Received: Accepted: OctoPublished: November 11, 2021Ĭopyright: © 2021 Singh et al. PLoS Comput Biol 17(11):Įditor: Manja Marz, bioinformatics, GERMANY (2021) MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data. 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.Ĭitation: Singh LN, Ennis B, Loneragan B, Tsao NL, Lopez Sanchez MIG, Li J, et al. We provide an example of applying MitoScape in replicating an association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men. We show that MitoScape produces more accurate heteroplasmy estimates compared to published software. MitoScape takes advantage of rho-zero cell data to model the characteristics of NUMTs.
#MITOCHONDRIAL DNA SEQUENCE ANALYSIS SOFTWARE#
We introduce MitoScape, a novel, big-data, software which models mitochondrial genetics through machine learning to accurately identify mtDNA sequence from NGS data. Current software for analyzing mtDNA from NGS do not accurately model the unique characteristics of mitochondrial genetics. Identifying mtDNA sequence accurately is complicated by the presence of nuclear encoded mitochondrial sequences (NUMTs), which are homologous to mtDNA. 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. 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 improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics. 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). 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. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. 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. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. 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.
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