Evidence map›Paper›PMID 38605401›Full record

ArticleGenome biology2024

Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies.

Xiaowen Feng, Heng Li

Abstract read
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Bioinformatics advances · 2026
    Article
  5. Review
  6. High-resolution metagenome assembly for modern long reads with myloasm.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. 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

2 authors.

Xiaowen FengDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, USA.
Heng LiDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, USA. hli@ds.dfci.harvard.edu.ORCID 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
The construction and utility of reference pan-genome graphsU01HG010961 · NHGRI · DANA-FARBER CANCER INST · PI LI, HENG, PATEN, BENEDICT · 2020 to 2023
$3.2M
NHGRI NIH HHS R01 HG010040NHGRI NIH HHS R01HG010040NHGRI NIH HHS U01 HG010961NHGRI NIH HHS U01HG010961
6 · The paper itself

Abstract

backgroundIn the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them.

resultsHere, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins.

conclusionsOur work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.

Indexed as

MetagenomeMicrobiotaAlgorithmsBacteriaMetagenomicsAssembly completenessBinningMetagenomeMetagenome-assembled genomes

Identifiers

PMID38605401
PMCPMC11007910

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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.