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A eukaryotic cell can have up to three different types of genetic systems: nuclear, mitochondrial, and chloroplast. During evolution, organelles have exported many genes to the nucleus; this transfer is still ongoing in some plant species. Approximately 18% of the Arabidopsis thaliana nuclear genome is thought to be derived from the chloroplast’s cyanobacterial ancestor, and around 75% of the yeast genome derived from the mitochondria’s bacterial ancestor. This export has occurred...
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Related Experiment Video

Updated: Oct 13, 2025

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MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing

Larry N Singh1, Brian Ennis2, Bryn Loneragan3

  • 1Center for Mitochondrial and Epigenomic Medicine, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.

Plos Computational Biology
|November 11, 2021
PubMed
Summary
This summary is machine-generated.

MitoScape accurately extracts mitochondrial DNA variants from next-generation sequencing data, improving disease association discoveries. This novel software enhances precision for personalized medicine and clinical diagnostics.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data for studying complex diseases.
  • Mitochondrial DNA (mtDNA) variants, especially heteroplasmic ones, significantly influence human disease severity.
  • Existing software for mtDNA variant analysis from NGS data often lack accuracy due to unique mitochondrial genetics.

Purpose of the Study:

  • Introduce MitoScape, a novel big-data software for accurate extraction of mitochondrial DNA sequences from NGS data.
  • Address limitations in current mtDNA variant analysis tools by incorporating machine learning and rho-zero data.
  • Facilitate discoveries in heteroplasmy-disease associations for improved diagnostics.

Main Methods:

  • Developed MitoScape, a big-data software utilizing machine learning to model unique mitochondrial genetics.
  • Employed rho-zero (mtDNA-depleted) data to model nuclear-encoded mitochondrial sequences.
  • Validated MitoScape's accuracy using gold-standard mtDNA data and compared its performance against common variant callers.

Main Results:

  • MitoScape demonstrated superior performance compared to existing tools in heteroplasmy estimation, with significant improvements in reducing false positives and negatives.
  • The software accurately estimated heteroplasmy levels using gold-standard mtDNA data.
  • MitoScape facilitated the discovery of heteroplasmy-disease associations, reinforcing a link between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003).

Conclusions:

  • MitoScape provides highly accurate mitochondrial DNA variant analysis from NGS data.
  • The software's enhanced accuracy is crucial for personalized medicine, clinical diagnostics, and advancing our understanding of mitochondrial genetics in complex diseases.
  • MitoScape is instrumental in uncovering critical heteroplasmy-disease associations.