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  • Brief Communication
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DADA2: High-resolution sample inference from Illumina amplicon data


We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors ( 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 undetected Lactobacillus crispatus variants.

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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.

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  1. Human Microbiome Project Consortium. Nature 486, 207–214 (2012).

  2. Rosen, M.J., Davison, M., Bhaya, D. & Fisher, D.S. Science 348, 1019–1023 (2015).

    Article  CAS  Google Scholar 

  3. Reeder, J. & Knight, R. Nat. Methods 7, 668–669 (2010).

    Article  CAS  Google Scholar 

  4. Quince, C., Lanzen, A., Davenport, R.J. & Turnbaugh, P.J. BMC Bioinformatics 12, 38 (2011).

    Article  Google Scholar 

  5. Rosen, M.J., Callahan, B.J., Fisher, D.S. & Holmes, S.P. BMC Bioinformatics 13, 283 (2012).

    Article  Google Scholar 

  6. Bragg, L., Stone, G., Imelfort, M., Hugenholtz, P. & Tyson, G.W. Nat. Methods 9, 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. Methods 7, 335–336 (2010).

    Article  CAS  Google Scholar 

  9. Edgar, R.C. Nat. Methods 10, 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. USA 111, 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 Genomics 14, 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. Bioinformatics 31, 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. USA 108, 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. Bioinformatics 27, 2194–2200 (2011).

    Article  CAS  Google Scholar 

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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.).

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Authors and Affiliations



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.

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Correspondence to Benjamin J Callahan.

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The authors declare no competing financial interests.

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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)

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Callahan, B., McMurdie, P., Rosen, M. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016).

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