Evidence map›Paper›PMID 38284225›Full record

ArticleCurrent protocols2024

MSeqDR Quick-Mitome (QM): Combining Phenotype-Guided Variant Interpretation and Machine Learning Classifiers to Aid Primary Mitochondrial Disease Genetic Diagnosis.

Lishuang Shen, Marni J Falk, Xiaowu Gai

Open access · greenAbstract read
In one paragraph

Article in Current protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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

3 authors at 3 institutions in 1 country.

Lishuang ShenCenter for Personalized Medicine, Department of Pathology & Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-0436-0199
Marni J FalkMitochondrial Medicine Frontier Program, Division of Human Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Xiaowu GaiCenter for Personalized Medicine, Department of Pathology & Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California, USA.
Children's Hospital of Los Angeles · USChildren's Hospital of Philadelphia · USUniversity of Southern California · US

Funding

The Intellectual and Developmental Disabilities Research Center (IDDRC) at CHOP/PennP50HD105354 · NICHD · CHILDREN'S HOSP OF PHILADELPHIA · PI ERIC D MARSH, ROBERT Thomas SCHULTZ · 2021 to 2026
$9.2M
Primary Mitochondrial Disease Expert Curation PanelU24HD093483 · NICHD · CHILDREN'S HOSP OF PHILADELPHIA · PI FALK, MARNI J, GAI, XIAOWU · 2017 to 2023
$2.2M
NICHD NIH HHS P50 HD105354NICHD NIH HHS U24 HD093483
6 · The paper itself

Abstract

The international Mitochondrial Disease Sequence Data Resource Consortium (MSeqDR) Quick-Mitome (QM) is a web-based platform enabling automated variant interpretation of whole-exome sequencing (WES) datasets for the genetic diagnosis of primary mitochondrial diseases (PMD). Designed specifically to address the unique dual genome nature of PMD etiologies, QM includes features for both nuclear and mitochondrial DNA (mtDNA) genome analysis. QM requires VCF variant lists, HPO ID clinical phenotypes, and pedigree files for multiple-sample VCF inputs. QM maps phenotypes to HPO terms before analysis. QM analysis requires 2 to 20 min for 100,000 variants on an 8-vCPU AWS server using Exomiser's "PASS_ONLY" mode for nuclear variants. QM ranks variants based on allele frequency, phenotype-gene association, functional impact, and inheritance mode. Variants are further annotated with multiple data sources such as OMIM, ClinVar, dbNSFP, gnoMAD, MITOMAP, and MSeqDR. In addition to standard Exomiser results, QM generates an Analysis Report and QM Integrated Report with add-on mtDNA-specific analyses, including haplogroup prediction with Phy-Mer, heteroplasmy calculation, and mvTool annotations. We developed the Mitochondrial Disease Variant (MDV) classifier using XGBoost to predict variant pathogenicity for PMD. The MDV classifier was trained on >120 features and performance benchmarking showed that it correctly classified >98% of nuclear gene variants as being pathogenic or benign, and predicted PMD-causing variants with 94% precision. The MSeqDR QM server is an open-access resource for phenotype-driven dual-genome analyses for PMD diagnosis by the global mitochondrial disease community. It is publicly available for non-commercial, non-clinical research use at https://mseqdr.org/quickmitome.php. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Standardizing clinical phenotypes into human phenotype ontology (HPO) terms as the phenotype input for Quick-Mitome (QM) Basic Protocol 2: Prepare the pedigree input for multiple-sample VCF Basic Protocol 3: Quick-Mitome (QM) analysis Basic Protocol 4: Reviewing and understanding the QM Integrated Report and Analysis Report.

Indexed as

Mitochondrial DiseasesDNA, MitochondrialHumansMachine LearningMitochondriaPhenotypeDNA, Mitochondrialexomiserheteroplasmyhuman phenotype ontology (HPO)mitochondrial disease diagnosismitochondrial disease variant (MDV) classifiermtDNA variantwhole exome sequencing (WES)

Identifiers

PMID38284225
PMCPMC11046528
OpenAlexW4391325852

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.