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Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data.


ABSTRACT: 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.

SUBMITTER: Yuan X 

PROVIDER: S-EPMC10985232 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data.

Yuan Xilu X   Wang Haishuai H   Sun Zhongquan Z   Zhou Chunpeng C   Chu Simon Chong SC   Bu Jiajun J   Shen Ning N  

Cell reports methods 20240318 3


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  ...[more]

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