## 3 Results
As illustrated in Figure 4, AS consistently demonstrated superior depth across all examined genes (CYP1A2, CYP2E1, GSTP1, and CYP3A4), with coverage extending uniformly throughout intronic and exonic regions. This enhanced resolution enabled the identification of additional genetic variants that were either poorly covered or entirely missed by the WGS approach. Quantitative assessment of the sequence data confirmed that AS yielded a mean coverage depth approximately 3.5-fold higher than WGS across the targeted ADME gene panel. This improved coverage directly translated to more comprehensive haplotype determination, with AS detecting 27% more variants of potential pharmacogenomic significance. Notably, several clinically relevant alleles in CYP3A4 and CYP2E1 were robustly identified in the AS dataset but fell below detection thresholds in the WGS data, highlighting the practical advantage of the AS methodology for pharmacogenomic applications. Adaptive Sampling with Linearization Enables Comprehensive Mitochondrial Genome Coverage and Reveals Distinct Profiles Compared to WGS  
Comprehensive and uniform coverage of the mitochondrial genome is critical for the accurate detection of heteroplasamy, identification of structural variants, and assessment of mitochondrial DNA integrity--factors with significant implications for cellular metabolism, disease pathogenesis, and aging processes. Our approach specifically addresses the challenges of mitochondrial genome analysis by employing Pvull-HF enzymatic linearization, combined with adaptive sampling, a methodology designed to overcome the traditional limitations of circular genome sequencing. The analysis of mitochondrial DNA coverage profiles revealed striking differences between adaptive sampling (AS) and conventional whole genome sequencing (WGS) approaches in the primary MSC cell line. Circular visualization of the mitochondrial genome demonstrated substantially higher and more consistent coverage using AS (Figures 5A and S6), with coverage depth represented by a _viridis_ colormap where darker purple regions indicate higher coverage. A sharp drop-off in  
Figure 4: (**A**) Comparison of the calculated weighted average sequencing coverage depth across selected ADME genes for targeted sequencing (AS, (**left panel**)) and whole genome sequencing (WGS, (**right panel**)). (**B**) Comparative sequence coverage and genetic variation detection in key ADME genes using adaptive sampling (AS) versus whole genome sequencing (WGS). The figure displays coverage depth, genetic variations, and haplotype representation for four critical ADME genes: CYP1A2, CYP2E1, GSTP1, and CYP3A4. For each gene, the upper panels represent data obtained through adaptive sampling (AS), while the lower panels show data from whole genome sequencing (WGS). The AS approach consistently demonstrates higher sequencing depth and more comprehensive coverage across all gene regions, enabling enhanced detection of genetic variants and more accurate haplotype resolution. Blue bars indicate exonic regions, with vertical blue lines marking the positions of identified variants. Gray bars represent individual sequence reads, with colored positions highlighting detected variants. This improved resolution provided by AS facilitates more accurate and complete pharmacogenomic profiling compared to standard WGS. read coverage is visible at the cleavage site where PvuII-HF cuts (m.2652). On both sides of this site, coverage spikes are observed, as most reads start at these positions and are, therefore, more likely to pass through the nanopores. Figure 5: Comparison of mitochondrial DNA coverage profiles between adaptive sampling and whole genome sequencing approaches in primary MSC cell line. **(A)** Circular visualization of mitochondrial DNA coverage using adaptive sampling (AS). The inner circle shows gene positions, while the outer ripple plot represents sequencing depth.