Bioinformatic removal of NUMT-associated variants in mitotiling NGS data from whole blood samples

Nuclear mitochondrial DNA segments (NUMTs) have arisen due to the transposition of segments of the mitochondrial DNA genome (mitogenome) into the nuclear genome. When using a "mitotiling" strategy, NUMTs may be more readily amplified when targeting the entire mitogenome compared to the con...

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Bibliographic Details
Published inElectrophoresis
Main Authors Ring, Joseph D, Sturk-Andreaggi, Kimberly, Peck, Michelle A, Marshall, Charla
Format Journal Article
LanguageEnglish
Published Germany 26.08.2018
Subjects
Next Generation Sequencing (NGS)
Nuclear-Mitochondrial DNA Segments (NUMTs)
Mitochondrial DNA
Bioinformatics
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Summary:Nuclear mitochondrial DNA segments (NUMTs) have arisen due to the transposition of segments of the mitochondrial DNA genome (mitogenome) into the nuclear genome. When using a "mitotiling" strategy, NUMTs may be more readily amplified when targeting the entire mitogenome compared to the control region, as hundreds of primers are required for complete sequencing coverage. In samples with a high percentage of nuclear DNA copies per cell, such as whole blood, NUMT co-enrichment may be exacerbated. The present examined bioinformatic approaches to removing NUMTs and NUMT-associated variants (NAVs) from next-generation sequence data generated using two mitotiling kits (Precision ID and QIAseq). Across 16 samples with low mtDNA copy number, NUMT co-enrichment produced in 890 NAVs above 5% variant frequency. The use of the consensus sequence to eliminate NUMT reads proved to be effective for QIAseq data, and resulted in >85% NAV removal in Precision ID data. This method was bolstered by NAV filtering in Precision ID analysis. Alternative high stringency mapping to the revised Cambridge Reference Sequence (rCRS) and the human genome reference GRCh38 for the QIAseq data caused a reduction in mitogenome coverage without complete NUMT removal. These bioinformatic solutions facilitate mitotiling sequence data analysis for low-level variant detection. This article is protected by copyright. All rights reserved.
ISSN:1522-2683