Mining technologies for functional gene markers of emerging contaminants
Water Security: Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants, Page: 273-287
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Book Chapter Description
Emerging contaminants (ECs) pose a significant threat to human health and environmental stability due to their high occurrence, biological toxicity, and ecological risks. Thus enhancing the removal of these pollutants and understanding their migration and transformation patterns is crucial. Microorganisms, driven by the metabolic diversity determined by functional degradation genes, are key to the degradation of ECs in nature. This chapter emphasized the necessity of continuous research on the microbial degradation of ECs and summarized common strategies for mining degradation genes and specifically focused on the operational processes of three traditional methods: genomic library construction, insertional mutagenesis library construction, and protein separation and purification. Moreover, this chapter detailed the existing high-throughput omics analysis methods for mining functional gene markers and revealing unknown metabolic pathways. Common analysis tools, including databases and software used in omics analysis, were also discussed. Overall, this chapter provides theoretical support and practical guidance for deciphering the biodegradation mechanisms of ECs and for the technological innovation in functional gene mining and analysis.
Bibliographic Details
Elsevier BV
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