BIGDATA: DA: Interpreting massive genomic data sets via summarization

BIGDATA：DA：通过汇总解释海量基因组数据集

• 批准号: 8642168
• 负责人: William Stafford Noble
• 金额: $ 20.78万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8642168
• 关键词: Bees; Big Data; Biology; Characteristics; Computing Methodologies; DNA Sequence; Data; Data Set; Disease; Elements; Explosion; Genes; Genome; Genomics; Grant; Human; Human Biology; Human Genome; Human body; Joints; Knowledge; Libraries; Malignant Neoplasms; Metagenomics; Methods; Natural Language Processing; Population; Recording of previous events; Research; Site; Technology; Text; Variant; cell type; computer framework; coping; design; epigenomics; human disease; microbial; novel; software development

Genomic data is big and getting ever bigger, but current analysis methods will not scale to the analysis of thousands or millions of genomes. Consequently, a critical technical challenge is to develop new methods that can analyze these enormous data sets. In this proposal, we describe a new computational framework for drawing inferences from massive genomic data sets. Our approach leverages submodular summarization methods that have been developed for analyzing text corpora. We will apply these methods to five big data problems in genomics: 1) identifying functional elements characteristic o f a given human cell type; 2) identifying genomic features associated with a particular subclass of cancer; 3-4) identifying genomic variants representative of ancestrally or phenotypically defined human populations; and 5) finding a set of microbial genes that characterize a given site on the human body. This project will advance discovery and understanding on two fronts. First, we will develop novel methods for summarizing genomic, epigenomic and metagenomic data sets. Indeed, to our knowledge, this grant proposes the first application of summarization methods to genomic data of any kind. The proposed research will significantly advance our ability to apply submodularity to these summarization tasks, particularly with respect to identifying and creating a library of distance functions that have bee validated with respect to the five tasks outlined in the proposal. Second, we will apply our novel methods to problems of profound importance. Indeed, significant progress toward any one of our five tasks would represent an important advance in our scientific understanding of human history, biology or disease. The impact of this project will grow as the big data problem grows, even after the project is complete. The results of this project, both the software that we develop and the summaries that we produce, will be useful for answering a wide array of questions in any field that must cope with big data.

基因组数据很大，而且越来越大，但当前的分析方法无法扩展到数千或数百万个基因组的分析。因此，一个关键的技术挑战是开发可以分析这些巨大数据集的新方法。在本提案中，我们描述了一种新的计算框架，用于从大量基因组数据集中得出推论。我们的方法利用了为分析文本语料库而开发的子模块摘要方法。我们将把这些方法应用于基因组学中的五个大数据问题：1）识别给定人类细胞类型的功能元件特征； 2) 识别与特定癌症亚类相关的基因组特征； 3-4) 鉴定代表祖先或表型定义的人群的基因组变异； 5) 寻找一组表征人体特定部位的微生物基因。该项目将促进两个方面的发现和理解。首先，我们将开发总结基因组、表观基因组和宏基因组数据集的新方法。事实上，据我们所知，这项资助首次提出将汇总方法应用于任何类型的基因组数据。拟议的研究将显着提高我们将子模块性应用于这些摘要任务的能力，特别是在识别和创建距离函数库方面，这些距离函数已针对提案中概述的五个任务进行了验证。其次，我们将把我们的新颖方法应用于具有深远意义的问题。事实上，我们五项任务中任何一项的重大进展都将代表我们对人类历史、生物学或疾病的科学理解的重要进步。即使在项目完成后，该项目的影响也会随着大数据问题的增长而增长。该项目的结果，无论是我们开发的软件还是我们生成的摘要，都将有助于回答任何必须处理大数据的领域中的各种问题。