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Fast and sensitive taxonomic classification for metagenomics with Kaiju

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Menzel, Peter, Ng, Kim Lee, Krogh, Anders·2016·Nature Communications

Abstract

Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic k -mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k -mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at http://kaiju.binf.ku.dk .

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Metadata

Authors
Menzel, P., Ng, K. L., & Krogh, A.
Venue
Nature Communications
Published
2016-04-13
Volume
7
Issue
1
Pages
11257-11257
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2337747100
Cited by
2,118
References
28
FWCI
40.33
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Fast and sensitive taxonomic classification for metagenomics with Kaiju. https://app.cochat.ai/papers/10.1038/ncomms11257
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Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications. https://doi.org/10.1038/ncomms11257

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