Chimeric RNA molecules, or fusion transcripts, are transcripts where exons from two or more different genes are fused into one transcript. These have the potential to encode novel proteins, and might play roles in the development of cancer. Advantages in high throughput RNA sequencing have made it easier to detect fusion transcripts, but the software used to detect fusions from sequencing data often yield a high false discovery rate. To evaluate which of the chimeric RNA molecules observed in cancer cells are relevant for the development of the disease, good prioritization of the results is important. This will be helped by a visualization framework that automatically integrates RNA data with known genomic features. In this thesis, we have developed an R package that automates the creation of chimeric RNA visualizations. The package, named chimeraviz, implements a unified format for representing fusion transcripts, and can take input from nine different fusion-finder tools. The package provides sorting and filtering functions, as well as multiple visualizations of chimeric RNA molecules that improves upon what has been seen in the literature previously.