Fast and sensitive taxonomic classification for metagenomics with Kaiju

By Peter Menzel, K. Ng, Anders Krogh. Nature Communications, 2016

1,744 citations recorded by the source corpus.

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. Here, Anders Krogh and colleagues describe Kaiju, a metagenome taxonomic classification program that uses maximum (in-)exact matches on the protein-level to account for evolutionary divergence. The authors show that Kaiju performs faster and is more sensitive compared with existing algorithms and can be used on a standard computer.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7cd164015a1f98d243ce3ab25c7db28b85cb64bd

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In Ecology

1. Florian P. Breitwieser
2. Jennifer Lu
3. Steven L. Salzberg
4. B. Duarte
5. Joao Marques
6. A. Thompson
7. Bas E. Dutilh
8. Luke R. Thompson

In Molecular Biology

1. Jennifer Lu
2. Florian P. Breitwieser
3. Steven L. Salzberg
4. B. Duarte
5. Joao Marques
6. A. Thompson
7. Bas E. Dutilh
8. Luke R. Thompson

Citations in the corpus

• A review of methods and databases for metagenomic classification and assembly (2019, methodology): “using FM-index with reduced amino acid alphabet [69] https://github.”
• Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT (2019, positive): “We compared classification by CAT to (1) the recently published LAST+MEGAN-LR algorithm 17 , (2) the widely used Kaiju algorithm 13 , and (3) a standard best-hit approach with Diamond 22 (Figure 3).”
• Next-Generation Sequencing Technology: Current Trends and Advancements (2023, positive): “Shotgun Metagenomics Taxonomic classification MetaPhlAn4 [158], Kaiju [159], Kraken [160] Assembly of metagenomic reads metaSPAdes [86], metaIDBA [87] Protein databases for taxonomic classification NCBI non-redundant protein database [83] Gene annotation Prokka [88], MetaGeneMark [89] Databases for ”
• DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, methodology): “Bioinformatic tools to aid microbial classification include similarity based approaches (Liu et al., 2011; Menzel et al., 2016), phylogenetic placement of sequences to known reference trees (Matsen et al.”