美国南卡罗莱纳大学Feifei Xiao博士及Guoshuai Cai博士学术报告会

报告题目:An Accurate and Powerful Method for Copy Number Variation Detection

时间:2018年6月28日(周四)下午3:30-4:10

地点:中国药科大学江宁校区理学院实验楼C116

报告人:Feifei Xiao博士

主持人:言方荣


报告人简介:Feifei Xiao,现为美国南卡罗莱纳大学阿诺德公共卫生学院,流行病学和生物统计学系副教授,主要从事大数据分析,肿瘤流行病学,整合基因组学,群体遗传学等。

报告摘要:

Integration of multiple genetic sources for copy number variation detection is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change-point based methods can increase statistical power to identify variants, it remains challenging to effectively identify CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. Here we proposed a novel improvement by integrating the relative allelic intensity with prior information of statistics into modeling to boost statistical power for the identification of variants, so called modSaRa2. Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods. The improvement for weak CNV signals is the most substantial, while simultaneously improving stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated CNV variants which facilitates understanding of the possible roles of germline copy number variants in development of melanoma.


报告题目:Dispersion in RNA-seq Differential Analysis: Characterizing and Modeling

时间:2018年6月28日(周四)下午4:20-5:00

地点:中国药科大学江宁校区理学院实验楼C116

报告人:Guoshuai Cai博士

主持人:言方荣


报告人简介:Guoshuai Cai,现为美国南卡罗莱纳大学阿诺德公共卫生学院,环境健康科学系副教授,主要从事测序数据分析,整合基因组学,疾病预测及标记物选择等。

报告摘要:

RNA-seq is a common technique for surveying RNA expression. Because sequencing read counts from individuals often show dispersion of measurements significantly larger than that given by Poisson distribution, fine modeling on this so-called overdispersion is required for RNA-seq data analysis. Various methods have been proposed for RNA-seq differential expression detection, each with its own limitations. Here we asked (1) how is the dispersion between technical replicates? (2) is the dispersion specific to each base pair? and (3) how to model mRNA abundance to unlock the integration with numerous established upstream and downstream analyses? To answer these three questions, we studied the properties of RNA-seq read count including its dependency with sequencing depth and local primer sequence. Based on our findings, we propose models for accurate detection of differential expression from RNA-seq data.


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中国药科大学理学院

2018年6月20日





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