Using AI to Profile Egyptian Lactobacillus for Next Generation Functional Foods

Domaine:

healthcare

Type de record:

paper

Créateur:

SheMohMosMoh

Éditeur:

IGI

Hôte:

In response to the growing demand for sustainable, health-promoting foods, this chapter details an AI-powered pipeline for whole-genome sequencing and functional annotation of six Egyptian Lactobacillus isolates selected from 50 dairy-derived candidates. High-coverage Illumina MiSeq reads were quality-filtered (FastQC), assembled (PATRIC), and annotated via RASTtk, antiSMASH, BAGEL4, ResFinder, and CRISPRCasFinder. Analyses revealed four bacteriocin biosynthesis clusters, two Type I-E CRISPR arrays, prophage elements, and no antibiotic resistance or virulence genes. Phylogenomic analysis confirmed membership in the L. paracasei and L. rhamnosus groups. This in silico approach accelerates safety assessment and guides the selection of bacteriocin-rich cultures for next-generation functional foods, with alignment with Egyptian regulatory standards and recommendations for digital-twin modeling and pilot-scale validation.

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