Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals

The work is based on metagenomic sequencing data of 16S rRNA from fecal samples of patients with ulcerative colitis (UC) and healthy controls (HC) from different previously published studies. The different studies have been divided into two cohorts: a discovery cohort (CD) and a validation cohort (C...

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Detalhes bibliográficos
Autor: Castany Roma, Miquel
Tipo de documento: dissertação
Data de publicação:2023
País:España
Recursos:Universitat Oberta de Catalunya (UOC)
Repositório:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/148589
Acesso em linha:http://hdl.handle.net/10609/148589
Access Level:Acceso aberto
Palavra-chave:16S rRNA
metagenomics
gut microbiome
metagenòmica
microbioma intestinal
Ulcerative colitis -- TFM
Colitis ulcerosa -- TFM
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spelling Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecalsAnalysis of the intestinal dysbiosis pattern in ulcerative colitis from fecal samplesCastany Roma, Miquel16S rRNAmetagenomicsgut microbiome16S rRNAmetagenòmicamicrobioma intestinalUlcerative colitis -- TFMColitis ulcerosa -- TFMThe work is based on metagenomic sequencing data of 16S rRNA from fecal samples of patients with ulcerative colitis (UC) and healthy controls (HC) from different previously published studies. The different studies have been divided into two cohorts: a discovery cohort (CD) and a validation cohort (CV). The relevant taxonomic classifications of each sample have been performed using specialized software for these tasks. In our case, we have used the software known as Mothur (Schloss et al., 2009) and a taxonomic classification database for 16S rRNA sequences such as SILVA (Quast et al., 2013). Once the taxonomic classification data has been obtained, statistical analysis has been carried out using Rstudio to determine and extract a common dysbiosis pattern to the samples of patients composing the discovery cohort. Subsequently, these results have been attempted to be extrapolated to the validation cohort, which consisted of UC patient samples from another study. Afterwards, a task dedicated to the prediction of clinical outcomes has been carried out using the relative abundances of different taxa as differentiating features. Machine learning techniques (ML) (Serrano-Gómez et al., 2021) have been used for this purpose, but other approaches based on generalized linear models (GLMs) or multiple regressions have also been employed (Rivera-Pinto et al., 2018).El treball parteix de dades de seqüenciació metagenòmica de 16S rRNA de mostres fecals de pacients amb colitis ulcerosa (UC) i pacients sans (HC) de diferents estudis prèviament publicats. Els diferents estudis s'han dividit en dos cohorts: un de discovery (CD) i un altre de validation (CV). S'han realitzat les pertinents classificacions taxonòmiques de cada una de les mostres mitjançant programari especialitzat per a aquestes tasques. En el nostre cas hem utilitzat el software conegut com a Mothur (Schloss et al., 2009) i una base de dades de classificació taxonòmica de seqüències de 16S rRNA com és SILVA (Quast et al., 2013). Un cop s'han obtingut les dades de classificació taxonòmica, s'ha procedit a l'anàlisi estadístic mitjançant Rstudio per tal de determinar i extreure'n un patró de disbiòsi comú a les mostres de pacients que componen la cohort discovery. Seguidament, aquests resultats s'han intentat extrapolar a la cohort validation, que ha estat compost de mostres de pacients amb UC però provinents d’un altre estudi. Posteriorment, s'ha realitzat una tasca dedicada a la predicció de resultats clínics utilitzant les abundàncies relatives dels diferents taxons com a característica diferencial. S'han fet servir tècniques de Machine Learning (ML) (Serrano-Gómez et al., 2021) però també hem emprat altres aproximacions basades en models lineals generalitzats (GLMs) o regressions múltiples (Rivera-Pinto et al., 2018).Universitat Oberta de Catalunya (UOC)Guillén, Yolanda202320232023info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/148589reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)CatalánCC BY-NC-NDhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1485892026-05-28T12:42:01Z
dc.title.none.fl_str_mv Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
Analysis of the intestinal dysbiosis pattern in ulcerative colitis from fecal samples
title Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
spellingShingle Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
Castany Roma, Miquel
16S rRNA
metagenomics
gut microbiome
16S rRNA
metagenòmica
microbioma intestinal
Ulcerative colitis -- TFM
Colitis ulcerosa -- TFM
title_short Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
title_full Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
title_fullStr Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
title_full_unstemmed Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
title_sort Anàlisi del patró de disbiòsi intestinal en colitis ulcerosa a partir de mostres fecals
dc.creator.none.fl_str_mv Castany Roma, Miquel
author Castany Roma, Miquel
author_facet Castany Roma, Miquel
author_role author
dc.contributor.none.fl_str_mv Guillén, Yolanda
dc.subject.none.fl_str_mv 16S rRNA
metagenomics
gut microbiome
16S rRNA
metagenòmica
microbioma intestinal
Ulcerative colitis -- TFM
Colitis ulcerosa -- TFM
topic 16S rRNA
metagenomics
gut microbiome
16S rRNA
metagenòmica
microbioma intestinal
Ulcerative colitis -- TFM
Colitis ulcerosa -- TFM
description The work is based on metagenomic sequencing data of 16S rRNA from fecal samples of patients with ulcerative colitis (UC) and healthy controls (HC) from different previously published studies. The different studies have been divided into two cohorts: a discovery cohort (CD) and a validation cohort (CV). The relevant taxonomic classifications of each sample have been performed using specialized software for these tasks. In our case, we have used the software known as Mothur (Schloss et al., 2009) and a taxonomic classification database for 16S rRNA sequences such as SILVA (Quast et al., 2013). Once the taxonomic classification data has been obtained, statistical analysis has been carried out using Rstudio to determine and extract a common dysbiosis pattern to the samples of patients composing the discovery cohort. Subsequently, these results have been attempted to be extrapolated to the validation cohort, which consisted of UC patient samples from another study. Afterwards, a task dedicated to the prediction of clinical outcomes has been carried out using the relative abundances of different taxa as differentiating features. Machine learning techniques (ML) (Serrano-Gómez et al., 2021) have been used for this purpose, but other approaches based on generalized linear models (GLMs) or multiple regressions have also been employed (Rivera-Pinto et al., 2018).
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/148589
url http://hdl.handle.net/10609/148589
dc.language.none.fl_str_mv Catalán
language_invalid_str_mv Catalán
dc.rights.none.fl_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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