Mauriello, Italia Elisa (2023) Identification of species and sub-species in food and human microbiomes. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Resource language: English
Title: Identification of species and sub-species in food and human microbiomes
Creators:
Creators
Email
Mauriello, Italia Elisa
italiaelisa.mauriello@unina.it
Date: 10 March 2023
Number of Pages: 102
Institution: Università degli Studi di Napoli Federico II
Department: Agraria
Dottorato: Food Science
Ciclo di dottorato: 35
Coordinatore del Corso di dottorato:
nome
email
Barone, Amalia
ambarone@unina.it
Tutor:
nome
email
Pasolli, Edoardo
UNSPECIFIED
Date: 10 March 2023
Number of Pages: 102
Keywords: microbiome, metagenomics, large-scale analysis
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 - Telecomunicazioni
Date Deposited: 20 Mar 2023 17:34
Last Modified: 10 Apr 2025 12:45
URI: http://www.fedoa.unina.it/id/eprint/15074

Collection description

The study of food and human microbiomes has assumed a central role in the scientific community, especially in the last few years thanks to the advancement of sequencing technologies and the dramatic decrease of sequencing cost for exploring complex microbial communities. This is generating a large amount of data that needs proper computational tools and methodologies to be analysed. For that purpose, this thesis aims to develop and apply tools and analyses for the identification of species and sub-species in food and human microbiomes. This is accomplished by considering large-scale approaches built on shotgun metagenomics data. It has been developed and validated a methodology based on a clustering approach for sub-species identification from genomes. The main idea is to view clustering as a supervised classification problem, in which we must estimate the true number of clusters (i.e., sub-species in our case). Such methodology has been validated on synthetic data in which we tried to estimate the right number of clusters/sub-species by changing different variables. The methodology has been also applied in real scenarios by considering two microbial families of great relevance in the food science field as lactic acid bacteria (LAB) and Bifidobacteriaceae.

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