Green Cleaner: Advanced Decontamination Algorithm for Catheterized Urine 16S rRNA Sequencing Data

Abstract Background Contamination of low-biomass samples, such as urine, is a significant challenge in 16S rRNA sequencing. The presence of extraneous DNA in reagents and the environment often obscures microbial DNA, making it difficult to identify and remove contaminants. In silico decontamination algorithms developed so far still have some limitations in identifying and removing contaminants accurately. In this study, we developed a novel decontamination algorithm, Green Cleaner, to enhance the accuracy of 16S rRNA sequencing data by effectively distinguishing and removing contaminants especially from catheterized urine samples. Results We evaluated the performance of Green Cleaner against SCRuB using a series of vaginal microbiome dilution experiments as a proxy for low-biomass urine samples. Our results demonstrate that Green Cleaner outperforms SCRuB across all contamination levels, with higher accuracy, F1-score, and lower beta-dissimilarity. Specifically, Green Cleaner showed improved specificity and positive predictive value (PPV), correctly removing more contaminant amplicon sequence variant (ASV) features than SCRuB did. This was evidenced by the more diminished alpha diversity of the decontamination results in Green Cleaner than SCRuB, indicating a more precise elimination of contaminants by Green Cleaner. Conclusions Green Cleaner offers a robust solution for decontaminating 16S rRNA sequencing data from low-biomass samples, particularly catheterized urine samples, thus addressing the key limitations of the existing methods. By utilizing a single blank extraction control per batch and a set of intuitive and adjustable decontamination rules, Green Cleaner provides a practical and efficient approach for real-world applications. Our findings suggest that Green Cleaner has the potential to substantially advance urine microbiome research by providing more accurate and reliable microbial profiles.