Single‐target contamination modeling in pathogen metagenomic sequencing via simple linear regression

Abstract

A simple linear regression framework was developed to quantify contamination in pathogen metagenomic next‐generation sequencing (mNGS). The model was trained on simulated contamination samples generated by spiking HeLa cell backgrounds (1 × 10 ⁵ cells/mL) with gradient concentrations of Streptococcus pneumoniae positive standards (5.6 × 10 ⁶ copies/mL) to cover admixture ratios of 0.05%–10% (2.8 × 10 ³ to 5.6 × 10 ⁵ copies/mL). Samples were independently generated as two sets: a modeling set ( n = 178) and an independent validation set ( n = 32). Metagenomic libraries were prepared and sequenced, followed by quality trimming (fastp) and host sequence removal (Kraken2); non‐host normalized reads were calculated using a custom database based on the S. pneumoniae reference genome (NC_003028.3). After refinement via outlier exclusion and model rebuilding, the final model achieved an R ² of 0.966 and an overall MAPE of 16.46% on an independent validation set, supporting clinically actionable contamination monitoring pending multi‐species validation.