肿瘤融合基因RNASeq检测生物信息学分析质控物的研究

RNASeq融合基因检测在肿瘤诊断和用药指导中发挥着重要作用。作为RNASeq融合基因检测的重要组成部分，生物信息学分析对于检测结果的准确性具有决定性意义。但缺乏合适的生物信息学分析参考数据对生物信息学分析流程进行评估，是目前RNASeq融合基因检测实验室面临的难题。在前期研究中，我们开发了一款制备生物信息学分析参考数据的肿瘤体细胞突变模拟软件，但该软件不能用于RNASeq融合基因的模拟。本项目拟在前期的基础上，开发一款RNASeq融合基因模拟软件，用于制备融合基因RNASeq检测生物信息学分析参考数据。通过实验证明该软件模拟融合基因的真实性、可靠性与稳定性，并评价其用于室内质控和室间质评的效果。本研究解决了融合基因RNASeq检测生物信息学分析参考数据来源的问题，可用于实验室自建方法的性能确认、室内质控以及室间质评，将为临床融合基因RNASeq检测的标准化提供助力。

The driving roles of next-generation RNA sequencing fusion genes detection have been recognized for decades, with efficacies demonstrated in clinical tumor diagnosis and targeted therapy. As an integral component of next-generation sequencing, bioinformatics pipelines to detect fusion genes have a significant impact on genetic test results. However, the real-world performance of bioinformatics pipelines in clinical laboratories is unclear due to the lack of appropriate reference dataset with known positive and negative fusion genes. In our preliminary study, we developed a tool for variant simulation to generate user-specific reference datasets based on real sequencing data which emulate the real-world environment of the wet laboratory process. However，this tool is only suit for DNA sequencing, can't simulate fusion genes for RNA sequencing data. Therefore, based on the previous research, we intend to develop a software tool for simulating fusion genes. A series of proof-of-principle validation studies will be performed to demonstrated the reliability and robustness of this tool as an unbiased and powerful calibration tool for fusion genes simulation. The fusion gene reference datasets generated by this tool will be further evaluated as internal quality control (IQC) samples and external quality assessment (EQA) samples. This software tool can solve the problem of reference datasets sources for fusion genes analysis in clinical laboratories and can be used to confirm the performance of laboratory LDTs, internal quality control, and to evaluate the accuracy of bioinformatics pipelines among laboratories, which provide a guarantee to improve the accuracy of RNASeq fusion gene detection.