Bioinformatics Pipeline Detects Mitochondrial DNA Variants with High Accuracy

Researchers from Maastricht University have developed a novel bioinformatics pipeline that effectively detects homoplasmic and heteroplasmic mitochondrial DNA (mtDNA) single nucleotide variants (SNVs) in single-cell RNA sequencing (scRNA-seq) data. The pipeline includes quality control, alignment to the mitochondrial genome, SNV calling, and annotation, and filters out sequencing errors. This breakthrough could be instrumental in understanding the mechanism and clinical manifestation of mtDNA SNVs, which are associated with various pathologies, particularly in energy-demanding tissues like muscles and brain.

Key Takeaways:

• The developed bioinformatics pipeline is specifically designed for detecting mtDNA SNVs from scRNA-seq data, addressing existing pipeline limitations.
• The pipeline includes quality control, alignment to the mitochondrial genome, SNV calling, and annotation, and customizable coverage-dependent thresholds for detecting heteroplasmic SNVs.
• Duplicate reads are retained as valid biological duplicates, and strand bias errors, RNA modification-induced errors, and overrepresented SNVs are removed.
• The pipeline has demonstrated high accuracy in detecting homoplasmic and heteroplasmic mtDNA SNVs in scRNA-seq data.
• The research dataset utilized public single-cell RNA sequencing data as an invaluable resource for validating the pipeline's performance.
• Zhiling Guan, Rick Kamps, Hubert J. M. Smeets, and Patrick Lindsey collaborated on this study, with Lindsey serving as the primary contact for additional information.
• The pipeline has the potential to facilitate a better understanding of mitochondrial DNA SNVs and their clinical implications.

Statistics:

• Homoplasmic and heteroplasmic mtDNA SNVs were detected with high accuracy using the developed pipeline.
• The pipeline has been demonstrated to work effectively with scRNA-seq data from various samples.
• Customizable coverage-dependent thresholds indicate the pipeline's adaptability to diverse sequencing data requirements.
• 27.5% of the investigated samples contained at least one heteroplasmic mtDNA SNV.

Sources:

• A bioinformatics pipeline for identifying homoplasmic and heteroplasmic mitochondrial DNA SNVs in single-cell RNA-Seq datasets. Genomics, 2025:111122.
• Genomics. Academic Press Inc Elsevier Science, 525 B St, Ste 1900, San Diego, CA 92101-4495, USA. www.journals.elsevier.com/genomics.
• Maastricht University, Department of Translational Genomics.
• Zhiling Guan, Rick Kamps, and Hubert J. M. Smeets, Researchers at Maastricht University.