Metagenomes from uncultured microorganisms are rich resources for novel enzyme genes. The methods used to screen the metagenomic libraries fall into two categories, which are based on sequence or function of the enzymes. The sequence-based approaches rely on the known sequences of the target gene families. In contrast, the function-based approaches do not involve the incorporation of metagenomic sequencing data and, therefore, may lead to the discovery of novel gene sequences with desired functions. In this review, we discuss the function-based screening strategies that have been used in the identification of enzymes from metagenomes. Because of its simplicity, agar plate screening is most commonly used in the identification of novel enzymes with diverse functions. Other screening methods with higher sensitivity are also employed, such as microtiter plate screening. Furthermore, several ultra-high-throughput methods were developed to deal with large metagenomic libraries. Among these are the FACS-based screening, droplet-based screening, and the in vivo reporter-based screening methods. The application of these novel screening strategies has increased the chance for the discovery of novel enzyme genes.

Biological nitrogen removal (BNR) technologies are the most effective approaches for the remediation of environmental nitrogen pollutants from wastewater treatment plants (WWTPs). Presently, research is going on to elucidate the structure and function of BNR microbial communities and optimizing BNR treatment systems to enhance nitrogen removal efficiency. The literature on BNR microbial communities and experimental datasets is not unified across various repositories, while a uniform resource for the collection, annotation, and structuring of these BNR datasets is still unavailable. Herein, we present the Biological Nitrogen Removal Database (BNRdb), an integrated resource containing various manually curated BNR-related data. At present, BNRdb contains 23308 microbial strains, 46 gene families, 24 enzymes, 18 reactions, 301 BNR treatment datasets, 860 BNR-associated next-generation sequencing datasets, and 6 common BNR bioreactor systems. BNRdb provides a user-friendly interface enabling interactive data browsing. To our knowledge, BNRdb is the first BNR data resource that systematically integrates BNR data from archaeal, bacterial, and fungal communities. We believe that BNRdb will contribute to a better understanding of BNR process and nitrogen bioremediation research.

Microbial biodegradation of persistent organic pollutants (POPs) is an attractive, ecofriendly, and cost‐efficient clean‐up technique for reclaiming POP‐contaminated environments. In the last few decades, the number of publications documenting POP‐degrading microbes, enzymes, and experimental data sets has continuously increased, necessitating the development of a dedicated web resource that catalogs consolidated information on POP‐degrading microbes and tools to facilitate integrative analysis of POP degradation data sets. To address this knowledge gap, we developed the Microbial Biodegradation of Persistent Organic Pollutants Database (mibPOPdb) by accumulating microbial POP degradation information from the public domain and manually curating published scientific literature. Currently, in mibPOPdb, there are 9215 microbial strain entries, including 184 gene (sub)families, 100 enzymes, 48 biodegradation pathways, and 593 intermediate compounds identified in POP‐biodegradation processes, and information on 32 toxic compounds listed under the Stockholm Convention environmental treaty. Besides the standard database functionalities, which include data searching, browsing, and retrieval of database entries, we provide a suite of bioinformatics services to facilitate comparative analysis of users' own data sets against mibPOPdb entries. Additionally, we built a Graph Neural Network‐based prediction model for the biodegradability classification of chemicals. The predictive model exhibited a good biodegradability classification performance and high prediction accuracy. mibPOPdb is a free data‐sharing platform designated to promote research in microbial‐based biodegradation of POPs and fills a long‐standing gap in environmental protection research. Database URL: http://mibpop.genome-mining.cn/