Core D: Quantitative Biology: Biostatistics, Bioinformatics, and Computation

核心 D：定量生物学：生物统计学、生物信息学和计算

• 批准号: 8889482
• 负责人: MARTYN T SMITH
• 金额: $ 0.5万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8889482
• 关键词: Address; Archives; Area; Bioinformatics; Biological; Biological Markers; Biology; Biometry; Collaborations; Communities; Computational Biology; Consensus; Consult; Data; Data Collection; Data Set; Databases; Development; Disease; Environment; Environmental Exposure; Epidemiology; Equilibrium; Etiology; Evolution; Experimental Designs; Genome; Goals; Homologous Gene; Knowledge; Measurement; Measures; Metadata; Methodology; Methods; Modeling; Online Systems; Pathway interactions; Pattern; Persons; Poison; Proliferating; Proteome; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Sampling; Soil; Solutions; System; Techniques; Technology; Toxin; Work; Yeasts; base; data mining; effective intervention; experimental analysis; high throughput technology; method development; outreach; programs; tool

The purpose of the Quantitative Biology Core is to provide investigators with consultative support in biostatistics/computational biology and bioinformatics, and to support web-based dissemination of bioinformatic solutions and database access. Most specific aims with the projects produce high-dimensional biological and exposure data, and often involve complicated questions addressing the possible interaction of environmental exposures and high-dimensional measures of the genome, proteome, and other high throughput technologies. These high-dimensional data sets are characterized by many thousands of measurements made on each unit (e.g. person, yeast culture, soil community). Core D reflects an evolution in the field of biostatistics and bioinformatics towards developing methodologies that can both find patterns in high dimensional data sets as well as providing proper statistical inference for these patterns. A consensus among our project researches and the methodological experts has formed around a set of core principles regarding optimal estimation and inference in the context of complicated questions and high-dimensional data. Specifically, the consensus favors using (when possible): semi-parametric locally efficient estimation with robust inference and the development of optimal methods used to integrate the statistical results into existing metadata to suggest relevant biological pathways and networks. Applying this approach will enable analyses to incorporate diverse data to query similar patterns/pathways in both related toxins and possible related diseases thus substantially leveraging data generated by the Program. To implement this methodology, the Quantitative Biology Core will provide access to a computational environment that lends itself to the computationally intensive methods developed for data mining and re-sampling based inference. Because of the scale of the data collection as well as the desirability of converging to a general methodology, our Program requires a more centralized system that can both archive data for, provide sharing to this Core, guidance on the access of metadata/annotation and routines for leveraging such data to find overprinting of our results on existing hypothesized regulatory networks. The Core will also develop tools to find and compares pathway, and create and maintain a web-based system that will allow for both efficient sharing of our methodological expertise with the project researchers and ultimately serve as a tool for outreach among the general scientific community.

数量生物学核心的目的是为研究人员提供生物统计学/计算生物学和生物信息学方面的咨询支持，并支持生物信息学解决方案的网上传播和数据库访问。这些项目的最具体目标是产生高维生物学和暴露数据，并经常涉及复杂的问题，涉及环境暴露与基因组、蛋白质组和其他高通量技术的高维测量之间的可能相互作用。这些高维数据集的特点是在每个单元(例如人、酵母培养物、土壤群落)上进行了数千次测量。核心D反映了生物统计和生物信息学领域朝着开发既能在高维数据集中找到模式又能为这些模式提供适当统计推断的方法的演变。我们的项目研究人员和方法论专家围绕一套关于复杂问题和高维数据背景下的最佳估计和推理的核心原则达成了共识。具体地说，共识倾向于(在可能的情况下)使用：具有稳健的半参数局部有效估计 推断和开发用于将统计结果整合到现有元数据中的最佳方法，以提出相关的生物路径和网络。应用这一方法将使分析能够合并不同的数据，以查询相关毒素和可能的相关疾病的相似模式/途径，从而在很大程度上利用该方案产生的数据。为了实现这一方法论，定量生物学核心将提供一个计算环境，该环境适合为数据挖掘和基于重新采样的推理开发的计算密集型方法。由于数据收集的规模以及向一般方法融合的可取性，我们的计划需要一个更集中的系统，该系统可以将数据存档，并提供与该核心的共享、元数据/注释访问指南和利用这些数据在现有假想的监管网络上发现我们的结果的套印的例程。核心还将开发工具来寻找和比较路径，并创建和维护一个基于网络的系统，使我们能够有效地与项目研究人员分享我们的方法学专门知识，并最终成为在一般科学界进行宣传的工具。