Evidence map›Paper›PMID 38012712›Full record

ArticleHuman genomics2023

MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases.

Shuangshuang Huang, Zhaoyu Wu, Tong Wang, Rui Yu, Zhijian Song, Hao Wang

Open access · goldAbstract read
In one paragraph

Article in Human genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact, top 68% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 3 institutions in 1 country.

Shuangshuang HuangDepartment of Clinical Laboratory, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Zhaoyu WuDepartment of Clinical Laboratory, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Tong WangDepartment of Clinical Laboratory, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Rui YuDepartment of Ophthalmology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Zhijian SongOrigiMed, 5th Floor, Building 3, No.115 Xin Jun Huan Road, Minhang District, Shanghai, China. zhijiansong@126.com.
Hao WangDepartment of Clinical Laboratory, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China. wang_hao@zju.edu.cn.
Children's Hospital of Zhejiang University · CNGuangdong Medical College · CNUnimed Medical Institute · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields.

Indexed as

Computational BiologyMitochondrial DiseasesDNA, MitochondrialHigh-Throughput Nucleotide SequencingHumansMitochondriaMutation, MissenseDNA, MitochondrialGeneticsMachine learningPathogenicity predictorPrimary mitochondrial diseaseVariant

Identifiers

PMID38012712
PMCPMC10683248
OpenAlexW4389046054

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.