摘要
目的通过对现有4种拷贝数变异(CNV)分析工具CODEX、CONTRA、cn.MOPS和VarScan 2的评估,找到检测CNV的最适工具,以更好地进行眼遗传学研究。方法我们同时采用由软件模拟的数据、2组含有144个眼病基因的靶向二代测序和1组遗传性眼病患者-正常对照的全外显子测序(WES)数据来分析评价CNV检测软件,以灵敏度(sensitivity)和错误发现率(FDR)作为评价指标。结果4种工具均有较高的灵敏度(70%~90%)和较低的FDR(30%左右),且都能很好地检测扩增情况。cn.MOPS(灵敏度88.70%)和VarScan2(灵敏度80.26%)分别在检测扩增和缺失时有良好的表现。cn.MOPS虽然在算法上降低了FDR且在检测扩增时稍优于其他软件,但是在检测缺失情况时的效果相对较差。结论用于评估的CNV分析软件各有优缺点,可以根据研究的需要选择合适的CNV检测软件。
Objective To assess four tools used for the detection of copy number variation(CNV),and to find the proper ones for the genetic research in ophthalmology.Methods We used simulated data,two targeted next generation sequencing data including 144 genes WES data of two paired groups of patients-controls to evaluate the detection tools.Sensitivity and false discovery rate(FDR)were set as indicators for evaluating the accuracy of CNV detection tools.Results In general,each tool had a good sensitivity(70%~90%)and a low FDR(about 30%).All of them showed better detecting abilities of duplication variants than deletions.cn.MOPS(sensitivity=88.70%)and VarScan2(sensitivity=80.26%)showed better performance than other two tools in detecting duplications and deletions,respectively.Although cn.MOPS had a lower FDR with its algorithm and slightly better efficiency in detecting duplications than other softwares,it had a relatively poor performance in detecting deletions.Conclusions Each CNV detection software has its own advantages and disadvantages,and suitable CNV detection software should be chosen according to the needs of researchers.
作者
兰远铮
许晶乔
王大利
陈雪莉
LAN Yuanzheng;XU Jingqiao;WANG Dali;CHEN Xueli(Department of Ophthalmology,Eye&ENT Hospital,Fudan University,Shanghai 200031,China;State Key Laboratory of Genetic Engineering,Department of Microbiology and Microbial Engineering,School of Life Science,Fudan University,Shanghai 200438,China;Dalian Children’s Hospital of Liaoning Province,Dalian 116012,China)
出处
《中国眼耳鼻喉科杂志》
2022年第6期579-583,共5页
Chinese Journal of Ophthalmology and Otorhinolaryngology
关键词
遗传性眼病
CNV检测工具
二代测序
Hereditary eye disease
CNV detected tools
Next generation sequencing
