Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Methods
  • Brief Communication
  • Published:

DADA2: High-resolution sample inference from Illumina amplicon data

Nature Methodsvolume 13, pages581–583 (2016)Cite this article

Subjects

Abstract

We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetectedLactobacillus crispatus variants.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

￥ 4,980

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Comparison of sequence variants inferred by DADA2 with OTUs constructed by UPARSE.
Figure 2:L.crispatus sequence variants in the human vaginal community during pregnancy.

Similar content being viewed by others

Accession codes

Primary accessions

Sequence Read Archive

References

  1. Human Microbiome Project Consortium.Nature486, 207–214 (2012).

  2. Rosen, M.J., Davison, M., Bhaya, D. & Fisher, D.S.Science348, 1019–1023 (2015).

    Article CAS  Google Scholar 

  3. Reeder, J. & Knight, R.Nat. Methods7, 668–669 (2010).

    Article CAS  Google Scholar 

  4. Quince, C., Lanzen, A., Davenport, R.J. & Turnbaugh, P.J.BMC Bioinformatics12, 38 (2011).

    Article  Google Scholar 

  5. Rosen, M.J., Callahan, B.J., Fisher, D.S. & Holmes, S.P.BMC Bioinformatics13, 283 (2012).

    Article  Google Scholar 

  6. Bragg, L., Stone, G., Imelfort, M., Hugenholtz, P. & Tyson, G.W.Nat. Methods9, 425–426 (2012).

    Article CAS  Google Scholar 

  7. Schloss, P.D. et al.Appl. Environ. Microbiol.75, 7537–7541 (2009).

    Article CAS  Google Scholar 

  8. Caporaso, J.G. et al.Nat. Methods7, 335–336 (2010).

    Article CAS  Google Scholar 

  9. Edgar, R.C.Nat. Methods10, 996–998 (2013).

    Article CAS  Google Scholar 

  10. Eren, A.M., Borisy, G.G., Huse, S.M. & Welch, J.L.M.Proc. Natl. Acad. Sci. USA111, E2875–E2884 (2014).

    Article CAS  Google Scholar 

  11. Eren, A.M. et al.ISME J.9, 968–979 (2015).

    Article CAS  Google Scholar 

  12. Tikhonov, M., Leach, R.W. & Wingreen, N.S.ISME J.9, 68–80 (2015).

    Article  Google Scholar 

  13. Wang, C., Mitsuya, Y., Gharizadeh, B., Ronaghi, M. & Shafer, R.W.Genome Res.17, 1195–1201 (2007).

    Article CAS  Google Scholar 

  14. McElroy, K., Zagordi, O., Bull, R., Luciani, F. & Beerenwinkel, N.BMC Genomics14, 501 (2013).

    Article  Google Scholar 

  15. Guarner, F.Nat. Rev. Gastroenterol. Hepatol.11, 647–649 (2014).

    Article  Google Scholar 

  16. Schirmer, M. et al.Nucleic Acids Res.43, e37 (2015).

    Article  Google Scholar 

  17. Kozich, J.J., Westcott, S.L., Baxter, N.T., Highlander, S.K. & Schloss, P.D.Appl. Environ. Microbiol.79, 5112–5120 (2013).

    Article CAS  Google Scholar 

  18. Edgar, R.C. & Flyvbjerg, H.Bioinformatics31, 3476–3482 (2015).

    Article CAS  Google Scholar 

  19. MacIntyre, D.A. et al.Sci. Rep.11, 8988 (2015).

    Article  Google Scholar 

  20. Ravel, J. et al.Proc. Natl. Acad. Sci. USA108, 4680–4687 (2011).

    Article CAS  Google Scholar 

  21. Sun, Y. et al.Nucleic Acids Res.37, e76 (2009).

    Article  Google Scholar 

  22. Caporaso, J.G. et al.ISME J.6, 1621–1624 (2012).

    Article CAS  Google Scholar 

  23. Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C. & Knight, R.Bioinformatics27, 2194–2200 (2011).

    Article CAS  Google Scholar 

Download references

Acknowledgements

We thank M. Schirmer and D. MacIntyre for productive correspondence. This work was supported by the NSF (DMS-1162538 to S.P.H.), the NIH (R01AI112401 to S.P.H.), and the Samarth Foundation (Stanford Microbiome Seed Grant to B.J.C. and S.P.H.).

Author information

Authors and Affiliations

  1. Department of Statistics, Stanford University, Stanford, California, USA

    Benjamin J Callahan & Susan P Holmes

  2. Second Genome, South San Francisco, California, USA

    Paul J McMurdie, Andrew W Han & Amy Jo A Johnson

  3. Department of Applied Physics, Stanford University, Stanford, California, USA

    Michael J Rosen

Authors
  1. Benjamin J Callahan
  2. Paul J McMurdie
  3. Michael J Rosen
  4. Andrew W Han
  5. Amy Jo A Johnson
  6. Susan P Holmes

Contributions

B.J.C. and S.P.H. designed the research; B.J.C., P.J.M., and M.J.R. implemented the algorithm; B.J.C. performed the analysis; B.J.C., P.J.M., M.J.R., and S.P.H. wrote the paper; and A.W.H. and A.J.A.J. generated the Extreme data set designed by B.J.C., P.J.M., and A.W.H.

Corresponding author

Correspondence toBenjamin J Callahan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1–3 and Supplementary Notes 1 and 2 (PDF 1809 kb)

Supplementary Software

DADA2 software package and scripts for benchmarking and analysis (ZIP 1312 kb)

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Callahan, B., McMurdie, P., Rosen, M.et al. DADA2: High-resolution sample inference from Illumina amplicon data.Nat Methods13, 581–583 (2016). https://doi.org/10.1038/nmeth.3869

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Advertisement

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for theNature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox.Sign up for Nature Briefing: AI and Robotics
[8]ページ先頭
©2009-2025 Movatter.jp