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metaFlye: scalable long-read metagenome assembly using repeat graphs

Nature Methods volume 17, pages 1103–1110 (2020)Cite this article

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Abstract

Long-read sequencing technologies have substantially improved the assemblies of many isolate bacterial genomes as compared to fragmented short-read assemblies. However, assembling complex metagenomic datasets remains difficult even for state-of-the-art long-read assemblers. Here we present metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. First, we benchmarked metaFlye using simulated and mock bacterial communities and show that it consistently produces assemblies with better completeness and contiguity than state-of-the-art long-read assemblers. Second, we performed long-read sequencing of the sheep microbiome and applied metaFlye to reconstruct 63 complete or nearly complete bacterial genomes within single contigs. Finally, we show that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products.

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Fig. 1: metaFlye repeat annotation and examples of simple bubbles, superbubbles and roundabouts.
Fig. 2: Comparison of Canu, Flye, metaFlye, miniasm and wtdbg2 assemblies of the individual genomes in the SYNTH181 dataset.
Fig. 3: Per-species reference coverage and NGA50 statistics for the mock community datasets (HMP, ZymoEven GridION and ZymoLog GridION) computed using metaQUAST.
Fig. 4: Information about strains in the sheep microbiome revealed by metaFlye.

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Data availability

Sequencing data for the sheep gut sample are available under the NCBI BioProject PRJNA595610. HMP mock dataset is available at: https://github.com/PacificBiosciences/DevNet/wiki/Human_Microbiome_Project_MockB_Shotgun. Zymo datasets are at: https://github.com/LomanLab/mockcommunity. Cow rumen dataset is at: NCBI SRA repository under BioProject PRJNA507739. Human stool samples are at: ENA project PRJEB29152. NCBI accession codes for the sequences used in the NRPS analysis are: AM229678.1, AB101202.1, FP929054.1 and FP929054.1. All assemblies that were evaluated in this study, as well as SYNTH64 and SYNTH181 datasets are available at: https://doi.org/10.5281/zenodo.3986210 (ref. 66).

Code availability

metaFlye is freely available as a part of the Flye package at: https://github.com/fenderglass/Flye. The pbclip tool for PacBio subread splitting is available from https://github.com/fenderglass/pbclip.

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Acknowledgements

We are grateful to Denis Bertrand and Niranjan Nagarajan for sharing the metagenomic datasets before journal publication. M.K. and P.A.P. were supported by the NSF/MCB-BSF grant 1715911. B.B. was supported by the US National Institutes of Health grant 2-P41-GM103484. D.B. was funded by USDA CRIS project 5090-31000-026-00-D and K.K., S.S. and T.S. by project 3040-31000-100-00D. A.G. and M.R. were supported by the Russian Science Foundation (grant 19-16-00049). Computational resources were provided in part by the Research Park Computer Center at St. Petersburg State University.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of California, San Diego, CA, USA

    Mikhail Kolmogorov & Pavel A. Pevzner

  2. Cell Wall Biology and Utilization Laboratory, Dairy Forage Research Center, USDA, Madison, WI, USA

    Derek M. Bickhart

  3. Graduate Program in Bioinformatics and System Biology, University of California, San Diego, CA, USA

    Bahar Behsaz & Jeffrey Yuan

  4. Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia

    Alexey Gurevich, Mikhail Rayko & Evgeny Polevikov

  5. USDA-ARS US Meat Animal Research Center, Clay Center, NE, USA

    Sung Bong Shin, Kristen Kuhn & Timothy P. L. Smith

  6. Bioinformatics Institute, St. Petersburg, Russia

    Evgeny Polevikov

  7. Center for Microbiome Innovation, University of California, San Diego, CA, USA

    Pavel A. Pevzner

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  1. Mikhail Kolmogorov
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Contributions

M.K., J.Y. and P.P. developed the metaFlye concept. M.K. implemented and maintained metaFlye. E.P. implemented the short plasmid analysis module. D.B., S.B.S., K.K. and T.S. performed sheep gut sequencing. M.K., D.B., A.G. and M.R. benchmarked metaFlye and analyzed results. A.G. and M.K. performed analysis of synthetic datasets. M.R. analyzed plasmid and virus content. B.B. performed analysis of biosynthetic gene clusters. M.K., D.B., B.B., A.G., M.R., T.S. and P.P. edited the manuscript. P.P. supervised the project. All authors read and approved the manuscript.

Corresponding author

Correspondence to Pavel A. Pevzner.

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Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Information about metaFlye, Flye, Canu, miniasm, and wtdbg2 assemblies of the individual genomes in the SYNTH64 dataset.

NGA50 (in megabases) and reference coverage (in percentages) reported for all genomes from the SYNTH64 dataset. Genomes are ordered in the increasing mean NGA50 across all assemblers. Challenging genomes that have closely related species or strains in the metagenome are marked with (!). Grey bars on the NGA50 plot represent the length of the longest chromosome in the reference sequence for each genome (a theoretical upper bound for NGA50). NGA50 is shown in logarithmic scale (not shown for values lower than 100 kb or if the reference coverage is below 50%). The full metaQUAST report for the SYNTH64 dataset is provided in Supplementary Table 1.

Extended Data Fig. 2 NGAx plots for the mock community datasets (HMP mock, ZymoEven GridION, ZymoLog GridION).

NGA(x) is the statistic computed for contigs that are broken at their misassembly breakpoints (if any). NGA(x) is the highest possible number L such that all broken contigs that are longer than L cover at least X% of the reference. Plots were generated by metaQUAST using all available references for each dataset. Flye failed to assemble the ZymoLog datasets due to poor k-mer indexing (Methods).

Extended Data Fig. 3 Base-pair accuracy analysis for assemblies of the mock community datasets (HMP, ZymoEven GridION, and ZymoLog GridION).

Heatmaps showing the number of mismatches and short indels per 100 kbp for each species reference, computed using metaQUAST. Blue and red colors correspond to the values higher and lower than the median, respectively. Statistics were not computed for genomes with no assembled sequence (“-” symbol). Flye failed to assemble the ZymoLog datasets due to poor k-mer indexing (Methods).

Extended Data Fig. 4 The ORF lengths distribution and the GC content distribution of metaFlye and Canu assemblies of the sheep microbiome.

The ORF length distribution suggests similar base-level accuracy for both assemblies.

Extended Data Fig. 5 Taxonomic assignments of sheep microbiome assemblies.

a, metaFlye contigs assignment at the phylum level visualized with BlobTools. b, Length distributions of metaFlye and Canu contigs within each assigned superkingdom.

Extended Data Fig. 6 Statistics of simple bubbles for the metaFlye assemblies human gut and cow rumen.

(Left) the human gut dataset with 615 bubbles, and (right) the cow rumen dataset with 1510 bubbles. Bubble counts exclude loops, and include roundabouts with two edges.

Extended Data Fig. 7 Analysis of sequence overlap between 19 human gut samples.

Multi-way sequence alignments were computed using SiebliaZ. (left) The proportions of unique and shared sequences in each sample. An assembled segment within a sample is called unique if it has no alignments against sequence from any other samples. Otherwise, the segment is shared. (right) The total amount of sequence for each multiplicity bin. A sequence fragment belongs to the multiplicity bin X if it is shared by exactly X samples.

Supplementary information

Supplementary Information

Supplementary Tables 4–10 and Notes 1–8

Reporting Summary

Supplementary Table 1

Detailed information about metaFlye, Flye, Canu, miniasm and wtdbg2 assemblies of the SYNTH64 dataset

Supplementary Table 2

Detailed information about metaFlye, Flye, Canu, miniasm and wtdbg2 assemblies of the SYNTH181 dataset

Supplementary Table 3

Detailed information about metaFlye, Flye, Canu, miniasm and wtdbg2 assemblies of all mock community datasets

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Kolmogorov, M., Bickhart, D.M., Behsaz, B. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 17, 1103–1110 (2020). https://doi.org/10.1038/s41592-020-00971-x

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