Data Science Tools to Increase Insight in Genomics Data

项目总结 高通量分子技术正越来越多地应用于生物医学和基础科学。新技术-- 生物学和化验正在快速发展，并有可能询问细胞过程。 以前所未有的分辨率和吞吐量。由于测量技术的发展， 越来越多的分子生物学研究人员需要统计方法来分析复杂的数据。我们有一个 开发这种方法和R35机制的记录将为我们提供灵活性，使我们的 随着新的分子方法的发展，人们正在做出努力。 我们将重点介绍(1)在实验之间测量共享分子组分的方法，(2)不同的方法。 利用单细胞RNA-SEQ进行鉴定和细胞周期测量，(3)检测作用于分子的选择 DNA甲基化、组蛋白修饰和转录因子结合等特征，以及(4)表观基因组学 分析包括纳米孔测序。

Project summary High-throughput molecular technologies are increasingly being used in biomedical and basic science. New tech- nologies and assays are being developed at a rapid pace, and have the potential to interrogate cellular processes at an unprecedented resolution and throughput. As a result of the developments in measurement technologies, more investigators in molecular biology are in need of statistical methods to analyze complex data. We have a track record of developing such methods and the R35 mechanism will provide us with the flexibility to pivot our effort as new molecular approaches are being developed. We will focus on methods for (1) measuring the shared molecular component between experiments, (2) differ- entiation and cell cycle measurements using single-cell RNA-seq, (3) detecting selection acting on molecular features such as DNA methylation, histone modifications and transcription factor binding, and (4) epigenomics assays including nanopore sequencing.