Cui Yunlong, Li Hongfeng, Liu Pengfei, Wang Hailong, Zhang Zhenzhen, Qu Hongzhu, Tian Caijuan, Fang Xiangdong
Department of Hepatobiliary Oncology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Department of Clinical Laboratory, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin, China.
Front Genet. 2023 Jan 6;13:1118183. doi: 10.3389/fgene.2022.1118183. eCollection 2022.
Genetic testing is becoming more and more accepted in the auxiliary diagnosis and treatment of tumors. Due to the different performance of the existing bioinformatics software and the different analysis results, the needs of clinical diagnosis and treatment cannot be met. To this end, we combined Bayesian classification model (BC) and fisher exact test (FET), and develop an efficient software DeteX to detect SNV and InDel mutations. It can detect the somatic mutations in tumor-normal paired samples as well as mutations in a single sample. Combination of Bayesian classification model (BC) and fisher exact test (FET). We detected SNVs and InDels in 11 TCGA glioma samples, 28 clinically targeted capture samples and 2 NCCL-EQA standard samples with DeteX, VarDict, Mutect, VarScan and GatkSNV. The results show that, among the three groups of samples, DeteX has higher sensitivity and precision whether it detects SNVs or InDels than other callers and the F1 value of DeteX is the highest. Especially in the detection of substitution and complex mutations, only DeteX can accurately detect these two kinds of mutations. In terms of single-sample mutation detection, DeteX is much more sensitive than the HaplotypeCaller program in Gatk. In addition, although DeteX has higher mutation detection capabilities, its running time is only .609 of VarDict, which is .704 and .343 longer than VarScan and MuTect, respectively. In this study, we developed DeteX to detect SNV and InDel mutations in single and paired samples. DeteX has high sensitivity and precision especially in the detection of substitution and complex mutations. In summary, DeteX from NGS data is a good SNV and InDel caller.
基因检测在肿瘤辅助诊断和治疗中越来越被接受。由于现有生物信息学软件性能各异,分析结果不同,无法满足临床诊断和治疗需求。为此,我们将贝叶斯分类模型(BC)和费舍尔精确检验(FET)相结合,开发了一款高效软件DeteX来检测单核苷酸变异(SNV)和插入缺失(InDel)突变。它既能检测肿瘤-正常配对样本中的体细胞突变,也能检测单一样本中的突变。结合贝叶斯分类模型(BC)和费舍尔精确检验(FET)。我们用DeteX、VarDict、Mutect、VarScan和GatkSNV检测了11个TCGA胶质瘤样本、28个临床靶向捕获样本和2个NCCL-EQA标准样本中的SNV和InDel。结果表明,在这三组样本中,无论检测SNV还是InDel,DeteX的灵敏度和精准度都高于其他调用程序,且DeteX的F1值最高。特别是在检测替换和复杂突变时,只有DeteX能准确检测这两种突变。在单样本突变检测方面,DeteX比Gatk中的HaplotypeCaller程序灵敏得多。此外,虽然DeteX的突变检测能力更强,但其运行时间仅为VarDict的0.609,分别比VarScan和MuTect长0.704和0.343。在本研究中,我们开发了DeteX来检测单样本和配对样本中的SNV和InDel突变。DeteX具有高灵敏度和精准度,尤其是在检测替换和复杂突变方面。总之,来自NGS数据的DeteX是一款优秀的SNV和InDel调用程序。