ArticleGenes2023
MitoTrace: A Computational Framework for Analyzing Mitochondrial Variation in Single-Cell RNA Sequencing Data.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 66 citations in OpenAlex.
- Advances in diagnosis of mitochondrial dysfunction and primary mitochondrial disease.Disease models & mechanisms · 2026Review
- Article
- Characterization of Mitochondrial Double-Stranded RNA Levels in Non-Small Cell Lung Carcinoma.Cancer research communications · 2026Article
- MitoDelta: identifying mitochondrial DNA deletions at cell-type resolution from single-cell RNA sequencing data.BMC genomics · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Single-mitochondrion sequencing uncovers distinct mutational patterns and heteroplasmy landscape in mouse astrocytes and neurons.BMC biology · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
Abstract
Genetic variation in the mitochondrial genome is linked to important biological functions and various human diseases. Recent progress in single-cell genomics has established single-cell RNA sequencing (scRNAseq) as a popular and powerful technique to profile transcriptomics at the cellular level. While most studies focus on deciphering gene expression, polymorphisms including mitochondrial variants can also be readily inferred from scRNAseq. However, limited attention has been paid to investigate the single-cell landscape of mitochondrial variants, despite the rapid accumulation of scRNAseq data in the community. In addition, a diploid context is assumed for most variant calling tools, which is not appropriate for mitochondrial heteroplasmies. Here, we introduce MitoTrace, an R package for the analysis of mitochondrial genetic variation in bulk and scRNAseq data. We applied MitoTrace to several publicly accessible data sets and demonstrated its ability to robustly recover genetic variants from scRNAseq data. We also validated the applicability of MitoTrace to scRNAseq data from diverse platforms. Overall, MitoTrace is a powerful and user-friendly tool to investigate mitochondrial variants from scRNAseq data.
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Registered trials
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.