The human microbiome represents the community of archaea, bacteria, micro-eukaryotes, and viruses present in and on the human body. Metagenomics is the most recent and advanced tool that allows the study of the microbiome at high resolution by sequencing the whole genetic content of a biological sample. The computational side of the metagenomic pipeline is recognized as the most challenging one as it needs to process large amounts of data coming from next-generation sequencing technologies to obtain accurate profiles of the microbiomes. Among all the analyses that can be performed, phylogenetics allows researchers to study microbial evolution, resolve strain-level relationships between microbes, and also taxonomically place and characterize novel and unknown microbial genomes. This thesis presents a novel computational phylogenetic approach implemented during my doctoral studies. The aims of the work range from the high-quality visualization of large phylogenies to the reconstruction of phylogenetic trees at unprecedented scale and resolution. Large-scale and accurate phylogeny reconstruction is crucial in tracking species at strain-level resolution across samples and phylogenetically characterizing unknown microbes by placing their genomes reconstructed via metagenomic assembly into a large reference phylogeny. The proposed computational phylogenetic framework has been used in several different metagenomic analyses, improving our understanding of the complexity of microbial communities. It proved, for example, to be crucial in the detection of vertical transmission events from mothers to infants and for the placement of thousands of unknown metagenome-reconstructed genomes leading to the definition of many new candidate species. This poses the basis for large-scale and more accurate analysis of the microbiome.

A phylogenetic framework for large-scale analysis of microbial communities / Asnicar, Francesco. - (2019), pp. 1-175.

A phylogenetic framework for large-scale analysis of microbial communities

Asnicar, Francesco
2019-01-01

Abstract

The human microbiome represents the community of archaea, bacteria, micro-eukaryotes, and viruses present in and on the human body. Metagenomics is the most recent and advanced tool that allows the study of the microbiome at high resolution by sequencing the whole genetic content of a biological sample. The computational side of the metagenomic pipeline is recognized as the most challenging one as it needs to process large amounts of data coming from next-generation sequencing technologies to obtain accurate profiles of the microbiomes. Among all the analyses that can be performed, phylogenetics allows researchers to study microbial evolution, resolve strain-level relationships between microbes, and also taxonomically place and characterize novel and unknown microbial genomes. This thesis presents a novel computational phylogenetic approach implemented during my doctoral studies. The aims of the work range from the high-quality visualization of large phylogenies to the reconstruction of phylogenetic trees at unprecedented scale and resolution. Large-scale and accurate phylogeny reconstruction is crucial in tracking species at strain-level resolution across samples and phylogenetically characterizing unknown microbes by placing their genomes reconstructed via metagenomic assembly into a large reference phylogeny. The proposed computational phylogenetic framework has been used in several different metagenomic analyses, improving our understanding of the complexity of microbial communities. It proved, for example, to be crucial in the detection of vertical transmission events from mothers to infants and for the placement of thousands of unknown metagenome-reconstructed genomes leading to the definition of many new candidate species. This poses the basis for large-scale and more accurate analysis of the microbiome.
2019
XXX
2019-2020
Ingegneria e scienza dell'Informaz (29/10/12-)
Information and Communication Technology
Blanzieri, Enrico
Segata, Nicola
no
Inglese
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore BIO/19 - Microbiologia Generale
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/368807
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