Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities

Objective evaluation of sequencing resolution is crucial for comparing technologies and ensuring reproducibility in microbiome analysis. Specifically, a systematic approach is necessary to quantitatively assess the effect of various platforms and experimental conditions on species-level resolution. Therefore, this study quantitatively evaluated multiple strategies, including 16S V3-V4 (16P), full-length 16S rRNA gene (16F), and whole metagenome shotgun sequencing (WMS), using a commercial DNA-based mock community (MC) and a domestically developed whole-cell MC (Korea MC [KMC]). The WMS strategy included 12 combinations of input DNA concentrations and sequencing output levels. A total of 64 WMS libraries were constructed for KMC samples, and 112 sequencing datasets were analysed. Taxonomic resolution was assessed using an adjusted F1-score integrating detection sensitivity and abundance-level reproducibility. Qualitatively examining the detected species against the expected species across platforms, WMS showed a true positive abundance ratio of over 90%, 16F was observed to have an average of 60%, and 16P was observed to have an average of less than 10%. The combination of 10 ng input and 10 gigabases output consistently yielded the highest species-level resolution. However, reduced performance was observed in some MCs under 1 ng or 100 ng DNA input conditions. Detection sensitivity varied by taxon and condition. Specifically, Streptococcus pneumoniae and Cryptococcus neoformans were detected only under high-input or -output conditions, whereas Escherichia coli exhibited optimal accuracy at intermediate inputs. Acinetobacter species demonstrated reduced resolution as input DNA increased. KMC samples showed species- and format-specific variability in DNA extraction efficiency. This study presents a quantitative evaluation of species-level resolution across sequencing conditions using defined mock communities. The results highlight how sequencing configuration and taxon-specific characteristics can influence detection performance and provide insights for interpreting microbiome sequencing results under different experimental conditions.