Decontamination Algorithm for Catheterized Urine 16S rRNA Sequencing Data
Contamination in low-biomass samples, such as urine, presents a major challenge for 16S rRNA sequencing, as extraneous DNA from reagents and the environment often obscures microbial signals. Existing in silico decontamination algorithms face limitations in accurately identifying and removing these contaminants. To address this issue, we developed Green Cleaner, a novel decontamination algorithm designed to enhance the accuracy of 16S rRNA sequencing data, specifically for catheterized urine samples. We evaluated Green Cleaner's performance using vaginal microbiome dilution experiments as a proxy for low-biomass urine samples and compared it to the SCRuB algorithm. Our results show that Green Cleaner consistently outperforms SCRuB across various contamination levels, with superior accuracy, F1-scores, and reduced beta-dissimilarity. Green Cleaner also demonstrated improved specificity and positive predictive value by correctly identifying and removing a higher number of contaminant amplicon sequence variants (ASVs). Furthermore, the reduced alpha diversity in the decontaminated datasets suggests more precise contaminant elimination. With its practical use of a single blank extraction control per batch and adjustable decontamination rules, Green Cleaner provides an efficient and scalable solution for real-world applications. Our findings highlight its potential to significantly advance urine microbiome research by delivering more accurate microbial profiles.