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MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data
- Source :
- PLoS Computational Biology, Vol 17, Iss 11, p e1009594 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.<br />Author summary Recent studies have highlighted the importance of mitochondrial DNA variation in both primary mitochondrial disease and complex, human pathology including COVID-19, and space-flight stress. The vast amount of existing, next-generation sequencing (NGS) data can be leveraged to interrogate both nuclear and mitochondrial DNA (mtDNA) sequence simultaneously, allowing for analysis of the interplay between mitochondrial and nuclear encoded genes in mitochondrial function. Identifying mtDNA sequence accurately is complicated by the presence of nuclear encoded mitochondrial sequences (NUMTs), which are homologous to mtDNA. Current software for analyzing mtDNA from NGS do not accurately model the unique characteristics of mitochondrial genetics. We introduce MitoScape, a novel, big-data, software which models mitochondrial genetics through machine learning to accurately identify mtDNA sequence from NGS data. MitoScape takes advantage of rho-zero cell data to model the characteristics of NUMTs. We show that MitoScape produces more accurate heteroplasmy estimates compared to published software. We provide an example of applying MitoScape in replicating an association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men. MitoScape is an important contribution to mitochondrial genomics allowing for accurate mtDNA variants, and the ability to tailor mtDNA analysis in different population and disease contexts, which is not available in other software.
- Subjects :
- Big Data
Heredity
Mitochondrion
computer.software_genre
Biochemistry
Haplogroup
Machine Learning
Database and Informatics Methods
Sequencing techniques
DNA sequencing
Biology (General)
Energy-Producing Organelles
Ecology
Software Engineering
High-Throughput Nucleotide Sequencing
Genomics
Mitochondrial DNA
Heteroplasmy
Mitochondria
Nucleic acids
Genes, Mitochondrial
Computational Theory and Mathematics
Modeling and Simulation
Engineering and Technology
Cellular Structures and Organelles
Sequence Analysis
Transcriptome Analysis
Research Article
Next-Generation Sequencing
Computer and Information Sciences
Forms of DNA
Bioinformatics
QH301-705.5
Context (language use)
Bioenergetics
Biology
Research and Analysis Methods
Machine learning
DNA, Mitochondrial
Computer Software
Cellular and Molecular Neuroscience
Artificial Intelligence
Genetics
Humans
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Evolutionary Biology
Biology and life sciences
Population Biology
business.industry
Computational Biology
DNA
Cell Biology
Genome Analysis
Molecular biology techniques
Haplogroups
Personalized medicine
Artificial intelligence
business
Sequence Alignment
computer
Population Genetics
Human mitochondrial DNA haplogroup
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....0f4db12b9845cf40895c13494ceda6aa