Evidence map›Paper›PMID 41278535›Full record

ArticleNAR genomics and bioinformatics2025

Comparison of short-read and long-read metagenome assemblies in a natural soil community highlights systematic bias in recovery of high-diversity populations.

Maureen Berg, Taylor Reiter, Joanne Emerson, C Titus Brown, Simon Roux

Abstract readComparative Study
In one paragraph

Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maureen BergJoint Genome Institute, Department of Energy, Berkeley, CA 94720, United States.ORCID https://orcid.org/0000-0002-0745-2742
Taylor ReiterPopulation Health and Reproduction, University of California, Davis, CA 95616, United States.
Joanne EmersonDepartment of Plant Pathology, University of California, Davis, CA 95616, United States.
C Titus BrownPopulation Health and Reproduction, University of California, Davis, CA 95616, United States.ORCID https://orcid.org/0000-0001-6001-2677
Simon RouxJoint Genome Institute, Department of Energy, Berkeley, CA 94720, United States.ORCID https://orcid.org/0000-0002-5831-5895

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Comparisons of long-read and short-read (meta)genome assemblies typically show that short-read sequence assemblies are less error-prone, but struggle to assemble complicated genome regions (e.g. repeats) compared to long-read sequence assemblies. Accurate metagenome assembly is especially challenging in diverse environments, such as soil, and long-read sequencing has been shown to improve assembly. Here, we use metagenomic data with paired long-read and short-read sequences to identify specific factors that impact genome assembly and assess their relative importance in a natural soil community. Our analysis suggests that low coverage and high sequence diversity are the two main factors leading to misassemblies in short-read data, and many of these "missed" regions tend to be variable parts of the genome, such as integrated viruses or defense system islands. Taken together, our results demonstrate that short-read metagenomes can possibly underestimate the diversity of these genome regions and that long-read sequencing can complement short-read metagenomes by improving assembly contiguity and the recovery of variable regions.

Indexed as

MetagenomeMetagenomicsSoil MicrobiologyGenetic VariationHigh-Throughput Nucleotide SequencingSequence Analysis, DNA

Identifiers

PMID41278535
PMCPMC12634412

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LicenceCC BY
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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.