Comparison of whole mitochondrial genome variants between hair shafts and reference samples using massively parallel sequencing.

Hair shafts are one of the most common types of evidence at crime scenes, and mitochondrial DNA (mtDNA) has been analyzed as a valuable genetic marker for hair shafts in forensic casework. However, the mtDNA analysis strategy may vary according to the quantity and quality of DNA extracted from a forensic sample and the available massively parallel sequencing (MPS) platform in laboratories. Forensic practitioners often have to interpret mtDNA sequences exhibiting point heteroplasmy (PHP) that are analyzed using different analytical methods. In the present study, the whole mitochondrial genome (mtGenome) variants of hair shaft samples obtained from 20 donors, which were sampled in duplicate and stored at room temperature for > 1 year, were analyzed using the Precision ID mtDNA Whole Genome Panel and Ion S5 system. The whole mtGenome variants of 20 blood and 20 buccal swab samples (reference samples) from the hair shaft donors were analyzed using the Nextera XT DNA Library Prep Kit and MiSeq System. A total of 20 unique mtGenome haplotypes were observed, and 56 PHP variants were identified across the 4 sets of tissue. When the major nucleotide of PHP was considered, 16 of 20 haplotypes of the hair shaft samples matched those of the corresponding blood and buccal swab samples. In four donors, the major nucleotide of PHP was inverted at one nucleotide position between the hair shaft and reference samples. However, the data obtained on MPS, showing high PHP resolution, provided substantial information to avoid false exclusion when comparing two haplotypes containing PHP with inverted major nucleotides. In conclusion, the present study demonstrates the utility of MPS in forensic casework in the comparative analysis of mtGenome variants containing PHP.

BAMixChecker: an automated checkup tool for matched sample pairs in NGS cohort.

SUMMARY: Mislabeling in the process of next generation sequencing is a frequent problem that can cause an entire genomic analysis to fail, and a regular cohort-level checkup is needed to ensure that it has not occurred. We developed a new, automated tool (BAMixChecker) that accurately detects sample mismatches from a given BAM file cohort with minimal user intervention. BAMixChecker uses a flexible, data-specific set of single-nucleotide polymorphisms and detects orphan (unpaired) and swapped (mispaired) samples based on genotype-concordance score and entropy-based file name analysis. BAMixChecker shows ∼100% accuracy in real WES, RNA-Seq and targeted sequencing data cohorts, even for small panels (<50 genes). BAMixChecker provides an HTML-style report that graphically outlines the sample matching status in tables and heatmaps, with which users can quickly inspect any mismatch events. AVAILABILITY AND IMPLEMENTATION: BAMixChecker is available at https://github.com/heinc1010/BAMixChecker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.