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BMC Bioinformatics

Open Access

Practicality of identifying mitochondria variants from exome and RNAseq data

  • Pan Zhang1,
  • David C Samuels2,
  • Brian Lehmann3,
  • Thomas Stricker4,
  • Jennifer Pietenpol3,
  • Yu Shyr1 and
  • Yan Guo1Email author
BMC Bioinformatics201516(Suppl 15):P6

Published: 23 October 2015


The rapid progress in high throughput sequencing technology has significantly enriched our capability to study mitochondria genomes. Other than performing mitochondria targeted sequencing, an increasingly popular alternative approach is to utilize the off-target reads from exome sequencing to infer mitochondria genomic variants including SNP and heteroplasmy[19]. However, the effectiveness and practicality of such an approach has not been tested. Recently, RNAseq data has also been suggested as good source for alternative data mining[10, 11], but whether mitochondria variants are minable has not been studied.

Materials and methods

We designed a specific study using targeted mitochondria sequencing data as a gold standard to evaluate the practicality of SNP and heteroplasmy detection using exome sequencing and RNAseq data. Five breast cancer cell lines were sequenced for mitochondria targeted sequencing, exome sequencing, and RNAseq. Furthermore, we examined three mitochondria alignment strategies: 1) align all reads directly to the mitochondria genome; 2) align all reads to the nuclear genome and mitochondria genome simultaneously; 3) align all reads to the nuclear genome first, then used the unaligned reads to align to the mitochondria genome.


Our analyses found that exome sequencing can accurately detect mitochondria SNPs and can detect a portion of the true heteroplasmies with a reasonable false discovery rate. RNAseq data on the other hand had a lower detection rate of SNP but higher detection rate for heteroplasmy. However, the higher false discovery rate makes RNAseq a less ideal source for studying mitochondria compared to exome sequencing data. Furthermore, we found that aligning all reads directly to the mitochondria genome reference or aligning all reads to the nuclear genome and mitochondria genome references simultaneously produced the best results.


Exome sequencing and RNAseq data can be potentially mined for mitochondria variants. Overall, exome sequencing provides less false discovery than RNAseq for mitochondria variant detection, making it a more desirable choice. In conclusion, our study provides important guidelines for future studies that intend to use exome sequencing or RNAseq data to infer mitochondria SNP and heteroplasmy.

Authors’ Affiliations

Center for Quantitative Sciences, Vanderbilt University
Center for Human Genetics Research, Vanderbilt University
Department of Biochemistry, Vanderbilt University
Department of Pathology, Vanderbilt University


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© Zhang et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.