Application of advanced genomic tools in food safety rapid diagnostics: challenges and opportunities. (October 2022)

• Record Type: Journal Article
• Authors: Banerjee, Goutam; Agarwal, Saumya; Marshall, Austin; Jones, Daleniece H; Sulaiman, Irshad M; Sur, Shantanu; Banerjee, Pratik
• Is Part Of: Current opinion in food science. Volume 47(2022)
• Publication Date: 2022-10
• Subjects: Food -- Microbiology -- Periodicals; Food -- Analysis -- Periodicals; Food -- Biotechnology -- Periodicals; Food -- Periodicals; Food -- Research -- Periodicals; Nutrition -- Periodicals; Food -- Biotechnology; Food -- Microbiology; Food -- Analysis; Food; Food -- Research; Nutrition; Food Technology -- Periodicals; Food Analysis -- Periodicals; Food Microbiology -- Periodicals; Periodicals; Electronic journals; 641.3
• DOI: 10.1016/j.cofs.2022.100886
• Languages: English
• ISSNs: 2214-7993

Abstract

Abstract : The increasing adoption of genomics and sequencing-based approaches to detect foodborne pathogens has led to a new direction in food safety diagnostics. Sequencing technologies such as next-generation sequencing are used to confirm the taxonomic hierarchy and revision, describe a novel species, genome construction, prediction of microbial functions, risk assessment, identify the transmission dynamics, and conduct the routine surveillance of foodborne pathogens. Growing implementation of these culture-independent diagnostics tools by several public health, clinical, governmental, and industrial entities to replace the slow and labor-intensive culture-dependent methods underscores the impact of sequencing-based methods. This review focuses on the emerging computational genomics and bioinformatics approaches for rapid detection and characterization of foodborne pathogens to improve the resolution of molecular diagnostics. Highlights: Next-generation sequencing tools facilitate rapid detection of foodborne pathogens. Sequencing-based methods yield results faster than culture-based methods. Long-read sequencing enables rapid strain-level identification. Deep metagenomic sequencing reduces the need for pure culture in functional analysis. Machine-learning models improve functional genomics and risk assessment.