BIOMETA: Bioinformatics procedures for the advancement of the implementation of clinical metagenomics

Abstract

Clinical metagenomics is an emerging field that permits the identification of microbes and facilitates the diagnosis of pathological conditions, providing a more nuanced understanding of microbiome behaviour. Currently, 16S rRNA sequencing is the most used method to identify bacteria and archaea. In this Thesis, we focus on the development of two bioinformatics procedures and a database with the aim of enhancing our comprehension of the microbiome. The procedures include the evaluation of primers or primer pairs, and the classification of phenotypes through a machine learning ensemble-based pipeline. The database compiles the 16S rRNA gene copy numbers present in complete bacterial and archaeal genomes. Although we studied mainly oral microbiome data, the developed procedures can be used to study any microbiome.