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Longitudinal metagenomics

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Longitudinal metagenomics

dc.contributor.advisor	Peña Garay, Carlos
dc.contributor.author	Martí Martínez, Jose Manuel
dc.contributor.other	Facultat de Ciències Biològiques	es_ES
dc.date.accessioned	2019-04-04T10:48:15Z
dc.date.available	2020-04-04T04:45:05Z
dc.date.issued	2018	es_ES
dc.date.submitted	29-03-2019	es_ES
dc.identifier.uri	http://hdl.handle.net/10550/69798
dc.description.abstract	Culture-independent approaches are revolutionizing biology. Whether in a clinical or in an environmental sample, metagenomics can reveal which microorganisms exist and what they actually do. Metagenomic studies have unveiled more microbial diversity in a few years than traditional microbiology in centuries. From the top branches down to the roots, its discoveries are reshaping the tree of life dramatically. Metagenomics is a powerful tool for the study of microbial communities, but it requires equally powerful methods of analysis. Current challenges in the analysis of metagenomic data include the accurate comparison of samples, the estimation of the uncertainty in the results, and the effective removal of contamination. The scarcer the microbes are in an environment, the more essential it is to have solutions for these issues. Examples of sites with few microbes come not only from oligotrophic habitats but also from many body tissues and fluids. This Ph.D. dissertation implements a novel approach to the dynamics of microbial communities studied by metagenomics. This has been achieved through the development of new techniques in the field of computational microbiology, with the support of statistical, mathematical, and parallel computational methods. That strategy was used to analyze clinical and environmental longitudinal metagenomic datasets. By removing multiple types of contamination and providing confidence levels for the results, I was finally able to unveil the dynamics of microbial communities in an efficient and robust manner. To achieve universal solutions for the challenges and range of applications of clinical, environmental, food, and even forensic studies, I have developed a series of methods that are field-, platform- and implementation-agnostic. Then, I used these novel tools under diverse circumstances to validate this broad-spectrum strategy for longitudinal metagenomics: 1. with public and own datasets of both clinical and environmental samples, 2. with Illumina dye and Oxford Nanopore Technologies sequencing, 3. with standard and customized whole-genome sequences databases, 4. with results generated with three different taxonomic classification engines, 5. and last but not least, in time and space series. Many doctoral dissertations in scientific or technical disciplines focus on a single specific problem. This is not the case with this piece of work. Issues are sometimes clustered together like a system, with mutual dependencies reducing the degrees of freedom to the point that progress in any of them require collective improvements. The general organization of the dissertation is: Part I is an introductory block. Chapter 1, Introduction, contains a broad opening for this work, which relates metagenomics to fundamental scientific topics. Since metagenomics is revolutionizing the tree of life, this chapter covers this latter subject. However, the tree of life is closely linked to the problem of the origin and evolution of life on Earth, and this is the real keystone of the chapter, which includes a perspective of this problem from astronomy, astrophysics, and cosmology points of view. Chapter 2, Objectives, details the context, general and detailed objectives of this work. Part II is the block covering the methods used in this dissertation, mostly custom-developed computational microbiology tools. Chapter 3, Metagenomics, introduces the particularities of longitudinal metagenomic studies. Next, we review the specific laboratory and computational methods that we have used in this thesis, ranging from the sampling to the taxonomic classification of sequences from shotgun metagenomic sequencing (SMS). Chapter 4, Recentrifuge, presents a new method of comparative analysis and contamination removal for metagenomics. With Recentrifuge, researchers can interactively explore which organisms are in their samples and at which level of confidence, enabling robust comparative analysis of multiple samples in any metagenomic study. Recentrifuge's novel approach combines robust statistics, arithmetic of scored taxonomic trees, and parallel computational algorithms. Chapter 5, cmplxCruncher, deals with a new software engineered to support the analysis through longitudinal metagenomics of the dynamics of ranking processes in biological complex systems. This computational biology code enables dynamic analysis of longitudinal metagenomic samples of diverse origin, clinical and environmental, and constituted in temporal, spatial, or hosting-environment series. Chapter 6, RATSVM, presents a computational biology protocol for automatic and robust analysis of time series in virome metagenomics. Real data from a published virome study is used to illustrate the procedure conveniently. Part III represents the block covering research applications. Chapter 7, Using populations of human and microbial genomes for organism detection in SMS, copes with the building and application to different massive metagenomic projects of the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search. Chapter 8, Health and disease imprinted in the time variability of the human microbiome, analyzes the microbial composition of the gut microbiome in several subjects under different circumstances and time spans. Temporal fluctuations in the microbial composition of the human gut show significant differences due to host-related conditions. We prove that stable microbiotas can be distinguished from unstable ones. Chapter 9, Application of Recentrifuge to an SMS study of plasma in individuals with ME/CFS, used a ``worst-case scenario'' to challenge Recentrifuge with data from a metagenomic investigation of RNA in plasma from individuals with severe disorders. This study suffered from critical contamination to the point of preventing any positive conclusion. Despite the difficulties, Recentrifuge provided results that yielded new biological insight, supporting the growing evidence of blood translocated microbiota. Chapter 10, The Gollum Project, deals with the identification and characterization of the ultra-oligotrophic endolithic microbial communities living in a range of different host rocks throughout the length of the Somport tunnel, from the surface to the maximum depth. The ultra-low microbial biomass of the samples of the Gollum endolithic microbial communities posed a challenge requiring state-of-the-art and innovative DNA extraction, sequencing, and computational biology methods. Chapter 11, Environmental applications, summarizes other environmental SMS studies where the developed methods had a central role: microbial communities in polar solar panels, a work suggesting that Verticillium wilt of Olea europaea could actually be a polymicrobial attack, biodiversity in lagoons of San Cristóbal (Galápagos Islands), and an exploratory study of real-time low-cost nanopore SMS for biosecurity. Part IV represents the block of conclusions. Chapter 12, General conclusions, reviews the main conclusions, principal contributions of this thesis by research project, by chapter, and by computational biology code. Chapter 13, Resumen en Español, summarizes the text in Spanish. Part V is the block of appendixes.	es_ES
dc.format.extent	628 p.	es_ES
dc.language.iso	en_US	es_ES
dc.subject	metagenomics	es_ES
dc.subject	time series	es_ES
dc.subject	space series	es_ES
dc.subject	robust comparative analysis	es_ES
dc.subject	contamination removal	es_ES
dc.subject	dynamics	es_ES
dc.subject	longitudinal series	es_ES
dc.title	Longitudinal metagenomics	es_ES
dc.type	doctoral thesis	es_ES
dc.subject.unesco	UNESCO::MICROBIOLOGIA	es_ES
dc.subject.unesco	UNESCO::COMPUTACION DIGITAL	es_ES
dc.subject.unesco	UNESCO::BIOESTADISTICA	es_ES
dc.subject.unesco	UNESCO::MICROBIOLOGIA CLINICA	es_ES
dc.embargo.terms	1 year	es_ES