Estrategia bioinformática para el análisis taxonómico y funcional de la microbiota subgingival de pacientes colombianos con periodontitis
| dc.contributor.advisor | Barreto Hernández , Emiliano | |
| dc.contributor.author | Neuta Poveda, Yineth | |
| dc.contributor.orcid | 0000-0001-9350-0941 | |
| dc.coverage.country | Colombia | |
| dc.date.accessioned | 2025-09-02T16:31:29Z | |
| dc.date.available | 2025-09-02T16:31:29Z | |
| dc.date.issued | 2025 | |
| dc.description | ilustraciones, diagramas | spa |
| dc.description.abstract | El análisis de datos a partir de genes marcadores se ha centrado en estrategias bioinformáticas que agrupan diversas herramientas desarrolladas en los últimos años, incluyendo múltiples pasos. Esas estrategias se han utilizado para analizar la microbiota de la cavidad oral a partir de datos genómicos basados en marcadores como el gen 16S rRNA, sin embargo, existen pocos estudios que evalúen la microbiota oral en pacientes colombianos con periodontitis, de acuerdo con la última clasificación de la enfermedad, por lo que el objetivo del presente trabajo fue implementar la estrategia bioinformática más adecuada para el análisis taxonómico y funcional de datos metagenómicos del gen 16s rRNA, obtenidos a partir de muestras de placa subgingival de pacientes colombianos con periodontitis. Se determino realizar el análisis con QIIME2, que se caracteriza por ser de código abierto y en el cual se pueden realizar análisis a partir de datos de secuencias de amplicones incluyendo en su análisis demultiplexación y filtrado de calidad, asignación taxonómica por ASV, y reconstrucción filogenética, utilizando en complemento DADA 2. Adicionalmente se realizó predicción de la función con PICRUST2, a partir de los ASV identificados. Se concluyo que QIIME 2 es un framework que permite realizar análisis múltiples, lo que confirma su uso para análisis de comunidades microbianas, así mismo en la clasificación, el uso de base de datos especializadas como HOMD para evaluar la microbiota oral, permite focalizar los resultados al ambiente determinado, logrando una clasificación hasta el nivel de especie, evidenciaron microorganismos específicos más frecuentes asociados con periodontitis tales como Filifactor alocis, Fretibacterium, Eubacterium nodatum group, Eubacterium saphenum, Eubacterium brachy, Dialister invisus, Porphyromonas gingivalis, Desulfubulbus, Selenomonas, Sneathia, Treponema, Tannerella forythia y Parvimonas. El análisis con PICRUST2 permitió observar vías metabólicas que se asociaron principalmente con los grupos de periodontitis, lo que podría sugerir una alta actividad metabólica de los microorganismos asociados con esta condición clínica. (Texto tomado de la fuente) | spa |
| dc.description.abstract | Data analysis from marker genes has focused on bioinformatics strategies that group various tools developed in recent years, including multiple steps. Different strategies have been used to analyze the oral cavity microbiota from genomic data based on markers such as the 16S rRNA gene; however, few studies evaluate the oral microbiota in Colombian patients with periodontitis, according to the latest classification of the disease. Therefore, the objective of the present work was to implement the most appropriate bioinformatics strategy for the taxonomic and functional analysis of metagenomic data of the 16s rRNA gene, obtained from subgingival plaque samples of Colombian patients with periodontitis. It was decided to perform the analysis with QIIME2, which is characterized by being open source and in which analyses can be performed from amplicon sequence data, including demultiplexing and quality filtering, taxonomic assignment by ASV, and phylogenetic reconstruction, using the DADA 2 plugin. Additionally, function prediction was performed with PICRUST2, based on the identified ASVs. It was concluded that QIIME 2 is a framework that allows multiple analyses, which confirms its use for microbial community analysis, also in the classification, the use of specialized databases such as HOMD to evaluate the oral microbiota, allows focusing the results to the specific environment, achieving a classification up to the species level, they showed more frequent specific microorganisms associated with periodontitis such as Filifactor alocis, Fretibacterium, Eubacterium nodatum group, Eubacterium saphenum, Eubacterium brachy, Dialister invisus, Porphyromonas gingivalis, Desulfubulbus, Selenomonas, Sneathia, Treponema, Tannerella forythia and Parvimonas. The analysis with PICRUST2 allowed us to observe metabolic pathways that were associated with periodontitis groups, which could suggest a high metabolic activity of the microorganisms associated with this clinical condition | eng |
| dc.description.degreelevel | Maestría | |
| dc.description.degreename | Magister en Bioinformática | |
| dc.description.methods | Estudio descriptivo en el cual se seleccionan herramientas bioinformáticas y se utilizan para el análisis de datos metagenómicos a partir del gen 16 S rRNA. Se realizó una búsqueda bibliográfica de los pasos y las herramientas más utilizadas en los 5 últimos años para realizar el análisis taxonómico y funcional, a partir de secuencias del gen 16S rRNA obtenidos utilizando la tecnología NGS Ilumina. Como restricción se buscaron herramientas de acceso libre para su uso, y actualizadas, así como estar disponibles para ejecutarlas en entorno LINUX.Se instaló y configuró QIIME 2 y PICRUST2 en un entorno LINUX, en un servidor OpenSuse leap 42.2. El equipo debía cumplir con las características mínimas para su ejecución, tales como espacio en disco de al menos 25 GB, y memoria RAM de 16 GB. Para realizar el análisis, se organizaron los datos de las secuencias de ARNr 16S, los cuales fueron generados posterior a la secuenciación y estaban en formato FASTQ (. fq). Adicionalmente se organizó el archivo de metadatos en el cual se incluye información con datos básicos de las secuencias a analizar. Para el paso de procesamiento se realizó filtrado de la calidad, eliminando lecturas con calidad baja, de acuerdo con el umbral establecido y eliminación de secuencias correspondientes a primers o adaptadores. Se realizó identificación y exclusión de quimeras. Posteriormente se desmultiplexaron las secuencias, para eliminar los cebadores y marcadores añadidos a las secuencias. Las secuencias fueron agrupadas en ASV utilizando el complemento DADA 2, generando una tabla ASV para los análisis posteriores. A partir de la tabla ASV generada, las secuencias representativas fueron clasificadas, utilizando clasificadores de taxonomía con las bases de datos HOMD y SILVA. Se realizó normalización de los datos y análisis de la curva de rarefacción alfa para explorar la integridad y diversidad de cada muestra para obtener resultados imparciales en los análisis posteriores de diversidad. Se evaluó la diversidad alfa y la diversidad beta utilizando diferentes índices. Para alfa diversidad se utilizaron los estimadores de uniformidad, diversidad de Shannon, y distancia de Faith, mientras que la beta diversidad fue basada en los índices de Unifrac ponderado y no ponderado, distancia de Jaccard y disimilitud de Bray- Curtis. Se utilizaron los archivos ASV (seq.fna) generados por QIIME 2 como entrada en PICRUSt 2 para predecir las funciones biológicas de las comunidades, basadas en ortólogos (KO), de la Enciclopedia de Kioto de Genes y Genomas (KEGG), números de clasificación de enzimas, Grupos de genes ortólogos (COG), familias de proteínas (Pfam), y la base de datos de familias de proteínas del Instituto de Investigación Genómica. PICRUSt fue instalado en un entorno Bioconda. Se realizó comparación de alfa diversidad utilizando Kruskal-Wallis para establecer diferencias, y se evaluó mediante un análisis de varianza multivariado permutacional (PERMANOVA) la diferencia entre grupos. Se tuvo en cuenta un p < 0,05, para considerar las diferencias significativas, y cuando p < 0,01 y p < 0,001, las diferencias se consideraron altamente significativas. Para comparar los perfiles taxonómicos en cada grupo evaluado se utilizó la prueba t de Welch con el software STAMP 2.1.3. Se realizo la implementación de las herramientas bioinformáticas seleccionadas en un servidor Dell Power Edge M640 con 96Gb de RAM, doble procesador Xeon con sistema operativo Linus OpenSuse 15.6. Utilizando Miniconda se realizó la instalación de la distribución de amplicones QIIME2 y de PICRUST2. | |
| dc.description.researcharea | Bioinformática | |
| dc.format.extent | 69 páginas | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/88541 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Nacional de Colombia | |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | |
| dc.publisher.faculty | Facultad de Ingeniería | |
| dc.publisher.place | Bogotá, Colombia | |
| dc.publisher.program | Bogotá - Ingeniería - Maestría en Bioinformática | |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.license | Atribución-NoComercial-CompartirIgual 4.0 Internacional | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject.lemb | Análisis de sistemas | spa |
| dc.subject.lemb | System analysis | eng |
| dc.subject.lemb | Enfermedades periodontales | spa |
| dc.subject.lemb | Periodontal disease | eng |
| dc.subject.lemb | Bioinformática | spa |
| dc.subject.lemb | Bioinformatics | eng |
| dc.subject.proposal | Bioinformática | spa |
| dc.subject.proposal | Secuenciación | spa |
| dc.subject.proposal | Análisis del microbioma | spa |
| dc.subject.proposal | Periodontitis | spa |
| dc.subject.proposal | Bioinformatics | eng |
| dc.subject.proposal | Sequencing | eng |
| dc.subject.proposal | Microbiome analysis | eng |
| dc.subject.proposal | Periodontitis | eng |
| dc.title | Estrategia bioinformática para el análisis taxonómico y funcional de la microbiota subgingival de pacientes colombianos con periodontitis | spa |
| dc.title.translated | Bioinformatics strategy for the taxonomic and functional analysis of the subgingival microbiota of Colombian patients with periodontitis | eng |
| dc.type | Trabajo de grado - Maestría | |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | |
| dc.type.content | Text | |
| dc.type.driver | info:eu-repo/semantics/masterThesis | |
| dc.type.redcol | http://purl.org/redcol/resource_type/TM | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dcterms.audience.professionaldevelopment | Estudiantes | |
| dcterms.audience.professionaldevelopment | Investigadores | |
| dcterms.audience.professionaldevelopment | Maestros | |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |