Evidence map›Paper›PMID 41896477›Full record

ArticleNature biotechnology2026

High-resolution metagenome assembly for modern long reads with myloasm.

Jim Shaw, Maximillian G Marin, Heng Li

Abstract read
In one paragraph

Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. ASM case reports · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Bioinformatics advances · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jim ShawDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. jshaw@ds.dfci.harvard.edu.ORCID http://orcid.org/0000-0002-6990-7829
Maximillian G MarinDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9108-3328
Heng LiDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. hli@ds.dfci.harvard.edu.ORCID http://orcid.org/0000-0003-4874-2874

Funding

Advanced computational methods in analyzing high-throughput sequencing dataR01HG010040 · NHGRI · DANA-FARBER CANCER INST · PI Heng Li · 2018 to 2026
$3.7M
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) PDF-587396NHGRI NIH HHS R01 HG010040U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG010040
6 · The paper itself

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.

Identifiers

PMID41896477
PMCPMC13035328

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.