Skip to main content
  • Poster presentation
  • Open access
  • Published:

Practicality of identifying mitochondria variants from exome and RNAseq data


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.


  1. Samuels DC, Han L, Li J, Quanghu S, Clark TA, Shyr Y, Guo Y: Finding the lost treasures in exome sequencing data. Trends Genet. 2013, 29 (10): 593-599. 10.1016/j.tig.2013.07.006.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Ye F, Samuels DC, Clark T, Guo Y: High-throughput sequencing in mitochondrial DNA research. Mitochondrion. 2014, 17: 157-163.

    Article  CAS  PubMed  Google Scholar 

  3. Picardi E, Pesole G: Mitochondrial genomes gleaned from human whole-exome sequencing. Nature Methods. 2012, 9 (6): 523-524. 10.1038/nmeth.2029.

    Article  CAS  PubMed  Google Scholar 

  4. Guo Y, Li J, Li CI, Shyr Y, Samuels DC: MitoSeek: extracting mitochondria information and performing high-throughput mitochondria sequencing analysis. Bioinformatics. 2013, 29 (9): 1210-1211. 10.1093/bioinformatics/btt118.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Dinwiddie DL, Smith LD, Miller NA, Atherton AM, Farrow EG, Strenk ME, Soden SE, Saunders CJ, Kingsmore SF: Diagnosis of mitochondrial disorders by concomitant next-generation sequencing of the exome and mitochondrial genome. Genomics. 2013, 102 (3): 148-156. 10.1016/j.ygeno.2013.04.013.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Falk MJ, Pierce EA, Consugar M, Xie MH, Guadalupe M, Hardy O, Rappaport EF, Wallace DC, LeProust E, Gai XW: Mitochondrial Disease Genetic Diagnostics: Optimized Whole-Exome Analysis for All MitoCarta Nuclear Genes and the Mitochondrial Genome. Discov Med. 2012, 79: 389-U140.

    Google Scholar 

  7. Nemeth AH, Kwasniewska AC, Lise S, Schnekenberg RP, Becker EBE, Bera KD, Shanks ME, Gregory L, Buck D, Cader MZ, Talbot K, De Silva R, Fletcher N, Hastings R, Jayawant S, Morrison PJ, Worth P, Taylor M, Tolmie J, O'Regan M, Consortium UA, Valentine R, Packham E, Evans J, Seller A, Ragoussis J: Next generation sequencing for molecular diagnosis of neurological disorders using ataxias as a model. Brain. 2013, 136: 3106-3118. 10.1093/brain/awt236.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Sevini F, Giuliani C, Vianello D, Giampieri E, Santoro A, Biondi F, Garagnani P, Passarino G, Luiselli D, Capri M, Franceschi C, Salvioli S: mtDNA mutations in human aging and longevity: controversies and new perspectives opened by high-throughput technologies. Exp Gerontol. 2014, 56: 234-244.

    Article  CAS  PubMed  Google Scholar 

  9. McMahon S, LaFramboise T: Mutational patterns in the breast cancer mitochondrial genome, with clinical correlates. Carcinogenesis. 2014, 35 (5): 1046-1054. 10.1093/carcin/bgu012.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Han L, Vickers KC, Samuels DC, Guo Y: Alternative applications for distinct RNA sequencing strategies. Brief Bioinform. 2014, 16 (4): 629-639.

    Article  PubMed  Google Scholar 

  11. Vickers KC, Roteta LA, Hucheson-Dilks H, Han L, Guo Y: Mining diverse small RNA species in the deep transcriptome. Trends Biochem Sci. 2015, 40 (1): 4-7. 10.1016/j.tibs.2014.10.009.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yan Guo.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, P., Samuels, D.C., Lehmann, B. et al. Practicality of identifying mitochondria variants from exome and RNAseq data. BMC Bioinformatics 16 (Suppl 15), P6 (2015).

Download citation

  • Published:

  • DOI: