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Improved metagenomic analysis with Kraken 2
Open accessPublished version of a preprint
Abstract
Although Kraken’s k -mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
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Citations
Cites
Minimap2: Pairwise alignment for nucleotide sequences
Li, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty191 · Cited by 14,807
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
Self-citationMetagenomic microbial community profiling using unique clade-specific marker genes
Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. (2012) · Nature Methods · doi:10.1038/nmeth.2066 · Cited by 1,776
Centrifuge: rapid and sensitive classification of metagenomic sequences
Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016) · Genome Research · doi:10.1101/gr.210641.116 · Cited by 1,438
Federhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436
Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen
Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03317-6 · Cited by 559 · cited 1×
“As databases of assembled genomes continue to grow, databases of reference sequences used for metagenomics studies will also grow [21,23].”
BackgroundReducing storage requirements for biological sequence comparison
Roberts, M., Hayes, W., Hunt, B. R., Mount, S. M., & Yorke, J. A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth408 · Cited by 485 · cited 1×
“We also performed a third parameter sweep, focused on translated search (Kraken 2X), where we looked at values for l in the interval [11, 15], values for k in the interval [l, l+3], and values for s in the interval [0, 3].”
Method
Research works printed but not linked: 30
- Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16:236. · DOI · Paper page
- Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:11257. · DOI · Paper page
- Breitwieser FP, Baker DN, Salzberg SL. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 2018;19:198. · DOI · Paper page
- Lindgreen S, Adair KL, Gardner PP. An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep. 2016;6:19233. · DOI · Paper page
- Ye SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking metagenomics tools for taxonomic classification. Cell. 2019;178:779–94. · DOI · Paper page
- Eyice Ö, et al. SIP metagenomics identifies uncultivated Methylophilaceae as dimethylsulphide degrading bacteria in soil and lake sediment. ISME J. 2015;9:2336. · DOI
- Merelli I, et al. Low-power portable devices for metagenomics analysis: fog computing makes bioinformatics ready for the Internet of Things. Futur Gener Comput Syst. 2018;88:467–78. · DOI
- Lu J, Salzberg SL. Removing contaminants from databases of draft genomes. PLoS Comput Biol. 2018;14:e1006277. · DOI
- Donovan PD, Gonzalez G, Higgins DG, Butler G, Ito K. Identification of fungi in shotgun metagenomics datasets. PLoS One. 2018;13:e0192898. · DOI
- Meiser A, Otte J, Schmitt I, Grande FD. Sequencing genomes from mixed DNA samples - evaluating the metagenome skimming approach in lichenized fungi. Sci Rep. 2017;7:14881. · DOI
- Knutson TP, Velayudhan BT, Marthaler DG. A porcine enterovirus G associated with enteric disease contains a novel papain-like cysteine protease. J Gen Virol. 2017;98:1305–10. · DOI
- Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104. · doi:10.7717/peerj-cs.104
- Langmead B, Wilks C, Antonescu V, Charles R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics. 2018;35(3):421–32. · DOI · Paper page
- Pettengill EA, Pettengill JB, Binet R. Phylogenetic analyses of Shigella and enteroinvasive Escherichia coli for the identification of molecular epidemiological markers: whole-genome comparative analysis does not support distinct genera designation. Front Microbiol. 2016;6:1573. · DOI
- Helgason E, et al. Bacillus anthracis, Bacillus cereus, and Bacillus thuringiensis—one species on the basis of genetic evidence. Appl Environ Microbiol. 2000;66:2627 LP–2630. · DOI · Paper page
- Gomila M, Peña A, Mulet M, Lalucat J, García-Valdés E. Phylogenomics and systematics in Pseudomonas. Front Microbiol. 2015;6:214. · DOI · Paper page
- Parks DH, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996. · DOI · Paper page
- Sichtig H, et al. FDA-ARGOS: a public quality-controlled genome database resource for infectious disease sequencing diagnostics and regulatory science research. bioRxiv. 2018;482059. https://doi.org/10.1101/482059. · DOI
- Pandey, P., Bender, M. A., Johnson, R. & Patro, R. A general-purpose counting filter: making every bit count. in Proc 2017 ACM Int Conf Manag Data 775–787 (2017). doi:https://doi.org/10.1145/3035918.3035963 · doi:10.1145/3035918.3035963
- Flajolet P, Fusy É, Gandouet O, Meunier F. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. Discret Math Theor Comput Sci Proc. 2007;AH:127–46. · DOI · Paper page
- Břinda K, Sykulski M, Kucherov G. Spaced seeds improve k-mer-based metagenomic classification. Bioinformatics. 2015;31:3584–92. · DOI
- Church DM, et al. Extending reference assembly models. Genome Biol. 2015;16:13. · DOI · Paper page
- The UniVec Database. at <https://www.ncbi.nlm.nih.gov/tools/vecscreen/univec/>
- Morgulis A, Gertz EM, Schäffer AA, Agarwala R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J Comput Biol. 2006;13:1028–40. · DOI · Paper page
- Wootton JC, Federhen S. Analysis of compositionally biased regions in sequence databases. Methods Enzymol. 1996;266:554–71. · DOI · Paper page
- Flajolet P, Martin GN. Probabilistic counting algorithms for data base applications. J Comput Syst Sci. 1985;31:182–209. · DOI · Paper page
- Solis AD. Amino acid alphabet reduction preserves fold information contained in contact interactions in proteins. Proteins Struct Funct Bioinforma. 2015;83:2198–216. · DOI
- Holtgrewe, M. Mason - a read simulator for second generation sequencing data. Technical Report TR–B–10–06 (2010).
- Kodama Y, et al. The sequence read archive: explosive growth of sequencing data. Nucleic Acids Res. 2011;40:D54–6. · DOI · Paper page
- Lawrence JG, Hatfull GF, Hendrix RW. Imbroglios of viral taxonomy: genetic exchange and failings of phenetic approaches. J Bacteriol. 2002;184:4891 LP–4905. · DOI · Paper page
Cited by
Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A. M., et al. (2021) · eLife · doi:10.7554/elife.65088 · Cited by 2,303
Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients
Davar, D., Dzutsev, A. K., McCulloch, J. A., Rodrigues, R. R., Chauvin, J.-M., Morrison, R. M., Deblasio, R. N., Menna, C., Ding, Q., Pagliano, O., et al. (2021) · Science · doi:10.1126/science.abf3363 · Cited by 1,781
Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., Manghi, P., Dubois, L., Huang, K. D., Thomas, A. M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01688-w · Cited by 1,442
A unified catalog of 204,938 reference genomes from the human gut microbiome
Almeida, A., Nayfach, S., Boland, M., Strozzi, F., Beracochea, M., Shi, Z. J., Pollard, K. S., Sakharova, E., Parks, D. H., Hugenholtz, P., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0603-3 · Cited by 1,367
Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study.
Ren, L.-L., Wang, Y.-M., Wu, Z.-Q., Xiang, Z.-C., Guo, L., Xu, T., Jiang, Y.-Z., Xiong, Y., Li, Y.-J., Li, X.-W., et al. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000722 · Cited by 1,349
Metadata
- Authors
- Wood, D. E., Lu, J., & Langmead, B.
- Venue
- Genome Biology
- Published
- 2019-11-28
- Volume
- 20
- Issue
- 1
- Pages
- 257-257
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2990618091
- Cited by
- 7,061
- References
- 7
- FWCI
- 125.68
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Directorate for Computer and Information Science and Engineering (IIS-1349906)
- National Institute of General Medical Sciences (R01-GM118568, R35-GM130151)
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