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High-resolution metagenome assembly for modern long reads with myloasm

Nature Biotechnology (2026)Cite this article

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Abstract

Long-read metagenome assembly promises complete genomic recovery from microbiomes. However, the complexity of metagenomes poses challenges. Here we present myloasm, a metagenome assembler for modern long reads such as PacBio HiFi and Oxford Nanopore Technologies (ONT) R10.4 long reads. Myloasm uses polymorphic k-mers to construct a high-resolution string graph and then leverages differential abundance for graph simplification. On real-world ONT metagenomes, myloasm assembled three times more complete circular contigs than the next-best assembler. Myloasm can make ONT and HiFi assemblies comparable. For example, on a jointly sequenced gut metagenome, myloasm with ONT assembled more complete circular genomes than any assembler with HiFi. Myloasm also recovers previously inaccessible within-species diversity. Here, we recovered six complete Prevotella copri single-contig genomes from a gut metagenome and eight complete TM7 (Saccharibacteria) contigs with >93% similarity from an oral metagenome. Overall, we show that myloasm outperforms existing long-read metagenome assemblers across a range of environments and modern sequencing technologies.

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Fig. 1: Algorithm overview of myloasm.
Fig. 2: Benchmarking long-read metagenome assemblers on simulated communities with similar strains.
Fig. 3: Results on a concatenated ONT R10.4 mock metagenome with 48 genomes consisting of 14 isolates and 3 mock metagenomes.
Fig. 4: MAG and contig recovery results for real metagenomes.
Fig. 5: Quality control assessment for contigs.
Fig. 6: Myloasm unveils hidden strain heterogeneity and horizontal gene transfer in ONT R10.4 metagenomes.

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Data availability

The mock nanopore community was generated from the MicroBench116 suite by R. Kirkegaard (https://github.com/Kirk3gaard/MicroBench and accession PRJEB85558) along with 14 isolates taken from ref. 37. Accessions for the mock communities are available in Supplementary Table 2. Accessions for the real metagenomic datasets are available in Supplementary Table 3. Source data are provided with this paper.

Code availability

Myloasm is open source and available at https://github.com/bluenote-1577/myloasm. Documentation for myloasm is available at https://myloasm-docs.github.io/. The mylotools software suite is open source and available at https://github.com/bluenote-1577/mylotools.

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Acknowledgements

This work is supported by US National Institute of Health grant no. R01HG010040 to H.L. J.S. is supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship award (no. PDF-587396). We thank H. Cheng, X. Feng and members of the Li lab for helpful discussions. We thank M. Albertsen and R. Kirkegaard for benchmarking dataset support. We thank M. Sereika and T. Nielsen for providing valuable software feedback.

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Authors and Affiliations

  1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Jim Shaw, Maximillian G. Marin & Heng Li

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Jim Shaw, Maximillian G. Marin & Heng Li

Authors
  1. Jim Shaw
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  2. Maximillian G. Marin
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  3. Heng Li
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Contributions

J.S. and H.L. conceived the project. J.S. implemented the method and devised the algorithms with guidance from H.L. J.S. conducted the benchmarking analyses. J.S. and M.G.M. performed the horizontal gene transfer analyses. J.S. wrote the manuscript with contributions from H.L. and M.G.M. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Jim Shaw or Heng Li.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Alex Crits-Cristoph, John Lees and Víctor Rodríguez-Bouza for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Methods and Figs. 1–14.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Supplementary Table 1 (download XLSX )

QUAST metrics for synthetic long-read metagenome experiments.

Supplementary Table 2 (download XLSX )

Benchmarking metrics and datasets for the concatenated mock metagenome experiments.

Supplementary Table 3 (download XLSX )

Dataset description and timing results for the real long-read metagenomes.

Source data

Source Data Figs. 1–6 (download XLSX )

Source data for reproducing Figs. 1–6.

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Shaw, J., Marin, M.G. & Li, H. High-resolution metagenome assembly for modern long reads with myloasm. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03053-z

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