人群拷贝数多态性识别和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: | 案例西部储备大学 |
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(CNP)被定义为与基因组拷贝数有关的多态性基因组片段,在人群中以大于1%的频率分离,与多种疾病相关。由于稀有拷贝数变异(CNV)和CNP具有不同的特征,因此发现CNP的问题提供了超越旨在识别稀有CNV的算法可用的机会。我们提出了一种识别和基因分型的常见CNPs的方法。所提出的方法POLYGON会在每个CNP上生成样本的拷贝数基因型,并通过将CNP识别和基因分型成帧作为一个具有明确制定的目标函数的优化问题来微调其边界。我们将POLYGON应用于来自数百个样本的数据,并证明它可以显着提高现有单个样本CNV识别方法的性能。与其他两种CNP鉴定/基因分型方法相比,我们还证明了其优越的性能。 p> |
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: | 多态性基因组; 目标函数; 问题优化 |
课程来源: | 视频讲座网 |
数据采集: | 2021-03-07:zyk |
最后编审: | 2021-03-10:zyk |
阅读次数: | 47 |