This website provides a comprehensive platform for researching microbial communities and quorum sensing (QS) mechanisms in wastewater treatment. Through the AS Database, QSB Database, and AHLS-pred Server, it supports in-depth analysis and enhancement of microbial performance to improve the efficiency and stability of wastewater treatment systems. The Global Microbial Database of Activated Sludge is organized into three main modules, each offering unique insights and tools for wastewater research and application.

The Global Microbial Database of Activated Sludge provides comprehensive resources that enhance the study of microbial diversity and functionality in wastewater treatment. The AS Database offers extensive metagenomic data from various global sources, facilitating detailed microbial diversity analyses. In parallel, the QSB Database serves as an invaluable tool for exploring quorum sensing mechanisms, presenting a wealth of QS functional proteins and interaction networks that illustrate the complexities of microbial communication. Additionally, the AHLS-pred Server employs advanced machine learning techniques to accurately identify AHLs synthase bacteria, thereby advancing research into quorum sensing and its applications in improving wastewater treatment efficiency.

Module 1: AS Database

Overview: The AS Database compiles metagenomic data from activated sludge samples across 13 countries, totaling 469 projects and 14.68 TB of raw sequence data. The data undergoes rigorous processing, resulting in 2,326 high-quality metagenome-assembled genomes (MAGs).

Features: The database includes global sample distribution, microbial sequence fragments, and high-quality genome data, supporting diversity analysis, abundance analysis, functional annotation, and metabolic modeling.

Implementation: Through a comprehensive data processing workflow, the AS Database ensures high data quality and reliability, supporting diversity research in activated sludge microbial communities.

Module 2: QSB Database

Overview: The QSB Database is dedicated to QS microorganisms in wastewater treatment. It integrates data from SigMol, Quorumpeps, and UniProt, featuring over 193,306 QS functional protein entries. Based on this data, QS signaling networks reveal patterns of microbial communication.

Features: The QS signaling networks showcase intra- and inter-species interactions within the activated sludge microbial communities, helping researchers understand structural and functional dynamics.

Implementation: Using machine learning and manual verification, the QSB Database expands and refines data, building a comprehensive QS signaling network that serves as a resource for studying QS microbial interactions.

Module 3: AHLS-pred Server

Overview: AHLS-pred Server is the first machine learning-based prediction platform for AHLs synthase (AHLS) bacteria. Built with carefully curated training and testing datasets, the model effectively identifies potential AHLs-producing bacteria.

Features: The model achieves high-accuracy AHLS predictions, facilitating analysis of QS microbial distribution and potential enhancements in wastewater treatment applications.

Implementation: By using support vector machine (SVM) and other machine learning algorithms, AHLS-pred Server provides a reliable tool for AHLS bacterial prediction, supporting QS research and application.