Department of Statistics University of California, Berkeley, CA, USA.
Philos Trans A Math Phys Eng Sci. 2009 Nov 13;367(1906):4313-37. doi: 10.1098/rsta.2009.0164.
The landscape of genomics has changed drastically in the last two decades. Increasingly inexpensive sequencing has shifted the primary focus from the acquisition of biological sequences to the study of biological function. Assays have been developed to study many intricacies of biological systems, and publicly available databases have given rise to integrative analyses that combine information from many sources to draw complex conclusions. Such research was the focus of the recent workshop at the Isaac Newton Institute, 'High dimensional statistics in biology'. Many computational methods from modern genomics and related disciplines were presented and discussed. Using, as much as possible, the material from these talks, we give an overview of modern genomics: from the essential assays that make data-generation possible, to the statistical methods that yield meaningful inference. We point to current analytical challenges, where novel methods, or novel applications of extant methods, are presently needed.
在过去的二十年中，基因组学的格局发生了巨大的变化。测序成本的不断降低，已将主要关注点从获取生物序列转移到了研究生物功能上。已经开发出了许多检测方法来研究生物系统的复杂性，并且公开的数据库也推动了整合分析的发展，这些分析将来自多个来源的信息结合起来，得出复杂的结论。这种研究是最近在艾萨克·牛顿研究所（Isaac Newton Institute）举行的“生物学中的高维统计”研讨会的重点。会上展示和讨论了来自现代基因组学和相关学科的许多计算方法。我们尽可能使用这些演讲的材料，对现代基因组学进行了概述：从使数据生成成为可能的基本检测方法，到得出有意义推论的统计方法。我们指出了当前的分析挑战，需要新颖的方法，或者现有方法的新颖应用。
