An innovative data analysis strategy for accurate next-generation sequencing detection of tumor mitochondrial DNA mutations

Abstract

Next-generation sequencing technology has been commonly applied to detect mitochondrial DNA (mtDNA) mutations, which are reported to be strongly associated with cancers. However, several key challenges still exist regarding bioinformatics analysis of mtDNA sequencing data that greatly affect the detection accuracy of mtDNA mutations. Here we comprehensively evaluated several key analysis procedures in three different sample types. We found that a trimming procedure was essential for improving mtDNA mapping performance in plasma but not tissue samples. Mapping with a revised Cambridge reference sequence and human genome 19 reference was strongly suggested for mtDNA mutation detection in plasma samples because of the extreme abundance of nuclear DNA of mitochondrial origin. Moreover, our results showed that a setting of 3 mismatches was most appropriate for mtDNA mutation calling. Importantly, we revealed the presence of a negative logarithmic relationship between mtDNA site sequencing depth and minimum detectable mutation frequency and built an innovative and efficient filtering strategy to increase the accuracy and sensitivity of mutation detection. Finally, we verified that higher sequencing depth was required for a PCR-based compared with a capture-based enrichment strategy. We established an innovative data analysis strategy that is of great significance for improving the accuracy of mtDNA mutation detection for different types of tumor samples.

Keywords

• mtDNA mutation
• next-generation sequencing
• bioinformatics