人群拷贝数多态性识别与基因分型的优化算法Optimization Algorithms for Identification and Genotyping of Copy Number Polymorphisms in Human Populations |
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课程网址: | http://videolectures.net/prib2010_ruffalo_oaig/ |
主讲教师: | Matthew Ruffalo |
开课单位: | 凯斯西储大学 |
开课时间: | 2010-10-14 |
课程语种: | 英语 |
中文简介: | 最近的研究表明,拷贝数多态性(CNPs),定义为基因组片段,其在基因组拷贝数方面是多态的并且在群体中以大于1%的频率分离,与各种疾病相关。由于罕见的拷贝数变异(CNV)和CNP具有不同的特征,因此发现CNP的问题提供了超出旨在识别稀有CNV的算法可用的机会。我们提出了一种识别常规CNP并对其进行基因分型的方法。所提出的方法POLYGON在每个CNP处产生样本的拷贝数基因型,并通过将CNP识别和基因分型框定为具有明确制定的目标函数的优化问题来精细调整其边界。我们将POLYGON应用于来自数百个样本的数据,并证明它显着改善了现有单样本CNV识别方法的性能。与其他两种CNP鉴定/基因分型方法相比,我们也证明了其卓越的性能。 |
课程简介: | Recent studies show that copy number polymorphisms (CNPs), defined as genome segments that are polymorphic with regard to genomic copy number and segregate at greater than 1% frequency in the populations, are associated with various diseases. Since rare copy number variations (CNVs) and CNPs bear different characteristics, the problem of discovering CNPs presents opportunities beyond what is available to algorithms that are designed to identify rare CNVs. We present a method for identifying and genotyping common CNPs. The proposed method, POLYGON, produces copy number genotypes of the samples at each CNP and fine-tunes its boundaries by framing CNP identification and genotyping as an optimization problem with an explicitly formulated objective function. We apply POLYGON to data from hundreds of samples and demonstrate that it significantly improves the performance of existing single-sample CNV identification methods. We also demonstrate its superior performance as compared to two other CNP identification/genotyping methods. |
关 键 词: | 基因组片段; 拷贝数变异; 目标函数 |
课程来源: | 视频讲座网 |
最后编审: | 2019-09-14:lxf |
阅读次数: | 70 |