Structural variation in the genome is known to cause significant alteration to the transcriptome. Novel transcripts have been associated with oncogenesis and the progression of many cancers. They can include fusion genes, novel splice variants and transcribed structural variants, and are particularly common in cancers with genomic instability. High-grade serous ovarian carcinoma (HGSC) is characterised by a low frequency of oncogenic mutations, limited recurrent copy-number alterations and broad genomic instability, resulting from ubiquitous initiating TP53 mutations. While some patterns in the genomic structural variation of HGSC have been identified (specifically copy number signatures have been of great significance) little is known about their transcriptomic consequences and linking these changes to patterns in gene expression remains challenging. This project aims to characterise novel transcripts in HGSC. Shallow whole genome sequencing and RNA sequencing data collected on 11 HGSC patient-derived organoids (PDOs) and 2 fallopian tube PDO controls, will be evaluated. The computational pipeline MINTIE will be used to identify aberrant transcripts in the RNA sequencing data. MINTIE uses de novo transcript assembly and compares each HGSC sample to the control samples by differential expression analysis to detect novel variants. Canonical and non-canonical fusion genes, novel splice variants and transcribed structural variants such as insertions, deletions, inversions, and tandem duplications are all detected by MINTIE. The frequency and distribution of the variants identified by MINTIE will be investigated to identify patterns of transcriptomic change. The biological significance of variants in HGSC will also be examined alongside potential recurrent changes. Genomic copy number alterations and structural variations will be explored alongside disruptions to the transcript sequence and patterns in gene expression. Through this analysis, an enhanced understanding of the transcriptomic consequences of genomic instability will be developed to elucidate potential biological mechanisms driving HGSC development and progression.