title: Mitochondria sequence mapping strategies and practicability of mitochondria variant detection from exome and RNA sequencing data.
abstract: The rapid progress in high-throughput sequencing has significantly enriched our capacity for studying the mitochondrial DNA (mtDNA). In addition to performing specific mitochondrial targeted sequencing, an increasingly popular alternative approach is using the off-target reads from exome sequencing to infer mtDNA variants, including single nucleotide polymorphisms (SNPs) and heteroplasmy. However, the effectiveness and practicality of this approach have not been tested. Recently, RNAseq data have also been suggested as a good source for alternative data mining, but whether mitochondrial variants can be detected from RNAseq data has not been validated. We designed a study to evaluate the practicability of mtDNA variant detection using exome and RNA sequencing data. Five breast cancer cell lines were sequenced through mitochondrial targeted, exome, and RNA sequencing. Mitochondrial targeted sequencing was used as the gold standard to compute the validation and false discovery rates of SNP and heteroplasmy detection in exome and RNAseq data. We found that exome and RNA sequencing can accurately detect mitochondrial SNPs. However, the lower false discovery rate makes exome sequencing a better choice for heteroplasmy detection than RNAseq. Furthermore, we examined three alignment strategies and found that aligning reads directly to the mitochondrial reference genome or aligning reads to the nuclear and mitochondrial references genomes simultaneously produced the best results, and that aligning to the nuclear genome first and afterwards to the mitochondrial genome performed poorly. In conclusion, our study provides important guidelines for future studies that intend to use either exome sequencing or RNAseq data to infer mitochondrial SNPs and heteroplasmy.
authors.forename: Pan
authors.initials: P
authors.lastname: Zhang
authors.forename: David C
authors.initials: DC
authors.lastname: Samuels
authors.forename: Brian
authors.initials: B
authors.lastname: Lehmann
authors.forename: Thomas
authors.initials: T
authors.lastname: Stricker
authors.forename: Jennifer
authors.initials: J
authors.lastname: Pietenpol
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
authors.forename: Yan
authors.initials: Y
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001943:Breast Neoplasms / Q000235:genetics*; D045744:Cell Line, Tumor; D004272:DNA, Mitochondrial / Q000235:genetics*; D059472:Exome / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics; D006801:Humans; D008969:Molecular Sequence Data; D009360:Neoplastic Cells, Circulating; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012334:RNA, Neoplasm / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D004272:DNA, Mitochondrial; D012334:RNA, Neoplasm
keywords: SNP; data mining; heteroplasmy; mitochondria