###
:2019,28(9):50-57
本文二维码信息
码上扫一扫!
单肿瘤组织微卫星不稳定探测方法
(1.中国科学院 计算机网络信息中心, 北京 100190;2.中国科学院大学, 北京 100190;3.中国互联网络信息中心, 北京 100190)
Microsatellite Instability Detection Method for Single Tumor Tissue
(1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;2.University of Chinese Academy of Sciences, Beijing 100190, China;3.China Internet Network Information Center, Beijing 100190, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 81次   下载 68
投稿时间:2019-02-25    修订日期:2019-03-15
中文摘要: 目前,随着下一代测序技术(Next-Generation Sequencing technology,NGS)的发展,围绕高通量测序数据的微卫星不稳定(Micro-Satellite Instability,MSI)探测方法与软件工具层出不穷,但存在需要配对正常组织测序数据做参照或大量微卫星稳定(Micro-Satellite Stable,MSS)样本的正常组织测序数据构建基准线的问题,这会在一定程度上给使用者造成不便.针对以上问题,本文提出一种基于信息熵理论使用样本肿瘤组织测序数据探测MSI的模型.首先,基于之前开发的探测软件MSlsensor1.1,增加在单肿瘤组织测序数据上探测样本MSI状态的模块,扩增后的软件可实现基于两种数据模式的MSI探测.两种数据模式分别是肿瘤与正常组织成对测序数据和单肿瘤组织测序数据.其次,对扩展模块进行性能评估.依托于该模块,使用样本肿瘤组织的外显子测序数据对衡量软件性能的指标进行评估.结果显示,本研究提出的基于信息熵理论在单肿瘤组织测序数据上的探测模块性能表现较好,这为后续更为复杂的变异信号探测提供了理论依据和技术支撑.
Abstract:At present, with the development of Next-generation Sequencing Technology (NGS), new MSI detection methods and software tools are emerging around high-throughput sequencing data. However, existing methods have problems that they need paired normal tissue sequencing data for reference or a large number of other microsatellite stable (MSS) samples’ normal tissue sequencing data to construct baselines. And this will cause the inconvenience to useness. Regarding the issue above, a new MSI detection model based on the information entropy theory for tumor tissue sequencing data is proposed in this study. First, built on original detection software MSlsensor1.1, the MSI detection module based on only tumor tissue sequencing data is proposed and optimized. The augmented software can perform MSI detection for tumor-normal paired sequencing data and single tumor sequencing data. Second, benchmark performance is evaluated on the extended module. For this module, the software performance indicators were evaluated using exon sequencing data of samples. The results show that the performance of the detection module with information entropy theory for single tumor sequencinge data is better, which provides theoretical basis and technical support for the subsequent iteration of more complex mutation signal detection process.
文章编号:     中图分类号:    文献标志码:
基金项目:国家重点研发计划(2016YFC0503607,2018YFB0203903);国家自然科学基金(31771466);中国科学院信息化专项(XXH13504-08);青海省科技成果转化专项(2016-SF-127);中国科学院“百人计划”海外引进杰出人才择优支持(牛北方)
引用文本:
赵丹,尚秋明,韩鑫胤,李瑞琳,何小雨,祝海栋,牛北方.单肿瘤组织微卫星不稳定探测方法.计算机系统应用,2019,28(9):50-57
ZHAO Dan,SHANG Qiu-Ming,HAN Xin-Yin,LI Rui-Lin,HE Xiao-Yu,ZHU Hai-Dong,NIU Bei-Fang.Microsatellite Instability Detection Method for Single Tumor Tissue.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):50-57

用微信扫一扫

用微信扫一扫