Data from: Integrative discovery and functional dependency mapping uncover essential chimeric RNAs in the active transcriptome

Chimeric RNAs are a diverse class of molecules defined as RNA transcripts containing genetic information for multiple genes that arise from both DNA-level and RNA-level fusion events. While their expression patterns across various tissue types and disease states have been well-documented, the biological function of these transcripts remains poorly understood. In this study, we developed an integrative prediction pipeline to establish a comprehensive atlas of 569,640 chimeric RNAs across 1,019 cancer cell lines, providing the high-resolution landscape required for functional interrogation. Leveraging this resource, we performed a systematic functional dependency screen by integrating our predictions with large-scale RNAi datasets from The Cancer Dependency Map (DepMap) project. This approach moves beyond simple chimeric RNA prediction to identify high-confidence chimeric RNAs essential for cell viability, including a previously uncharacterized class of recurrent chimeric transcripts likely generated by cis-splicing of adjacent genes (cis-SAGe) found across wide varieties of tissue types and disease states. We demonstrate that depletion of selected candidate chimeras disrupts cell cycle progression and cellular fitness and that these transcripts are frequently dysregulated in primary breast tumors, highlighting their potential biological relevance and clinical significance. Together, these results demonstrate that chimeric RNAs constitute a previously underappreciated layer of gene expression, effectively expanding the repertoire of the functional genome beyond conventional genetics. By prioritizing function, this study establishes a framework for investigating biologically relevant chimeric transcripts and highlights their potential as therapeutic targets and biomarkers in cancer.