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Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome

Nature Biotechnology volume 34, pages 6469 (2016) | Download Citation

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Abstract

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequences remains a difficult problem. Here, we present an analysis of a human gut microbiome using TruSeq synthetic long reads combined with computational tools for metagenomic long-read assembly, variant calling and haplotyping (Nanoscope and Lens). Our analysis identifies 178 bacterial species, of which 51 were not found using shotgun reads alone. We recover bacterial contigs that comprise multiple operons, including 22 contigs of >1 Mbp. Furthermore, we observe extensive intraspecies variation within microbial strains in the form of haplotypes that span up to hundreds of Kbp. Incorporation of synthetic long-read sequencing technology with standard short-read approaches enables more precise and comprehensive analyses of metagenomic samples.

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Acknowledgements

This work was supported by US National Institutes of Health/National Human Genome Research Institute (NIH/NHGRI) grant T32 HG000044. V.K. was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) post-graduate fellowship. We thank Illumina, Inc. for their assistance in sample preparation.

Author information

Author notes

    • Serafim Batzoglou
    •  & Michael Snyder

    These authors contributed equally to this work.

Affiliations

  1. Department of Computer Science, Stanford University, Stanford, California, USA.

    • Volodymyr Kuleshov
    •  & Serafim Batzoglou
  2. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.

    • Volodymyr Kuleshov
    • , Chao Jiang
    • , Wenyu Zhou
    • , Fereshteh Jahanbani
    •  & Michael Snyder

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Contributions

S.B. and M.S. conceived the study. W.Z. and F.J. performed library preparation. V.K. developed the Nanoscope pipeline and the Lens algorithm. V.K. and C.J. performed computational analyses. V.K., C.J., S.B. and M.S. wrote the paper. S.B. and M.S. supervised the study.

Competing interests

V.K. serves as a consultant for Illumina Inc. S.B. is a co-founder of DNAnexus and a member of the scientific advisory boards of 23andMe and Eve Biomedical. M.S. is a co-founder of Personalis and a member of the scientific advisory boards of Personalis, AxioMx and Genapsys.

Corresponding authors

Correspondence to Volodymyr Kuleshov or Serafim Batzoglou or Michael Snyder.

Integrated supplementary information

Supplementary figures

  1. 1.

    Histogram of long read lengths for the mock metagenome

  2. 2.

    Histogram of long read lengths for the real metagenome

  3. 3.

    Fraction of genome covered with short and long reads, per organism, given an equal number of bases sequenced with each technology.

  4. 4.

    Estimated abundance using short and long reads.

  5. 5.

    Comparison of contig lengths obtained from short and long sequencing (real metagenome).

  6. 6.

    Recovery of operons from the assemblies obtained from short reads, long reads, and from the joint assembly (mock metagenome).

  7. 7.

    Recovery of genes from the assemblies obtained from short reads, long reads, and from the joint assembly (mock metagenome).

  8. 8.

    Fragment of 110 kbp genomic region in which there is variation between several bacterial subspecies.

  9. 9.

    Genomic region 50 kbp in length in which there is variation between several bacterial subspecies.

  10. 10.

    Percentage of genomic regions where all haplotypes are in perfect phylogeny, as a function of the percentage of positions that have to be corrected to ensure phylogeny.

  11. 11.

    Summary of the length and depth of genomic regions at which there is variation among bacteria.

  12. 12.

    Recovery of a 2.3 Mbp long contig from a species belonging to the genus Acinetobacter for which no finished genome was previously available.

  13. 13.

    Abundance estimates in the mock metagenome obtained from Nanoscope, compared to the abundances obtained from mapping short reads to the 20 known genome references.

  14. 14.

    Genomic variation statistics for 10 gut microbial species selected from our gut metagenome sample (at least 40% genomes were covered by reads).

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–14, Supplementary Tables 1–33 and Supplementary Methods

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    Supplementary Code

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DOI

https://doi.org/10.1038/nbt.3416

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