Harnessing The CRISPR Data Revolution to Uncover The Secrets of Double-Strand DNA Repair
C.F. Seale (TU Delft - Pattern Recognition and Bioinformatics)
Marcel JT Reinders – Promotor (TU Delft - Pattern Recognition and Bioinformatics)
Joana Gonçalves – Copromotor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology has transformed molecular biology by enabling a strategy for precise, efficient, and relatively simple genome editing. Guided by a small strand of RNA, CRISPR locates specific DNA sequences within the genome and introduces double-strand breaks (DSBs). A typical cell can detect and fix the damage by invoking one of several DNA repair pathways. However, repair is not error-free and often introduces mutations. The mutagenic nature of repair pathways can be leveraged to disrupt genes or regulatory elements with high specificity, providing a powerful tool for gaining insights into gene function. Researchers can also generate datasets of mutations left behind after DSB induction and repair within different genomic contexts to learn more about the mutagenic effects of DNA repair. In this thesis, we explore challenges and novel approaches for analysing large-scale datasets of mutations and gene essentiality generated via CRISPR technology.