ben Langmead
- Derrick E. Wood
- Martin Steinegger
- Heng Li
- Natalia Rincon
Citations in the corpus
- Metagenome analysis using the Kraken software suite (2022, methodology): “The standard Kraken 2 file outputs lines containing five tab-delimited fields; (1) ‘C’/‘U’ indicating classified or unclassified, (2) sequence ID, (3) taxonomy ID (0 if unclassified), (4) length of sequence in base pairs and (5) a space-delimited list indicating the lowest common ancestor mapping of”
- Improved metagenomic analysis with Kraken 2 (2019, methodology): “[2], Kraken 1 [4], and KrakenUniq [5]) and a translated search classifier (Kaiju [3]).”