Computational Prediction of Chimeric RNAs from Long Reads Using CTAT-LR-Fusion.

Chimeric or fusion transcripts play critical roles in disease mechanisms and therapeutic targets, making their accurate identification crucial. The emerging field of long-read chimeric RNA prediction is transforming our understanding of complex transcriptomic structures and their implications in various biological processes, particularly in cancer and genetic diseases. Long-read sequencing technologies offer significant advantages over traditional methods, enabling the identification and characterization of chimeric RNAs, which are often difficult to detect due to their complexity and the limitations of shorter reads. Long-read RNA sequencing has emerged as a powerful tool for detecting full-length fusion transcripts, overcoming limitations of short-read technologies. This chapter provides a comprehensive guide to computational prediction of fusion transcripts using CTAT-LR-fusion, a tool designed for analyzing long-read RNA-seq data. Key features, installation, usage, output interpretation, and challenges associated with its application are discussed, offering a practical framework for researchers exploring the fusion transcript landscape.