Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China.
Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
Cell Rep Methods. 2024 Mar 25;4(3):100733. doi: 10.1016/j.crmeth.2024.100733. Epub 2024 Mar 18.
Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.
在这里，我们提出了 Anchored-fusion，这是一种高度敏感的融合基因检测工具。它锚定感兴趣的基因，这些基因通常涉及驱动融合事件，并恢复通常被传统算法过滤掉的短读序列的非独特匹配。此外，Anchored-fusion 包含一个基于深度学习层次结构的模块，该模块结合了自我蒸馏学习（层次视图学习和蒸馏 [HVLD]），有效地过滤掉了在测序过程中产生的假阳性嵌合片段，同时保持了真正的融合基因。Anchored-fusion 能够高度敏感地检测融合基因，因此可以应用于测序深度较低的情况。我们在各种条件下对 Anchored-fusion 进行了基准测试，发现它在模拟数据、批量 RNA 测序 (bRNA-seq) 数据和单细胞 RNA 测序 (scRNA-seq) 数据中检测融合事件的性能优于其他工具。我们的结果表明，Anchored-fusion 可以成为临床相关 RNA-seq 数据中融合检测任务的有用工具，并可应用于研究 scRNA-seq 数据中的肿瘤内异质性。
