ABSTRACT16S rRNA is a commonly used technique to identify prokaryotes. However, this approach could only reflect the proportion of sequencing reads, instead of actual cell fraction. To accurately represent actual cell fraction, bioinformatic tools that estimate 16S gene copy number (GCN) of unmeasured species are required. All the existing tools rely on taxonomy or phylogeny to predict 16S GCN. The present study develops a deep learning-based method that establishes a novel route. The proposed approach, i.e., Artificial Neural Network Approximator for 16S rRNA Gene Copy Number (ANNA16), essentially links 16S sequence string directly to GCN, without the construction of taxonomy or phylogeny. The computational analysis revealed the success of the deep learning model built on 16S GCN data retrieved from rrnDB (N = 19,520). The resulting deep learning model outperforms all the major and state-of-the-art algorithms in the literature. Shapley Additive exPlanations (SHAP) shows ANNA16 is capable of detecting informative positions and weighing K-mers unequally according to their informativeness to more effectively utilize the information contained in 16S sequence. For the accessibility of ANNA16, it will be released as a public, an end-to-end, web-based tool that predicts copy number from 16S rRNA sequence.

The emergence of antimicrobial resistance (AMR) is an urgent and complex public health challenge worldwide. As a sub-problem of AMR, antibacterial resistance (ABR) is of particular concern due to inadequacy of alternative medication. Earlier studies have shown that ABR is not only impacted by antibiotics, but also affected by the interactions between bacteria and their environments. Therefore, to combat ABR in a specific region, local environmental conditions must be investigated to comprehensively understand which environmental factors might contribute to ABR and propose more tailored solutions. This study surveyed environmental contributors of antibiotic resistance genes (ARGs), the parameter for measuring ABR, in the Yangtze Delta. A high abundance of ARGs was detected, despite low antibiotic and heavy metal concentrations. Phosphorus, chromium, manganese, calcium, and strontium were identified as potential key contributors of ARGs. Suppression of ARGs could be realized through decreasing the concentration of phosphorus in surface water. Group 2A light metals (e.g., magnesium and calcium) could be developed as eco-friendly reagents for controlling antibiotic resistance in the future.