Evidence map›Paper›PMID 36376928›Full record

ArticleGenome biology2022

metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies.

Senying Lai, Shaojun Pan, Chuqing Sun, Luis Pedro Coelho, Wei-Hua Chen, Xing-Ming Zhao

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 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  16. Efficient Recovery of Complete Gut Viral Genomes by Combined Short- and Long-Read Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
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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

6 authors.

Senying LaiInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Shaojun PanInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Chuqing SunKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Luis Pedro CoelhoInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. luis@luispedro.org.
Wei-Hua ChenKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China. weihuachen@hust.edu.cn.
Xing-Ming ZhaoInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. xmzhao@fudan.edu.cn.ORCID 0000-0002-4531-3970

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC ( https://github.com/ZhaoXM-Lab/metaMIC ), a machine learning-based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results.

Indexed as

MetagenomeMetagenomicsAlgorithmsMachine LearningSequence Analysis, DNASoftwareBinningMetagenome-assembled genomesMetagenomic assembliesMisassembled contigsMisassembly breakpoints

Identifiers

PMID36376928
PMCPMC9661791

What OpenQuestion holds

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Registered trials

None linked

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.