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Fast and sensitive taxonomic classification for metagenomics with Kaiju
Open accessAbstract
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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Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201
Aligning sequence reads, clone sequences and assembly con*gs with BWA-MEM
Li, H. (2014) · arXiv (Cornell University) · doi:10.6084/m9.figshare.963153.v1 · Cited by 4,932
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521
Structure and function of the global ocean microbiome
Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J. R., Labadie, K., Salazar, G., Djahanschiri, B., Zeller, G., Mende, D. R., Alberti, A., et al. (2015) · Science · doi:10.1126/science.1261359 · Cited by 2,921
A Block-sorting Lossless Data Compression Algorithm
Burrows, M. T., & Wheeler, D. J. (1994) · Cited by 2,333
Interactive metagenomic visualization in a Web browser
Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-385 · Cited by 2,144
ART: a next-generation sequencing read simulator
Huang, W., Li, L., Myers, J. R., & Marth, G. T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr708 · Cited by 1,596
The oral microbiome in health and disease
Wade, W. G. (2013) · Pharmacological Research · doi:10.1016/j.phrs.2012.11.006 · Cited by 1,347
Metagenomics: Genomic Analysis of Microbial Communities
Riesenfeld, C. S., Schloss, P. D., & Handelsman, J. (2004) · Annual Review of Genetics · doi:10.1146/annurev.genet.38.072902.091216 · Cited by 1,129
Opportunistic data structures with applications
Ferragina, P., & Manzini, G. (2002) · Proceedings 41st Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2000.892127 · Cited by 1,063
Next‐generation sequencing technologies for environmental DNA research
SHOKRALLA, S., SPALL, J. L., GIBSON, J. F., & HAJIBABAEI, M. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05538.x · Cited by 925
Ounit, R., Wanamaker, S., Close, T. J., & Lonardi, S. (2015) · BMC Genomics · doi:10.1186/s12864-015-1419-2 · Cited by 632
Zhao, Y., Tang, H., & Ye, Y. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr595 · Cited by 418
An evaluation of the accuracy and speed of metagenome analysis tools
Lindgreen, S., Adair, K. L., & Gardner, P. P. (2016) · Scientific Reports · doi:10.1038/srep19233 · Cited by 331
Computational meta'omics for microbial community studies
Segata, N., Boernigen, D., Tickle, T. L., Morgan, X. C., Garrett, W. S., & Huttenhower, C. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.22 · Cited by 326
Comparative Genomic Structure of Prokaryotes
Bentley, S. D., & Parkhill, J. (2004) · Annual Review of Genetics · doi:10.1146/annurev.genet.38.072902.094318 · Cited by 312
Tools for mapping high-throughput sequencing data
Fonseca, N. A., Rung, J., Brazma, A., & Marioni, J. C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts605 · Cited by 277
Scalable metagenomic taxonomy classification using a reference genome database
Ames, S. K., Hysom, D. A., Gardner, S. N., Lloyd, G. S., Gokhale, M. B., & Allen, J. E. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt389 · Cited by 198
uShuffle: A useful tool for shuffling biological sequences while preserving the k-let counts
Jiang, M., Anderson, J., Gillespie, J., & Mayne, M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-192 · Cited by 175
Bioprospecting metagenomes: glycosyl hydrolases for converting biomass
Li, L.-L., McCorkle, S. R., Monchy, S., Taghavi, S., & van der Lelie, D. (2009) · Biotechnology for Biofuels · doi:10.1186/1754-6834-2-10 · Cited by 170
Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning
Cleary, B., Brito, I., Huang, K. H., Gevers, D., Shea, T., Young, S., & Alm, E. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3329 · Cited by 150
The human gut microbiome: Implications for future health care
Kinross, J. M., von Roon, A. C., Holmes, E., Darzi, A., & Nicholson, J. K. (2008) · Current Gastroenterology Reports · doi:10.1007/s11894-008-0075-y · Cited by 137
Comparative Metagenomics of Eight Geographically Remote Terrestrial Hot Springs
Menzel, P., Gudbergsdóttir, S. R., Rike, A. G., Lin, L., Zhang, Q., Contursi, P., Moracci, M., Kristjánsson, J. K., Bolduc, B., Gavrilov, S. N., et al. (2015) · Microbial Ecology · doi:10.1007/s00248-015-0576-9 · Cited by 125
Self-citationLong read alignment based on maximal exact match seeds
Liu, Y., & Schmidt, B. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts414 · Cited by 112
Frellsen, J., Menzel, P., & Krogh, A. (2014) · Elsevier eBooks · doi:10.1016/b978-0-444-53632-7.01103-5 · Cited by 5
Self-citation
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Next-Generation Sequencing Technology: Current Trends and Advancements
Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., Das, G., et al. (2023) · Biology · doi:10.3390/biology12070997 · Cited by 1,299
Zheng, P., Zeng, B., Liu, M., Chen, J., Pan, J., Han, Y., Liu, Y., Cheng, K., Zhou, C., Wang, H., et al. (2019) · Science Advances · doi:10.1126/sciadv.aau8317 · Cited by 704
Benchmarking Metagenomics Tools for Taxonomic Classification
Ye, S. H., Siddle, K. J., Park, D. J., & Sabeti, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.07.010 · Cited by 593
A review of methods and databases for metagenomic classification and assembly
Breitwieser, F. P., Lu, J., & Salzberg, S. L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbx120 · Cited by 566
Kalantar, K. L., Carvalho, T., de Bourcy, C. F. A., Dimitrov, B., Dingle, G., Egger, R., Han, J., Holmes, O. B., Juan, Y.-F., King, R., et al. (2020) · GigaScience · doi:10.1093/gigascience/giaa111 · Cited by 406
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
- gold
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