A bioinformatics pipeline integrating predictive metagenomics profiling for the analysis of 16S rDNA/rRNA sequencing data originated from foods. (December 2018)

• Record Type: Journal Article
• Authors: Mataragas, Marios; Alessandria, Valentina; Ferrocino, Ilario; Rantsiou, Kalliopi; Cocolin, Luca
• Is Part Of: Food microbiology. Volume 76(2018)
• Page Start: 279
• Page End: 286
• Publication Date: 2018-12
• Subjects: Amplicon sequencing -- Food microbiology -- Metabolic network -- Meta-omics -- Microbial risk assessment -- Molecular microbiology -- Predictive functional profiling; Food Microbiology -- Periodicals; Aliments -- Microbiologie -- Périodiques; Food -- Microbiology; Periodicals; Food -- Microbiology -- Periodicals; Food contamination -- Periodicals; 664.001579
• DOI: 10.1016/j.fm.2018.05.009
• Languages: English
• ISSNs: 0740-0020

Abstract

Abstract: The recent advances in molecular biology, such as the advent of next-generation sequencing (NGS) platforms, have paved the way to new exciting tools which rapidly transform food microbiology. Nowadays, NGS methods such as 16S rDNA/rRNA metagenomics or amplicon sequencing are used for the taxonomic profiling of the food microbial communities. Although 16S rDNA/rRNA NGS-based microbial data are not suited for the investigation of the functional potential of the identified operational taxonomic units as compared to shotgun metagenomics, advances in the bioinformatics discipline allow now the performance of such studies. In this paper, a bioinformatics workflow is described integrating predictive metagenomics profiling with specific application to food microbiology data. Bioinformatics tools pertinent to each sub-module of the pipeline are suggested as well. The published 16S rDNA/rRNA amplicon data originated from an Italian Grana-type cheese, using an NGS platform, was employed to demonstrate the predictive metagenomics profiling approach. The pipeline identified the microbial community and the changes that occurred in the microbial profile during manufacture of the food product studied (taxonomic profiling). The workflow also indicated significant changes in the functional profiling of the community. The tool may help to investigate the functional potential, alterations, and interactions of a microbial community. The proposed workflow may also find an application in the investigation of the ecology of foodborne pathogens encountered in various food products. Highlights: A bioinformatics workflow integrating predictive metagenomics profiling is described. Demonstration of the bioinformatics workflow using 16S rDNA/rRNA NGS-based microbial data originated from food. Taxonomic profiling of the microbiome of an Italian Grana-type cheese. Functional potential of the identified food microbial community.