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Title: | Determining the Three-dimensional Structure of Genomes and Genomic Domains Integrating Chromosome Conformation Capture Data and Microscopy Images |
Author: | Castillo Andreo, David |
Director/Tutor: | Martí-Renom, Marc A. |
Keywords: | Microscòpia Genòmica Genoma humà Microscopy Genomics Human genome |
Issue Date: | 1-Mar-2023 |
Publisher: | Universitat de Barcelona |
Abstract: | [eng] Microscopy and Chromosome Conformation Capture (3C) are the two main techniques for studying the three-dimensional (3D) organization of the genome. Microscopy, allowing the visualization of genomic loci in individual nuclei, pioneered the field of structural genomics and became the gold-standard for the validation of new discoveries. 3C and 3C-based techniques, identifying the number of contacts between pairs of genomic loci, have already been key to unveil the importance of the 3D genome organization in many cellular processes. Both techniques are continuously evolving pushing forward the technologies and giving rise to innovative assays that require the support of new computational methods for data collection, analysis and modeling. In this thesis, I have contributed to provide these essential computational methods to the Structural Genomics community. In Microscopy, I participated in the design and implementation of OligoFISSEQ, a novel multiplexing imaging technology to visualize multiple genomic regions in hundreds and thousands of individual cells. In 3C-based techniques, I contributed to the development of a tool for the reconstruction of the 3D organization of chromatin from highly-sparse 3C-based datasets (e.g. Promoter Capture Hi-C). Finally, I have introduced pTADbit, a novel approach for the reconstruction of the 3D Genome organization integrating both Microscopy and 3C data via the application of Machine Learning methods. |
URI: | https://hdl.handle.net/2445/196743 |
Appears in Collections: | Tesis Doctorals - Departament - Genètica, Microbiologia i Estadística |
Files in This Item:
File | Description | Size | Format | |
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DCA_PhD_THESIS.pdf | 63.61 MB | Adobe PDF | View/Open |
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