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

Nature Biotechnology volume 34, pages 64–69 (2016)Cite this article

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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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Figure 1: The Nanoscope pipeline and the Lens algorithm.
Figure 2: Long reads aligned to assembled metagenomic contigs reveal extensive variation within bacterial strains.
Figure 3: Bacterial species identified only by long reads (blue), only by short reads (magenta), ordered by abundance.

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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.

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Author notes

  1. Serafim Batzoglou and Michael Snyder: These authors contributed equally to this work.

Authors and 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

Authors
  1. Volodymyr Kuleshov
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  2. Chao Jiang
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  4. Fereshteh Jahanbani
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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.

Corresponding authors

Correspondence to Volodymyr Kuleshov, Serafim Batzoglou or Michael Snyder.

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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.

Integrated supplementary information

Supplementary Figure 1 Histogram of long read lengths for the mock metagenome

Supplementary Figure 2 Histogram of long read lengths for the real metagenome

Supplementary Figure 3 Fraction of genome covered with short and long reads, per organism, given an equal number of bases sequenced with each technology.

For several organisms, the % coverage greatly varies between the two technologies, indicating different types of bias.

Supplementary Figure 4 Estimated abundance using short and long reads.

For several organisms, the estimated abundances vary significantly.

Supplementary Figure 5 Comparison of contig lengths obtained from short and long sequencing (real metagenome).

About twenty contigs obtained from long read sequencing are longer than 1 Mbp.

Supplementary Figure 6 Recovery of operons from the assemblies obtained from short reads, long reads, and from the joint assembly (mock metagenome).

Short reads were assembled using Soapdenovo2, long reads were assembled with Celera; the two were merged with Minimus2. The joint assembly recovers more than half of all operons, and twice more than only short reads. Interestingly, long and short reads seem to recover different types of operons.

Supplementary Figure 7 Recovery of genes from the assemblies obtained from short reads, long reads, and from the joint assembly (mock metagenome).

Short reads were assembled using Soapdenovo2, long reads were assembled with Celera; the two were merged with Minimus2. The joint assembly recovers more than half of all genes, and twice more than only short reads. Interestingly, long and short reads seem to recover different types of genes.

Supplementary Figure 8 Fragment of 110 kbp genomic region in which there is variation between several bacterial subspecies.

The contig belongs to the bacterium Parabacteroides distasonis.

Supplementary Figure 9 Genomic region 50 kbp in length in which there is variation between several bacterial subspecies.

The contig belongs to the bacterium Odoribacter splanchnicus.

Supplementary Figure 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.

More than 85% of positions are in perfect phylogeny, and by correcting less than 5% of positions, we can increase this number to more than 92%.

Supplementary Figure 11 Summary of the length and depth of genomic regions at which there is variation among bacteria.

Blue regions are in perfect phylogeny, and red regions are not. Blue regions are in perfect phylogeny, and red regions are not.

Supplementary Figure 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.

We mapped contigs from an earlier fragmented assembly (bottom) to a 2.3 Mbp contig that we assembled (top). Most of the long contig appears to be covered by shorter contigs from the fragmented assembly.

Supplementary Figure 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.

Supplementary Figure 14 Genomic variation statistics for 10 gut microbial species selected from our gut metagenome sample (at least 40% genomes were covered by reads).

There is no obvious correlation between genome size/coverage and SNP density and π, which may be due to limited number of genomes analyzed.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1–33 and Supplementary Methods (PDF 3325 kb)

Supplementary Code (TAR 96160 kb)

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Kuleshov, V., Jiang, C., Zhou, W. et al. Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome. Nat Biotechnol 34, 64–69 (2016). https://doi.org/10.1038/nbt.3416

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