Removing statistical bottle-necks in data analysis for the ENCODE Consortium

描述（由申请人提供）：ENCODE联盟生成的数据构成了对人类基因组的功能和结构进行生物医学推断的前所未有的机会。在近1000个全基因组分析中，现在公开的关于每个碱基对的信息的深度是惊人的，在下一轮的联盟工作中，这个深度可能会以几何级数增加。在本提案中，我们描述了统计挑战，如果满足，将大大提高分析工作组（AWG）的能力，使功能生物学推断ENCODE数据。我们将应对这些挑战，作为一个统计的“研究和开发”组件与内置的实验验证能力，并将提供AWG与有用的软件实现的统计工具，我们开发，以及这些工具的迭代改进接地实验验证我们的估算网络。我们尤其会：1）开发降维方法，帮助AWG进行数据可视化、总结和预测研究（如从染色质数据预测转录）; 2）开发新的用ENCODE分析的复杂生物系统的定量网络模型;和3）进行靶向生物验证测定，其设计用于询问重要的低-维度结构，并反馈以改进模型结构和性能。我们的降维方法将帮助生物学家从高维基因组学数据中解释和制定假设，我们的网络模型将促进可解释的预测算法的构建，这些算法直接导致可测试和可量化的假设，我们的验证分析将确保从我们的工具中得出的推论提供有意义的生物学见解。正如我们作为ENCODE和modENCODE数据分析中心的一部分所做的那样，我们将与AWG密切合作，以确保我们的软件实现对联盟立即和最大限度地有用，并且我们在网络推理和降维方面的工作的总体过程集中在对联盟利益至关重要的生物学问题上。

DESCRIPTION (provided by applicant): The data generated by the ENCODE Consortium constitutes an unprecedented opportunity to make biomedical inferences about the function and structure of the human genome. With nearly 1000 genome-wide assays, the depth of information now publicly available about each base-pair is staggering, and in the next round of consortium work this depth is likely to increase geometrically. In this proposal, we describe statistical challenges that, if met, will substantially enhance the capacity of the Analysis Workin Group (AWG) to make functional biological inferences from ENCODE data. We will tackle these challenges, serving as a statistical "Research and Development" component with built-in experimental validation capabilities, and will provide the AWG with useful software implementations of the statistical tools we develop, as well with iterative refinements of these tools grounded in experimental validations of our imputed networks. In particular, we will: 1) develop methods of dimension reduction that will aid the AWG in data visualization, summarization, and prediction studies (e.g. the prediction of transcription from chromatin data); 2) develop new quantitative network models of complex biological systems assayed by ENCODE; and 3) conduct targeted biological validation assays designed to interrogate important low-dimensional structures in our models and to feed back to improve both model structure and performance. Our approaches to dimension reduction will aid biologists in interpreting and formulating hypotheses from high-dimensional genomics data, our network models will facilitate the construction of interpretable predictive algorithms that lead directly t testable and quantifiable hypotheses, and our validation assays will ensure that inferences derived from our tools provide meaningful biological insights. As we did as part of the ENCODE and modENCODE data analysis centers, we will work closely with the AWG to ensure that our software implementations are immediately and maximally useful to the consortium, and that the overall course of our work on network inference and dimension reduction is focused around biological questions central to the interests of the consortium.