Computational approaches for integrative cancer genomics
Given the complexity and heterogeneity of cancer, the development of new high-throughput wide-genome technologies has open new possibilities for its study. Several projects around the globe are exploiting these technologies for generating unprecedented amount of data for cancer genomes. Its analysis...
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| Tipo de documento: | tese |
| Estado: | Versão publicada |
| Data de publicação: | 2015 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositório: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/328729 |
| Acesso em linha: | http://hdl.handle.net/10803/328729 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Cancer Genomics Mutations Drivers High-throughput Genòmica Mutacions 616 |
| Resumo: | Given the complexity and heterogeneity of cancer, the development of new high-throughput wide-genome technologies has open new possibilities for its study. Several projects around the globe are exploiting these technologies for generating unprecedented amount of data for cancer genomes. Its analysis, integration and exploration are still a key challenge in the field. In this dissertation, we first present Gitools, a tool for accessing databases in biology, analysing high-throughput data, and visualising multi-dimensional results with interactive heatmaps. Then, we show IntOGen, the methodology employed for collection and organization of the data, the methods used for its analysis, and how the results and analysis were made available to other researchers. Finally, we compare several methods for impact prediction of non-synonymous mutations, showing that new tools specifically designed for cancer outperform those traditionally used for general diseases, and also the need for using other sources of information for better prediction of cancer mutations. |
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