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.

Detecting contamination in viromes using ViromeQC

Subjects

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Survey of viral enrichment rates on 1,977 samples from 35 studies estimated as percentage of reads aligning to the small subunit rRNA gene.
Fig. 2: Combined quantification of ribosomal genes and genes encoding universal proteins identifies the cross-study set of 101 samples with >100× VLP enrichment.

Data availability

The raw reads analyzed in this study are available using accession numbers provided in Supplementary Tables 1 and 2.

Code availability

Code and documentation are available at http://segatalab.cibio.unitn.it/tools/viromeqc.

References

  1. 1.

    Shkoporov, A. N. & Hill, C. Cell Host Microbe 25, 195–209 (2019).

    CAS  Article  Google Scholar 

  2. 2.

    Suttle, C. A. Nat. Rev. Microbiol. 5, 801–812 (2007).

    CAS  Article  Google Scholar 

  3. 3.

    Wang, X. et al. Nat. Commun. 1, 147 (2010).

    Article  Google Scholar 

  4. 4.

    Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. PeerJ 3, e985 (2015).

    Article  Google Scholar 

  5. 5.

    Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. Microbiome 5, 69 (2017).

    Article  Google Scholar 

  6. 6.

    Thurber, R. V., Haynes, M., Breitbart, M., Wegley, L. & Rohwer, F. Nat. Protoc. 4, 470–483 (2009).

    CAS  Article  Google Scholar 

  7. 7.

    Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Nat. Biotechnol. 35, 833–844 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Reyes, A. et al. Nature 466, 334–338 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    McCann, A. et al. PeerJ 6, e4694 (2018).

    Article  Google Scholar 

  10. 10.

    Roux, S. et al. Nature 537, 689–693 (2016).

    CAS  Article  Google Scholar 

  11. 11.

    Watkins, S. C. et al. Mar. Freshw. Res. 67, 1700–1708 (2016).

    Article  Google Scholar 

  12. 12.

    Rosario, K., Fierer, N., Miller, S., Luongo, J. & Breitbart, M. Environ. Sci. Technol. 52, 1014–1027 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Roux, S., Krupovic, M., Debroas, D., Forterre, P. & Enault, F. Open Biol. 3, 130160 (2013).

    Article  Google Scholar 

  14. 14.

    Minot, S. et al. Genome Res. 21, 1616–1625 (2011).

    CAS  Article  Google Scholar 

  15. 15.

    Emerson, J. B. et al. Appl. Environ. Microbiol. 78, 6309–6320 (2012).

    CAS  Article  Google Scholar 

  16. 16.

    Minot, S. et al. Proc. Natl. Acad. Sci. USA 110, 12450–12455 (2013).

    CAS  Article  Google Scholar 

  17. 17.

    Kim, Y., Aw, T. G., Teal, T. K. & Rose, J. B. Environ. Sci. Technol. 49, 8396–8407 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Ly, M. et al. Microbiome 4, 64 (2016).

    Article  Google Scholar 

  19. 19.

    Reyes, A. et al. Proc. Natl. Acad. Sci. USA 112, 11941–11946 (2015).

    CAS  Article  Google Scholar 

  20. 20.

    Roux, S. et al. PLoS One 7, e33641 (2012).

    CAS  Article  Google Scholar 

  21. 21.

    Weynberg, K. D., Wood-Charlson, E. M., Suttle, C. A. & van Oppen, M. J. H. Front. Microbiol. 5, 206 (2014).

    Article  Google Scholar 

  22. 22.

    Hannigan, G.D. et al. MBio 6, e01578–15 (2015).

    CAS  Article  Google Scholar 

  23. 23.

    Aguirre de Cárcer, D., López-Bueno, A., Alonso-Lobo, J. M., Quesada, A. & Alcamí, A. FEMS Microbiol. Ecol. 92, fiw074 (2016).

    Article  Google Scholar 

  24. 24.

    Shkoporov, A. N. et al. Microbiome 6, 68 (2018).

    Article  Google Scholar 

  25. 25.

    Pasolli, E. et al. Nat. Methods 14, 1023–1024 (2017).

    CAS  Article  Google Scholar 

  26. 26.

    Leinonen, R., Sugawara, H. & Shumway, M. & International Nucleotide Sequence Database Collaboration. Nucleic Acids Res. 39, D19–D21 (2011).

    CAS  Article  Google Scholar 

  27. 27.

    Zolfo, M., Tett, A., Jousson, O., Donati, C. & Segata, N. Nucleic Acids Res. 45, e7 gkw837 (2016).

  28. 28.

    Quince, C. et al. Genome Biol. 18, 181 (2017).

    Article  Google Scholar 

  29. 29.

    Wu, M. & Scott, A. J. Bioinformatics 28, 1033–1034 (2012).

    CAS  Article  Google Scholar 

  30. 30.

    Mizuno, C. M. et al. Nat. Commun. 10, 752 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716575) to N.S. The work was also supported by MIUR ‘Futuro in Ricerca’’ RBFR13EWWI_001 and by the European Union (H2020-SFS-2018-1 project MASTER-818368 and H2020-SC1-BHC project ONCOBIOME-825410) to N.S.

Author information

Affiliations

Authors

Contributions

Study conception and design: M.Z. and N.S. Methodology and analysis: M.Z., F.P., F.A., A.T., F.B. and N.S. Public datasets collection and curation: M.Z. and P.M. All authors contributed to the writing of the final manuscript.

Corresponding author

Correspondence to Nicola Segata.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary Materials

Supplementary Methods, Supplementary Note 1 and Supplementary Figures 1–7

Supplementary Table 1

Summary of the 2,050 virome datasets considered in the analysis. Dataset sample sizes are related to the actual number of samples that could be classified as DNA VLP viromes according to the available metadata. The reference number refers to Fig. 1. Fig. 2d and Supplementary Fig. 1.

Supplementary Table 2

Summary of the 2,189 metagenomes and 109 synthetic metagenomes and mock communities considered in the analysis. Dataset sample sizes are related to the actual number of samples that could be classified as DNA metagenomes according to the available metadata. The reference number refers to Fig. 1. Fig. 2d and Supplementary Fig. 1.

Supplementary Table 3

Full dataset of metagenomes and viromes. Contaminant abundances and enrichment data for all the 1,871 metagenomes, 1,670 viromes and 109 synthetic and mock communities that passed all quality controls. Sample type and number of starting reads are provided, as well as the percentage of SSU and LSU rRNAs stratified by life domain.

Supplementary Table 4

Validation of the rRNA mapping approach. Expected abundances of 16S rRNA genes are reported for the 108 synthetic and mock communities (tab 1) and 917 16S amplicon sequencing samples (tab 2). Control metagenomes and 16S samples were mapped against the SSU rRNA genes and filtered at different stringency thresholds (see Supplementary Methods). For the amplicon 16S samples at the expected value was set to 100%. The selected threshold is highlighted in blue. The composition of each synthetic metagenome is reported in tab 3. The rRNA abundances in RNA viromes are reported in tab 4.

Supplementary Table 5

Detection of single-copy bacterial markers in viral genomes. Number of genomes in each database in which the 31 single-copy markers are detected. The IMG/VR database was split into isolate viruses and uncultivated viruses (tab 1). Number of distinct single-copy markers detected in each database (tab 2).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zolfo, M., Pinto, F., Asnicar, F. et al. Detecting contamination in viromes using ViromeQC. Nat Biotechnol 37, 1408–1412 (2019). https://doi.org/10.1038/s41587-019-0334-5

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing