Enhancing open data sharing for functional genomics experiments: Measures to quantify genomic information leakage and file formats for privacy preservation

加强功能基因组学实验的开放数据共享：量化基因组信息泄漏的措施和保护隐私的文件格式

• 批准号: 10251876
• 负责人: Mark Bender Gerstein
• 金额: $ 52.65万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-02 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10251876
• 关键词: 3-Dimensional; Address; Algorithms; Assessment tool; Biology; ChIP-seq; Code; Computer software; Consent; DNA sequencing; Data; Data Files; Data Science; Data Set; Databases; Diet; Disease; Environment; Equilibrium; Extravasation; Future; Gene Expression; Genes; Genetic; Genetic Transcription; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Glean; Hi-C; Human; Individual; Institutes; Laws; Learning; Letters; Life Style; Link; Machine Learning; Malignant Neoplasms; Maps; Measures; Medical Research; Methodology; Methods; Mining; Motivation; Participant; Patients; Phenotype; Positioning Attribute; Predisposition; Privacy; Privatization; Procedures; Process; Protein Isoforms; Protocols documentation; Provider; Pythons; Quantitative Trait Loci; RNA Splicing; Research Personnel; Risk; Risk Assessment; Sampling; Sequence Alignment; Signal Transduction; Single Nucleotide Polymorphism; Smoker; Source; Structure; Techniques; The Cancer Genome Atlas; Tissues; Variant; base; clinically relevant; computerized data processing; data mining; data sharing; experimental study; file format; functional genomics; genome sequencing; genomic data; human tissue; interest; large datasets; microbial; microbiome; open data; privacy preservation; social; tool; transcriptome sequencing

Project Summary/Abstract: With the surge of large genomics data, there is an immense increase in the breadth and depth of different omics datasets and an increasing importance in the topic of privacy of individuals in genomic data science. Detailed genetic and environmental characterization of diseases and conditions relies on the large-scale mining of functional genomics data; hence, there is great desire to share data as broadly as possible. However, there is a scarcity of privacy studies focused on such data. A key first step in reducing private information leakage is to measure the amount of information leakage in functional genomics data, particularly in different data file types. To this end, we propose to to derive information-theoretic measures for private information leakage in different data types from functional genomics data. We will also develop various file formats to reduce this leakage during sharing. We will approach the privacy analysis under three aims. First, we will develop statistical metrics that can be used to quantify the sensitive information leakage from raw reads. We will systematically analyze how linking attacks can be instantiated using various genotyping methods such as single nucleotide variant and structural variant calling from raw reads, signal profiles, Hi-C interaction matrices, and gene expression matrices. Second, we will study different algorithms to implement privacy-preserving transformations to the functional genomics data in various forms. Particularly, we will create privacy-preserving file formats for raw sequence alignment maps, signal track files, three-dimensional interaction matrices, and gene expression quantification matrices that contain information from multiple individuals. This will allow us to study the sources of sensitive information leakages other than raw reads, for example signal profiles, splicing and isoform transcription, and abnormal three-dimensional genomic interactions. Third, we will investigate the reads that can be mapped to the microbiome in the raw human functional genomics datasets. We will use inferred microbial information to characterize private information about individuals, and then combine the microbial information with the information from human mapped reads to increase the re-identification accuracy in the linking attacks described in the second aim. We will use the tools to quantify the sensitive information and privacy-preserving file formats in the available datasets from large sequencing projects, such as the ENCODE, The Cancer Genome Atlas, 1,000 Genomes, gEUVADIS, and Genotype-Tissue Expression projects.

项目摘要/摘要：随着大型基因组学数据的激增， 不同组学数据集的广度和深度以及隐私主题的重要性日益增加 基因组数据科学领域的个人。疾病的详细遗传和环境特征 条件依赖于功能基因组数据的大规模挖掘；因此，有强烈的分享愿望 尽可能广泛的数据。然而，针对此类数据的隐私研究却很少。一把钥匙 减少私人信息泄露的第一步是衡量信息泄露量 功能基因组学数据，特别是不同数据文件类型的数据。为此，我们建议推导出 不同数据类型中隐私信息泄露的信息论方法 基因组学数据。我们还将开发各种文件格式来减少共享过程中的这种泄漏。我们将 根据三个目标进行隐私分析。首先，我们将开发可用于 量化原始读取中的敏感信息泄漏。我们将系统分析链接攻击是如何进行的 可以使用各种基因分型方法来实例化，例如单核苷酸变异和结构 来自原始读数、信号图谱、Hi-C 相互作用矩阵和基因表达矩阵的变体调用。 其次，我们将研究不同的算法来实现对功能的隐私保护转换 各种形式的基因组学数据。特别是，我们将为原始序列创建保护隐私的文件格式 比对图、信号轨迹文件、三维交互矩阵和基因表达 包含来自多个个体的信息的量化矩阵。这将使我们能够研究 除原始读取之外的敏感信息泄漏源，例如信号配置文件、拼接和 同工型转录和异常的三维基因组相互作用。第三，我们将调查 可以映射到原始人类功能基因组数据集中的微生物组的读数。我们将使用 推断微生物信息来表征个人的私人信息，然后结合 微生物信息与人类映射读数的信息，以增加重新识别 第二个目标中描述的链接攻击的准确性。我们将使用这些工具来量化敏感的 大型测序项目的可用数据集中的信息和隐私保护文件格式， 例如 ENCODE、癌症基因组图谱、1,000 个基因组、gEUVADIS 和 Genotype-Tissue 表达项目。