Evidence map›Paper›PMID 35227283›Full record

ArticleGenome biology2022

HiCBin: binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps.

Yuxuan Du, Fengzhu Sun

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

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  15. Analysis of metagenomic data.Nature reviews. Methods primers · 2025
    Article
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  17. Sequencing-based analysis of microbiomes.Nature reviews. Genetics · 2024
    Review
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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.

Yuxuan DuDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, USA.
Fengzhu SunDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, USA. fsun@usc.edu.ORCID 0000-0002-8552-043X

Funding

Computational Studies of Virus-host Interactions Using Metagenomics Data and ApplicationsR01GM120624 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AHLGREN, NATHAN, SUN, FENGZHU · 2017 to 2020
$1.7M
Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological DataR01GM131407 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FAN, YINGYING · 2018 to 2021
$1.1M
NIGMS NIH HHS R01 GM120624NIGMS NIH HHS R01 GM131407
6 · The paper itself

Abstract

Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. We develop HiCBin, a novel open-source pipeline, to resolve high-quality MAGs utilizing Hi-C contact maps. HiCBin employs the HiCzin normalization method and the Leiden clustering algorithm and includes the spurious contact detection into binning pipelines for the first time. HiCBin is validated on one synthetic and two real metagenomic samples and is shown to outperform the existing Hi-C-based binning methods. HiCBin is available at https://github.com/dyxstat/HiCBin .

Indexed as

MetagenomeMicrobiotaAlgorithmsCluster AnalysisMetagenomicsContig binningLeiden clusteringMetagenomic Hi-CPotts modelSpurious contact detectionZero-inflated negative binomial normalization

Identifiers

PMID35227283
PMCPMC8883645

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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.