Methods and Software to Enhance Genomic Privacy and Sharing of RNA-Seq Data

摘要 随着生物医学研究的广度和深度的空前增加，隐私受到了广泛的关注 数据集，特别是个人基因组学数据集。大多数关于基因组隐私的研究都集中在保护方面 个人基因组的变异。然而，分子表型数据集也可以包含大量的 敏感信息。虽然它们没有明确的基因型信息， 相关性可用于统计学地连接表型和基因型数据集。我们会研究 表型数据集敏感信息泄漏的分析方法。重点抓好 RNA-seq数据集和相关的敏感信息泄漏源。这些泄漏是经过调解的 数量性状基因座的表达。我们将在三个目标下进行隐私分析。我们将首先瞄准 提出可用于量化敏感信息泄漏的统计指标， 表型数据集。这些量化可以用来评估隐私泄露的风险。在 第二个目标，我们将集中系统地分析如何链接攻击可以实例化和分析。我们 我将研究如何推广链接攻击，使隐私研究人员能够研究风险 与这些袭击有更系统的联系。然后我们将评估不同的基因型模型 预测和评估如何将其用于链接攻击。我们将特别关注离群基因 表达水平，并评估离群值如何用于基因型预测和链接攻击。 在第三个目标中，我们将开发实现量化、风险估计和风险管理的工具 方法，并将这些方法集成到一个连贯的软件套件中，以进行全面的隐私分析， 使得能够在数据集的不同汇总水平上保护RNA-seq数据集，例如，读段，基因 和转录本定量。我们将致力于增加基因组隐私软件工具的数量 分析.我们将研究不同的算法方法，以解决高计算复杂度的 文献中的匿名技术。我们会研究敏感资料外泄的来源， 基因表达水平，例如剪接和非编码转录。将研究这些信息来源 在先前目标中提出的风险量化和管理战略的背景下。我们将最终 使用这些工具量化来自大型测序的公开数据集中的敏感信息 例如ENCODE、1000 Genomes、TCGA、GEUVADIS和GTex。

Abstract Privacy is receiving much attention with the unprecedented increase in the breadth and depth of biomedical datasets, particularly personal genomics datasets. Most studies on genomic privacy are focused on protection of variants in personal genomes. Molecular phenotype datasets, however, can also contain substantial amount of sensitive information. Although there is no explicit genotypic information in them, subtle genotype-phenotype correlations can be used to statistically link the phenotype and genotype datasets. We will study the methodologies for analysis of sensitive information leakage from phenotype datasets. We will focus on the RNA-seq datasets and the associated sources of sensitive information leakage. These leakages are mediated by the expression quantitative trait loci. We will approach the privacy analysis under 3 aims. We will first aim at proposing statistical metrics that can be used for quantification of the sensitive information leakage from phenotype datasets. These quantifications can be used to evaluate the risks of privacy breaches. In the second aim, we will focus systematical analysis of how linking attacks can be instantiated and analyzed. We will study how one can generalize linking attacks that enables the privacy researchers study the risks associated with these attacks more systematically. We will then evaluate different models of genotype prediction and assess how these can be used in linking attacks. We will focus, specifically, on the outlier gene expression levels and evaluate how the outliers can be used for genotype prediction and in the linking attacks. In the third aim, we will develop tools that implement the quantification, risk estimation, and risk management methodologies and integrate these in a coherent software suite for a comprehensive privacy analysis, which enables protecting RNA-seq datasets at different levels of summarizations of the datasets, e.g., reads, gene and transcript quantifications. We will aim at increasing the number of software tools for genomic privacy analysis. We will study different algorithmic approaches to tackle with the high computational complexity of anonymization techniques in the literature. We will study sources of sensitive information leakage other than gene expression levels, e.g. splicing and non-coding transcription. These sources of information will be studied in the context of risk quantification and management strategies presented in the previous aims. We will finally use the tools to quantify the sensitive information in the publicly available datasets from large sequencing projects, for example ENCODE, 1000 Genomes, TCGA, GEUVADIS, and GTex.