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Analysis of chromosome conformation data and application to cancer

Abstract : The chromatin is not randomly arranged into the nucleus. Instead, the nuclear organization is tightly controlled following different organization levels. Recent studies have explored how the genome is organized to ensure proper gene regulation within a constrained nuclear space. However, the impact of the epigenome, and in particular the three-dimensional topology of chromatin and its implication in cancer progression remain largely unexplored. As an example, recent studies have started to demonstrate that defects in the folding of the genome can be associated with oncogenes activation. Although the exact mechanisms are not yet fully understood, it demonstrates that the chromatin organization is an important factor of tumorigenesis, and that a systematic exploration of the three-dimensional cancer genomes could improve our knowledge of cancer biology in a near future. High-throughput chromosome conformation capture methods are now widely used to map chromatin interaction within regions of interest or across the genome. The Hi-C technique empowered by next generation sequencing was designed to explore intra and inter-chromosomal contacts at the whole genome scale and therefore offers detailed insights into the spatial arrangement of complete genomes. The aim of this project was to develop computational methods and tools, that can extract relevant information from Hi-C data, and in particular, in a cancer specific context. The presented work is divided in three parts. First, as many sequencing applications, the Hi-C technique generates a huge amount of data. Managing these data requires optimized bioinformatics workflows able to process them in reasonable time and space. To answer this need, we developped HiC-Pro, an optimized and flexible pipeline to process Hi-C data from raw sequencing reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, generates and normalizes intra- and inter-chromosomal contact maps. In addition, HiC-Pro is compatible with all current Hi-C-based protocols.
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  • HAL Id : tel-01933733, version 1

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Nicolas Servant. Analysis of chromosome conformation data and application to cancer. Quantitative Methods [q-bio.QM]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066535⟩. ⟨tel-01933733⟩

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