Identification and characterization of non-coding genomic variations associated to cancer diseases
The genetic and molecular bases of most of the human diseases have become one of the main goals of the human biology in the last decades. To be able to unveil the genetic variations and the affected cellular processes associated with a specific disease is crucial in order to generate accurate diagno...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/397789 |
| Acceso en línea: | http://hdl.handle.net/10803/397789 |
| Access Level: | acceso abierto |
| Palabra clave: | Genòmica Genómica Genomics Càncer Cáncer Cancer Ciències Experimentals i Matemàtiques 575 |
| Sumario: | The genetic and molecular bases of most of the human diseases have become one of the main goals of the human biology in the last decades. To be able to unveil the genetic variations and the affected cellular processes associated with a specific disease is crucial in order to generate accurate diagnosis and further therapies. The Next Generation Sequencing (NGS) revolution, with the associated reduction in time and costs of sequencing, has allowed the scientist to access large number of human genomes to their biomedical studies. The study of genetic disorders, cancer in particular, has benefit from NGS identifying genetic variations associated with a given disorder. All these new results, some of them in regions with unknown function, have generated a double challenge in the scientific community. Firstly, detect as much as possible all the different variants associated with a disease, in some complex diseases several. Secondly, to understand the functional impact those modifications are causing in the cell. Regarding the first challenge, this thesis contributes in the identification of genetic modifications throw the development of a bioinformatics tool named SMUFIN (Moncunill et al. 2014). SMUFIN can detect somatic variants related with tumour development and progression in a quickly and effective way. Not limited to the software development, several tumours has been analysed and their somatic variants characterized. These tumours include mantel cell lymphoma, paediatric medulloblastoma and chronic lymphocytic leukaemia (Moncunill et al. 2014; Puente et al. 2015). In the evaluation of the functional impact, the thesis also includes a method, RELA, to determine when these annotated variants play a regulatory role as enhancers or promoters (Gonzalez et al. 2012). Combined with other available data and a spread methodology to unveil regulatory regions evaluation of variants affecting regulatory regions have been performed in chronic lymphocytic leukaemia (details included in the thesis discussion). To sum up, this thesis cover with methodology and provide bioinformatics tools to perform a complete genomic analysis of genetic variants in biomedicine studies. It includes from the identification of variants for each of the patients to the evaluation of their functional impact in the disease development and progression. This kind of approach is currently common in the research laboratories and it will be part of the healthcare system in a close future to diagnose and classify patients. |
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