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美国纽约大学胡明博士学术报告会

发布时间:2015-06-29浏览次数:1469

 

报告题目:Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

报告人:胡明博士 (美国纽约大学医学院公共健康系生物统计组,助理教授)

时间:2015630日(周二)下午16:30-17:30

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报告摘要:Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.

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2015625

报告人概况:胡明,1984年生,2006毕业于中国科学技术大学统计与金融系,获统计学学士学位及郭沫若奖学金;2008年于密歇根大学生物统计系(University of Michigan – Ann Arobr, School of Public Health, Department of Biostatistics)获生物统计硕士学位;2010年于密歇根大学生物统计系(University of Michigan – Ann Arbor, School of Public Health, Department of Biostatistics)获生物统计博士学位。2006年至2007年两次获得Richard G. Cornell Scholarship(理查德 G. 康奈尔奖学金)。2008年获Best Performance in Qualifying Exam(博士资格考试第一名)。2008年至2010年三次获得Rackham Conference Travel Grant AwardRackham会议旅行资助)。20109月至20137月于哈佛大学统计系(Harvard University, Department of Statistics)从事博士后研究工作。博士后导师为哈佛大学著名统计学家刘军博士。20138月起于纽约大学医学院公共健康系生物统计组(New York University School of Medicine, Department of Population Health, Division of Biostatistics)任终身教职序列助理教授(tenure-track assistant professor)。

研究方向: Modeling and computation in statistical genetics and genomics, applications in bioinformaticscomputational biology, Monte Carlo methods,统计基因学和基因组学中的模型和计算问题,在生物信息学、计算生物学中的应用,蒙特卡罗方法。

主要成果有以下7篇论文,至2015616日,共被引用1,238次:

[1] Hu M, Deng K, Qin ZS, Dixon J, Selvaraj S, Fang J, Ren B and Liu JS. (2013) Bayesian inference of spatial organizations of chromosomes. PLoS Computational Biology, 9(1): e1002893.  影响因子:5.940, 被引用38.

[2] Hu M, Deng K, Selvaraj S, Qin ZS, Ren B and Liu JS. (2012) HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics, 28(23): 3131-3133. 影响因子:6.968,被引用30.

[3] Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, Hu M, Liu JS and Ren B. (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485, 376-380. 影响因子:38.597,被引用674.

[4] Hu M, Zhu Y, Taylor JMG, Liu JS and Qin ZS. (2012) Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq. Bioinformatics, 28(1): 63-68. 影响因子:6.968,被引用20.

[5] Yu J, Yu J, Mani RS, Cao Q, Brenner CJ, Cao X, Wang X, Wu L, Li J, Hu M, Gong Y, Cheng H, Laxman B, Vellaichamy A, Shankar S, Li Y, Dhanasekaran SM, Morey R, Barrette T, Lonigro RJ, Tomlins SA, Varambally S, Qin ZS and Chinnaiyan AM. (2010) An integrated network of androgen receptor, polycomb, and TMPRSS2-ERG gene fusions in prostate cancer progression. Cancer Cell, 17(5): 443-454. 影响因子:27.283,被引用388次。

[6] Hu M, Yu J, Taylor JMG, Chinnaiyan AM and Qin ZS. (2010) On the detection and refinement of transcription factor binding sites using ChIP-Seq data. Nucleic Acids Research, 38(7), 2154-2167. 影响因子:8.378,被引用79次。

[7] Hu M and Qin ZS. (2009) Query large scale microarray compendium datasets using a model-based Bayesian approach with variable selection. PLoS One, 4(2): e4495.  影响因子:4.240. 被引用9次。

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