融合基因是众所周知的癌症驱动因子。然而，大多数已知的致癌融合基因是蛋白编码的，相对较少涉及非编码序列，这是由于缺乏合适的检测工具。我们开发了SFyNCS用于从转录组测序数据中检测蛋白编码基因和非编码序列的融合。本研究的主要优势在于使用从基因组数据中检测到的体细胞结构变异来验证从转录组数据中检测到的融合。这使得我们能够全面评估各种融合检测和过滤策略以及参数。我们通过在癌细胞系和患者样本中进行广泛的基准测试，展示了SFyNCS在敏感性和特异性方面比现有算法更具优势。然后，我们将SFyNCS应用于33种肿瘤类型的9565个肿瘤样本的癌基因组图谱队列中，共检测到165,139个融合。其中，72%的融合涉及非编码序列。我们在3%的前列腺癌中发现了一种长链非编码RNA与各种癌基因频繁融合。此外，在32%的分化不良脂肪肉瘤中，我们发现了涉及两个非编码RNA的融合，并在小鼠模型验证了其致癌功能。© 2023 作者。由牛津大学出版社代表核酸研究出版。

Fusion genes are well-known cancer drivers. However, most known oncogenic fusions are protein-coding, and very few involve non-coding sequences due to lack of suitable detection tools. We develop SFyNCS to detect fusions of both protein-coding genes and non-coding sequences from transcriptomic sequencing data. The main advantage of this study is that we use somatic structural variations detected from genomic data to validate fusions detected from transcriptomic data. This allows us to comprehensively evaluate various fusion detection and filtering strategies and parameters. We show that SFyNCS has superior sensitivity and specificity over existing algorithms through extensive benchmarking in cancer cell lines and patient samples. We then apply SFyNCS to 9565 tumor samples across 33 tumor types in The Cancer Genome Atlas cohort and detect a total of 165,139 fusions. Among them, 72% of the fusions involve non-coding sequences. We find a long non-coding RNA to recurrently fuse with various oncogenes in 3% of prostate cancers. In addition, we discover fusions involving two non-coding RNAs in 32% of dedifferentiated liposarcomas and experimentally validated the oncogenic functions in mouse model.© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.