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.

  • Correspondence
  • Published:

Accessible, curated metagenomic data through ExperimentHub

Nature Methods volume 14, pages 1023–1024 (2017)Cite this article

Subjects

To the Editor:

The microbiome has emerged as a key aspect of human biology and has been implicated in many disease etiologies. Shotgun metagenomic sequencing is an approach with the highest resolution currently available for studying the taxonomic composition and functional potential of the human microbiome. The increase in publicly available shotgun data theoretically enables hypothesis testing for specific diseases and environmental niches as well as meta-analysis across related studies. However, several factors prevent the research community from taking full advantage of these resources. Barriers include the need for substantial investments of time, computational resources and specialized bioinformatic expertise as well as inconsistencies in annotation and formatting between individual studies.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: curatedMetagenomicData production pipeline and examples of enabled analyses.

References

  1. Huber, W. et al. Nat. Methods 12, 115–121 (2015).

    Article  CAS  Google Scholar 

  2. Truong, D.T. et al. Nat. Methods 12, 902–903 (2015).

    Article  CAS  Google Scholar 

  3. Abubucker, S. et al. PLoS Comput. Biol. 8, e1002358 (2012).

    Article  CAS  Google Scholar 

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

  5. Koren, O. et al. PLoS Comput. Biol. 9, e1002863 (2013).

    Article  CAS  Google Scholar 

  6. Arumugam, M. et al. Nature 473, 174–180 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was made possible by the CUNY High Performance Computing Center, College of Staten Island, funded in part by the City and State of New York, CUNY Research Foundation, and National Science Foundation Grants CNS-0958379, CNS-0855217 and ACI 1126113. Support was provided by the European Union H2020 Marie-curie grant (707345) to E.P., the European Research Council (ERC-STG project MetaPG), MIUR “Futuro in Ricerca” RBFR13EWWI_001, the People Programme (Marie Curie Actions) of the European Union Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. PCIG13-GA-2013-618833, the LEO Pharma Foundation, and by Fondazione CARITRO fellowship Rif.Int.2013.0239 to N.S., the National Institute of Allergy and Infectious Diseases (1R21AI121784-01 to J.B.D. and L.W.) and the US National Cancer Institute (U24CA180996 to M.M. and L.W.).

Author information

Author notes

  1. Edoardo Pasolli, Lucas Schiffer and Paolo Manghi: These authors contributed equally to this work.

Authors and Affiliations

  1. Centre for Integrative Biology, University of Trento, Trento, Italy

    Edoardo Pasolli, Paolo Manghi, Duy Tin Truong, Francesco Beghini & Nicola Segata

  2. Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA

    Lucas Schiffer, Audrey Renson, Faizan Malik, Marcel Ramos, Jennifer B Dowd & Levi Waldron

  3. Institute for Implementation Science and Population Health, City University of New York, New York, New York, USA

    Lucas Schiffer, Audrey Renson, Valerie Obenchain, Marcel Ramos & Levi Waldron

  4. Roswell Park Cancer Institute, University of Buffalo, Buffalo, New York, USA

    Marcel Ramos & Martin Morgan

  5. Department of Global Health and Social Medicine, King's College London, London, UK

    Jennifer B Dowd

  6. Biostatistics Department, Harvard School of Public Health, Boston, Massachusetts, USA

    Curtis Huttenhower

  7. The Broad Institute, Cambridge, Massachusetts, USA

    Curtis Huttenhower

Authors
  1. Edoardo Pasolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lucas Schiffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Manghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Audrey Renson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Valerie Obenchain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Duy Tin Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Francesco Beghini
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Faizan Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marcel Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jennifer B Dowd
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Curtis Huttenhower
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Martin Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Nicola Segata
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Levi Waldron
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nicola Segata or Levi Waldron.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Clustering scores for enterotypes in gut WGS samples.

Consistent with Koren et al.5, these plots indicate weak support for any discrete clustering in the data and confirm that the three enterotypes hypothesis is likely an oversimplification that does not hold when considering large set of biogeographycally diverse populations. Thresholds for significance of clustering are presented as dashed lines, and are the same thresholds used by Koren et al.5. Each plot line represents an analysis that can be accomplished with one line of code using the R packages 'fpc' (prediction strength and Calinski-Harabasz) and 'cluster' (silhouette index), provided in the curatedMetagenomicData package examples.

Supplementary Figure 2 Health status classification from species abundance.

Six different classification problems of health status were attempted using a random forest algorithm and cross-validation to estimate prediction accuracy. Plots show ROC curves by using species abundance as microbiome features, one of the five data types considered in the Example 1 of Figure 1. Results are consistent with the meta-analysis conducted in32.

Supplementary Figure 3 Principal Coordinates Analysis (PCoA) plot of species abundance for gut samples on selected diseases.

Specimens are annotated by disease state (shape), study name (color), and abundance of Prevotella copri (size).

Supplementary Figure 4 Top correlations between metabolic pathways and genera.

Pearson correlation was calculated between each individual pathway (HUMAnN2 pathways from the full UniRef90 database) and each of the top 20 most abundant microbial genera, in a combined dataset obtained from merging 20 studies of gut specimens. The top correlations are 1) Ornithine de novo biosynthesis: Bacteroides (r = 0.86), activity that has been confirmed in cultures of this organism33, and 2) superpathway of allantoin degradation in yeast: Escherichia (r = 0.95). Although this superpathway has been associated with yeast, it includes subpathways (such as allantoin degradation to glyoxylate I and allantoin degradation to ureidoglycolate I) that are common in Escherichia, which is known to be an allantoin utilizier under anaerobic conditions34. Of note, the top 100 correlations have adjusted p < 0.001.

Supplementary Figure 5 Alpha diversity of taxa from 22 studies of the gut microbiome.

Shannon Alpha Diversity was calculated for each individual sample within each human gut microbiome study. The median diversity varies by a maximum factor of 1.5 between studies, however the variability within studies as measured by interquartile range varies by more than 3-fold.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Methods and Supplementary Tables 1–2

Life Sciences Reporting Summary

Life Sciences Reporting Summary

Supplementary Software

Snapshot of curatedMetagenomicData including pipelines, github.io website, and software. This is GitHub commit 7ec5083.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pasolli, E., Schiffer, L., Manghi, P. et al. Accessible, curated metagenomic data through ExperimentHub. Nat Methods 14, 1023–1024 (2017). https://doi.org/10.1038/nmeth.4468

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4468

This article is cited by

Search

Advanced 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