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

项目概要/摘要：随着大量基因组学数据的激增， 不同的组学数据集的广度和深度，以及在隐私问题上日益重要的 基因组数据科学中的个人。疾病的详细遗传和环境特征， 条件依赖于功能基因组学数据的大规模挖掘;因此，有很大的愿望分享 数据尽可能广泛。然而，很少有隐私研究关注这些数据。一个关键 减少私人信息泄漏的第一步是测量 功能基因组学数据，特别是不同数据文件类型的数据。为此，我们建议导出 不同数据类型中私有信息泄漏的信息论度量 基因组学数据。我们还将开发各种文件格式，以减少共享过程中的泄漏。我们将 在三个目标下进行隐私分析。首先，我们将开发统计指标， 量化来自原始读取的敏感信息泄漏。我们将系统地分析如何连接攻击 可以使用各种基因分型方法例如单核苷酸变体和结构 来自原始读数、信号谱、Hi-C相互作用矩阵和基因表达矩阵的变异识别。 其次，我们将研究不同的算法来实现对函数的隐私保护转换， 各种形式的基因组数据。特别是，我们将为原始序列创建隐私保护文件格式 比对图、信号跟踪文件、三维相互作用矩阵和基因表达 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 原始读取以外的敏感信息泄漏源，例如信号分布、拼接和 异构体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 推断的微生物信息来表征关于个体的私人信息，然后联合收割机将 微生物信息与来自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 在来自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， 如ENCODE、癌症基因组图谱、1,000个基因组、gEUVADIS和基因型组织 表达项目。

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.