Computational Tools for Mining Large Amounts of ChIP and Gene Expression Data

用于挖掘大量 ChIP 和基因表达数据的计算工具

• 批准号: 8856618
• 负责人: Hongkai Ji
• 金额: $ 39.38万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-25 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8856618
• 关键词: Address; Area; Biological; Biomedical Research; Categories; Collection; Computer software; Coupled; DNA; DNA-Protein Interaction; Data; Data Analyses; Data Collection; Data Set; Databases; Dependency; Development; Disease; Erinaceidae; Ewings sarcoma; Exons; Gene Expression; Gene Expression Regulation; Gene Proteins; Gene Targeting; Genes; Genomics; Goals; Human; Hybridization Array; Individual; Internet; Knowledge; Laboratories; Link; MYCN gene; Maps; Massive Parallel Sequencing; Measures; Methods; Mining; Molecular Profiling; Mus; Names; Nephroblastoma; Ontology; Pathway interactions; Pattern; Proteins; Public Domains; Regulatory Pathway; Research; Sampling; Scientist; Software Tools; Solutions; Statistical Methods; Surveys; System; Technology; Testing; Tissues; Transcription factor genes; Translating; Variant; base; biological systems; cell type; chromatin immunoprecipitation; computer framework; computerized tools; data integration; data mining; functional genomics; gene function; genome-wide; high throughput analysis; novel; novel strategies; programs; research study; skills; tool; transcription factor; transcriptome sequencing; user friendly software

DESCRIPTION (provided by applicant): ChIP-seq and ChIP-chip, hereinafter referred to as ChIPx, are powerful technologies to map genome-wide protein-DNA interactions (PDIs). Microarray, exon array and RNA-seq, on the other hand, are widely used to measure gene expression. Integrating ChIPx and gene expression data provides a powerful approach to study gene regulation both during development and in diseases. Traditionally, ChIPx and gene expression experiments conducted by a single laboratory are mainly used to study a specific biological system. The collective efforts of many labs have resulted in a large volume of data representing diverse biological systems. Jointly, these data contain enormous amounts of information that have not been fully utilized by each individual lab. This proposal aims to develop a coordinated set of computational, statistical and software tools to allow scientists to synthesize information in 3000+ publicly available ChIPx samples and 60,000+ gene expression profiles in human and mouse to make new discoveries. The project will turn these heterogeneous data into a tool for high-throughput discovery of biological contexts (i.e., cell types, tissues and diseases) associated with gene regulatory pathway activities. First, a statistical method named Gene Set Context Analysis (GSCA) will be developed. GSCA utilizes large amounts of public gene expression data to infer biological contexts and diseases in which one or more gene sets (i.e., groups of genes) are coordinately activated or inactivated. Second, based on the GSCA, a method called Transcription Factor Context Analysis (TFCA) will be developed. TFCA discovers novel functional contexts of transcription factors (TFs) and gene regulatory pathways. This method first classifies target genes of a TF into different functional categories by integrating one's own ChIPx and gene expression data with public ChIPx and Gene Ontology data. It then uses GSCA to systematically discover biological contexts (including diseases) associated with the function of each category. Collectively, GSCA and TFCA will establish a new paradigm for analyzing ChIPx and gene expression data. The conventional approach analyzes data tied to a particular system. In the new approach, one also leverages the rich information in public ChIPx and gene expression data to extend findings in one system to other biological systems. By allowing one to make novel discoveries beyond the scope of the original experiments and connect gene regulatory pathways to diseases, the new approach will significantly increase the value of both new and existing data. Applying GSCA and TFCA, 3000+ ChIPx samples and 60,000+ gene expression samples in human and mouse will be analyzed together to systematically map TF functions and ChIPx defined regulatory pathway activ- ities to diseases. Some new predictions will be validated experimentally. In addition to creating new knowledge about a variety of diseases, this research will provide urgently needed data integration and data mining tools to help scientists to translate the rich information in the publicly available ChIPx and gene expression data into new discoveries, and identify promising new areas of biomedical research.

描述(申请人提供)：CHIP-SEQ和CHIP-CHIP，以下简称ChIPx，是绘制全基因组蛋白质-DNA相互作用(PDI)的强大技术。另一方面，微阵列、外显子阵列和RNA-SEQ被广泛用于基因表达的检测。整合ChIPx和基因表达数据为研究发育和疾病中的基因调控提供了一种强有力的方法。传统上，ChIPx和基因表达实验由单个实验室进行，主要用于研究特定的生物系统。许多实验室的集体努力已经产生了代表不同生物系统的大量数据。总而言之，这些数据包含了大量的信息，每个实验室都没有充分利用这些信息。这项提议旨在开发一套协调的计算、统计和软件工具，使科学家能够在3000多个公开可用的ChIPx样本和60,000多个人类和老鼠的基因表达谱中合成信息，以做出新的发现。该项目将把这些异质数据转化为高通量发现与基因调控途径活动相关的生物学背景(即细胞类型、组织和疾病)的工具。首先，将开发一种名为基因集上下文分析(GSCA)的统计方法。GSCA利用大量公开的基因表达数据来推断生物学背景和疾病，在这些疾病中，一个或多个基因集(即，基因组)被协同激活或失活。其次，将在GSCA的基础上开发一种称为转录因子上下文分析(TFCA)的方法。TFCA发现了转录因子(TF)和基因调控途径的新功能背景。该方法首先通过将自己的ChIPx和基因表达数据与公开的ChIPx和基因本体论数据相结合，将转铁蛋白的目标基因分类为不同的功能类别。然后，它使用GSCA系统地发现与每个类别的功能相关的生物背景(包括疾病)。总而言之，GSCA和TFCA将建立一个分析ChIPx和基因表达数据的新范式。传统方法分析绑定到特定系统的数据。在新的方法中，人们还利用公共ChIPx和基因表达数据中的丰富信息来将一个系统中的发现扩展到其他生物系统。通过允许人们做出超出原始实验范围的新发现，并将基因调控途径与疾病联系起来，新方法将显著增加新数据和现有数据的价值。应用GSCA和TFCA，我们将对3000多个ChIPx样本和6万多个人和小鼠的基因表达样本进行分析，以系统地定位Tf的功能，并确定ChIPx对疾病的调控途径活性。一些新的预测将得到实验验证。除了创造关于各种疾病的新知识外，这项研究还将提供迫切需要的数据集成和数据挖掘工具，以帮助科学家将公开可用的ChIPx和基因表达数据中的丰富信息转化为新发现，并确定有前途的生物医学研究的新领域。