Shotgun sequencing (SGS) can be a useful analysis method for low-quality and low-quantity forensic trace samples. Using SGS, all DNA present in a sample is analysed, including nuclear (nuDNA) and mitochondrial DNA (mtDNA). In cases where nuDNA analysis is not feasible, mtDNA may serve as an alternative target to study, as it is more stable and occurs in higher copy numbers than nuDNA. 

This study aimed to analyse mtDNA in SGS data generated from various biological materials, including telogen hairs, together with the corresponding reference samples (buccal swabs and blood samples). A freeware pipeline for accurate mtDNA analysis in SGS data was developed, and its performance was compared with the commercially available CLC Genomics Workbench software (Qiagen) with the AQME plugin (hereafter referred to as CLC-AQME).

Samples were collected and pre-processed as part of previous studies evaluating nuDNA analysis from SGS data. The Mitomics pipeline employs the freeware tools BWA-MEM, Samtools, and GATK and incorporates a multi-mapping strategy in which reads are aligned to both the rCRS and a sample-specific consensus sequence to minimise interference from Nuclear Mitochondrial DNA segments (NUMTs). The impact of SGS data pre-processing and duplicate-read removal on mtDNA analysis was assessed and compared with results obtained from CLC-AQME analyses to benchmark the performance of the Mitomics pipeline to a widely used commercial analysis method.

Overall, the results showed that mtDNA profiling with SGS is feasible using a minimum read threshold of 100x and a heteroplasmy threshold of 5%. However, analyses performed with a 20x read threshold and 10% heteroplasmy threshold yielded more accurate haplotypes, especially for low-quality samples. Although duplicate reads can confound the interpretation of results, more accurate mtDNA profiles were obtained when duplicate reads were not removed. The Mitomics pipeline obtained more accurate mtDNA profiles compared to the commercial analysis method, CLC-AQME. On the other hand, the Mitomics pipeline was more labour-intensive and required more manual inspection of the sequence data, whereas CLC-AQME provided a user-friendly, integrated interface for analysis.

Authors

• Floor Claessens (Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark)
• Marie-Louise Kampmann (Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark)
• Jeppe Dyrberg Andersen (Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark)
• Walther Parson (Institute of Legal Medicine, Medical University of Innsbruck, Austria)
• Claus Børsting (Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark)
• Vania Pereira (Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark)