Gut microbiota plays an important role in the harvesting, storage, and expenditure of energy obtained from one’s diet. Our cross-sectional study aimed to identify differences in gut microbiota according to body mass index (BMI) in a Korean population. 16S rRNA gene sequence data from 1463 subjects were categorized by BMI into normal, overweight, and obese groups. Fecal microbiotas were compared to determine differences in diversity and functional inference analysis related with BMI. The correlation between genus-level microbiota and BMI was tested using zero-inflated Gaussian mixture models, with or without covariate adjustment of nutrient intake. We confirmed differences between 16Sr RNA gene sequencing data of each BMI group, with decreasing diversity in the obese compared with the normal group. According to analysis of inferred metagenomic functional content using PICRUSt algorithm, a highly significant discrepancy in metabolism and immune functions (P < 0.0001) was predicted in the obese group. Differential taxonomic components in each BMI group were greatly affected by nutrient adjustment, whereas signature bacteria were not influenced by nutrients in the obese compared with the overweight group. We found highly significant statistical differences between normal, overweight and obese groups using a large sample size with or without diet confounding factors. Our informative dataset sheds light on the epidemiological study on population microbiome.

The cost-effectiveness of whole exome sequencing (WES) remains controversial due to variant call variability, necessitating sensitivity and specificity evaluation. WES was performed by three companies (AA, BB, and CC) using reference standards composed of DNA from hydatidiform mole and individual blood at various ratios. Sensitivity was assessed by the detection rate of null–homozygote (N–H) alleles at expected variant allelic fractions, while false positive (FP) errors were counted for unexpected alleles. Sensitivity was approximately 20% for in-house results from BB and CC and around 5% for AA. Dynamic Read Analysis for GENomics (DRAGEN) analyses identified 1.34 to 1.71 times more variants, detecting over 96% of in-house variants, with sensitivity for common variants increasing to 5%. In-house FP errors varied significantly among companies (up to 13.97 times), while DRAGEN minimized this variation. Despite DRAGEN showing higher FP errors for BB and CC, the increased sensitivity highlights the importance of effective bioinformatic conditions. We also assessed the potential effects of target enrichment and proposed optimal cutoff values for the read depth and variant allele fraction in WES. Optimizing bioinformatic analysis based on sensitivity and specificity from reference standards can enhance variant detection and improve the clinical utility of WES.

Background: We previously developed a benchmarking strategy using mixtures of homozygote and heterozygote DNAs as reference standards to simultaneously assess sensitivity and false positive (FP) error rates in targeted next-generation sequencing (T-NGS) and whole-exome sequencing (WES), revealing substantial variability across commercial platforms. However, optimal analytic conditions for clinical application remain undefined. Methods: We systematically evaluated multiple sequencing kits and bioinformatics pipelines across various variant allele fraction (VAF) thresholds to identify conditions that maximize both sensitivity and specificity. Recurrent error-prone alleles were defined and filtered to enhance specificity. Results: Optimal performance was achieved using the DRAGEN pipeline with recurrent FP allele filtering. For T-NGS, a 1% VAF cutoff yielded a 95% detection threshold of 2.99% and 1.21 FPs per megabase (FP/Mb); for WES, a 2% cutoff yielded a 95% threshold of 5.02% and 1.15 FP/Mb. These settings improved sensitivity >3-fold and reduced FP rates >96% versus suboptimal pipelines. Notably, VAF thresholds flattened sensitivity differences across platforms, obscuring key performance disparities—challenging assumptions that T-NGS is inherently more sensitive than WES. In-house and conventional pipelines undercalled up to 10% of true variants. Restricting reporting of 1–4% VAF variants to ∼1,000 predefined actionable sites enabled recovery of clinically relevant mutations while reducing FP risk >99%. Conclusions: This study provides a quantitative framework for optimizing NGS performance. Our findings support actionable strategies to improve diagnostic accuracy in clinical genomics through tailored pipeline selection, VAF thresholding, and artifact filtering.