Advancing precision medicine: assessing genetic diversity impact on regulatory elements and genome editing outcomes

Over the past decade, the landscape of precision medicine has experienced a notable transformation, driven by the intersection of omics sciences and computational methodologies. This convergence has empowered researchers to explore individual-specific biological markers more deeply, offering the potential for tailored treatments. Central to this shift are genetic variants, which can profoundly influence the genomic environment by modifying regulatory elements. However, fully leveraging the potential of genetic variants in precision medicine necessitates accurate computational tools capable of deciphering their impact on cellular mechanisms. In response to this need, this PhD thesis introduces two innovative computational methods: GRAFIMO and MotifRaptor. GRAFIMO utilizes genome graph data structures to identify potential transcription factor binding sites while considering individual- and population-specific genetic variants. By accounting for various genetic events, from single nucleotide variants to complex structural variants, GRAFIMO provides a comprehensive analysis of transcription factor binding. Similarly, MotifRaptor adopts a Transcription Factor-centric approach to annotate the potential functional impact of non-coding variants, enhancing our understanding of regulatory mechanisms underlying complex traits and diseases. CRISPR genome editing holds promise for programmable editing of genomic sequences in therapeutic settings. However, its application hinges on two critical factors: accurately quantifying editing outcomes, and predicting and mitigating off-target effects. Addressing the former challenge, we benchmarked different methods used to assess genome editing outcomes across various cellular contexts, providing valuable insights into their efficacy and reliability. CRISPRme, on the other hand, represents a pioneering tool for variant- and haplotype-aware CRISPR off-target nomination. By considering genetic diversity across populations, CRISPRme facilitates thorough off-target analysis, potentially enabling safer and more precise genome editing interventions. These computational methods offer significant insights into exploiting genetic diversity for potential individual-oriented therapies, potentially contributing to the development of precision medicine-oriented approaches and therapies.