ABSTRACT Metagenomic experiments expose the wide range of microscopic organisms in any microbial environment through high-throughput DNA sequencing. The computational analysis of the

sequencing data is critical for the accurate and complete characterization of the microbial community. To facilitate efficient and reproducible metagenomic analysis, we introduce a

step-by-step protocol for the Kraken suite, an end-to-end pipeline for the classification, quantification and visualization of metagenomic datasets. Our protocol describes the execution of

the Kraken programs, via a sequence of easy-to-use scripts, in two scenarios: (1) quantification of the species in a given metagenomics sample; and (2) detection of a pathogenic agent from a

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customer support SIMILAR CONTENT BEING VIEWED BY OTHERS BENCHMARKING SECOND AND THIRD-GENERATION SEQUENCING PLATFORMS FOR MICROBIAL METAGENOMICS Article Open access 11 November 2022 CRITICAL

ASSESSMENT OF METAGENOME INTERPRETATION: THE SECOND ROUND OF CHALLENGES Article Open access 08 April 2022 UNVEILING MICROBIAL DIVERSITY: HARNESSING LONG-READ SEQUENCING TECHNOLOGY Article

30 April 2024 DATA AVAILABILITY The microbiome analysis used three samples from Taur et al.8, and the pathogen identification used ten samples from Li et al.9, all of which can be found on

NCBI with their SRA IDs. Source data are provided with this paper. CODE AVAILABILITY The following website details and links all software and databases used in this protocol:

http://ccb.jhu.edu/data/kraken2_protocol/. We also provide easy-to-use Jupyter notebooks for both workflows, which can be executed in the browser using Google Collab:

https://github.com/martin-steinegger/kraken-protocol/. CHANGE HISTORY * _ 29 AUGUST 2024 A Correction to this paper has been published: https://doi.org/10.1038/s41596-024-01064-1 _

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  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS Indexes for tools in the Kraken suite, including the indexes used in this protocol, are made freely available

on Amazon Web Services thanks to the AWS Public Dataset Program. B.L. was supported by NIH/NIHMS grant R35GM139602. S.L.S. was supported by NIH grants R35-GM130151 and R01-HG006677. M.S.

acknowledges support from the National Research Foundation of Korea grant (2019R1A6A1A10073437, 2020M3A9G7103933, 2021R1C1C102065 and 2021M3A9I4021220); New Faculty Startup Fund; and the

Creative-Pioneering Researchers Program through Seoul National University. AUTHOR INFORMATION Author notes * These authors contributed equally: Jennifer Lu, Natalia Rincon. AUTHORS AND

AFFILIATIONS * Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA Jennifer Lu, Natalia Rincon & Steven L. Salzberg * Center for Computational Biology,

Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA Jennifer Lu, Natalia Rincon, Derrick E. Wood, Florian P. Breitwieser, Christopher Pockrandt & Steven L.

Salzberg * Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA Derrick E. Wood, Ben Langmead & Steven L. Salzberg * Department of Biostatistics, Johns Hopkins

University, Baltimore, MD, USA Steven L. Salzberg * School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea

Martin Steinegger Authors * Jennifer Lu View author publications You can also search for this author inPubMed Google Scholar * Natalia Rincon View author publications You can also search for

this author inPubMed Google Scholar * Derrick E. Wood View author publications You can also search for this author inPubMed Google Scholar * Florian P. Breitwieser View author publications

You can also search for this author inPubMed Google Scholar * Christopher Pockrandt View author publications You can also search for this author inPubMed Google Scholar * Ben Langmead View

author publications You can also search for this author inPubMed Google Scholar * Steven L. Salzberg View author publications You can also search for this author inPubMed Google Scholar *

Martin Steinegger View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.L. and M.S. led the development of the protocol. N.R. executed and

designed the microbiome analysis protocol and is the author of the KrakenTools α-diversity tools. J.L. developed the pathogen identification protocol and is the author of Bracken and

KrakenTools. M.S. authored the Jupyter notebooks for the protocol. D.E.W. is the senior author of Kraken and Kraken 2. F.B. is the author of KrakenUniq. C.P. is an author for the KrakenTools

β-diversity script. B.L. supervised the development of Kraken 2. S.L.S. supervised the development of Kraken, KrakenUniq and Bracken. B.L. and S.L.S. supervised the development of this

protocol. All authors contributed to the writing of the manuscript. CORRESPONDING AUTHORS Correspondence to Jennifer Lu or Martin Steinegger. ETHICS DECLARATIONS COMPETING INTERESTS The

authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Protocols_ thanks the anonymous reviewers for their contribution to the peer review of this work.

ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. RELATED LINKS KEY REFERENCES

USING THIS PROTOCOL Salzberg, S. et al. _Neurol. Neuroimmunol. Neuroinflamm_. 3, e251 (2016): https://doi.org/10.1212/NXI.0000000000000251 Wood, D. et al. _Genome Biol_. 15, R46 (2014):

https://doi.org/10.1186/gb-2014-15-3-r46 Lu, J. et al. _Peer J. Comput. Sci_. 3, e104 (2017): https://doi.org/10.7717/peerj-cs.104 Breitwieser, F. et al. _Genome Biol_. 19, 198 (2018):

https://doi.org/10.1186/s13059-018-1568-0 Wood, D. et al. _Genome Biol_. 20, 257 (2019): https://doi.org/10.1186/s13059-019-1891-0 Breitwieser, F. et al. _Bioinformatics_ 36, 1303–1304

(2020): https://doi.org/10.1093/bioinformatics/btz715 KEY DATA USED IN THIS PROTOCOL Taur, Y. et al. _Sci. Transl. Med_. 10, eaap9489 (2018): https://doi.org/10.1126/scitranslmed.aap9489 Li,

Z. et al. _Invest_. _Ophthalmol. Vis. Sci_. 59, 280–288 (2018): https://doi.org/10.1167/iovs.17-21617 SUPPLEMENTARY INFORMATION SUPPLEMENTARY TABLE 1 Supplementary Table 1 SUPPLEMENTARY

TABLE 2 Supplementary Table 2 SOURCE DATA SOURCE DATA FIG. 2 Breport text for plotting Sankey, and krona counts for plotting krona plots. SOURCE DATA FIG. 6 Alpha diversity table text, bray

Curtis equation text, and heatmap values for beta diversity. SOURCE DATA FIG. 7 Pathogen sample species heat map data. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society

or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of

this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Lu, J., Rincon, N., Wood, D.E.

_et al._ Metagenome analysis using the Kraken software suite. _Nat Protoc_ 17, 2815–2839 (2022). https://doi.org/10.1038/s41596-022-00738-y Download citation * Received: 29 June 2021 *

Accepted: 16 June 2022 * Published: 28 September 2022 * Issue Date: December 2022 * DOI: https://doi.org/10.1038/s41596-022-00738-y SHARE THIS ARTICLE Anyone you share the following link

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