Analysis of genomics datasets at a massive scale

了解人类基因表达的正常和致病模式有助于了解生物学 人类疾病的威胁。现在已经进行了数千项研究，以测量不同物种的基因表达 组织和疾病。通过使用HIGH来聚集和分析所有可用的人类RNA测序数据 强大的计算和统计框架，我们将提供一个变革性的资源来描述 人类基因表达模式包括罕见的转录事件、细胞网络和遗传变异。 在目标1中，我们建议统一处理所有公开可用的人类转录组测序数据并收集它们 到名为Transcriptome Aggregation Resource(TAR)的公开可用资源中；至少150,000个样本 将使用云计算进行处理。该资源将包含表达的单基分辨率图， 从新定位外显子-外显子剪接连接和等位基因fic在一组常见变异中的表达。 我们将使用经过清理和预测的元数据来补充表达式数据。在目标2中，我们将发展统计学 以及充分实现这一资源潜力所需的计算方法。我们将删除特定的fi 并开发混合模型来总结基因中的大量数据资源， 交界处标高和单基准标高。在目标3中，我们将分析此资源以解决以下基本问题 表达生物学，包括对基因上表达异常值和等位基因fic表达的系统研究， 结点和单碱基分辨率。我们将在两个表达的基础上推断功能强大的共表达网络 基因和拼接模式。 这项工作将通过对基因组数据的分析，为我们理解基因表达做出重大贡献。fi 巨大的规模。

Understanding both normal and pathogenic patterns of human gene expression can help shed light on the biology of human disease. Thousands of studies have now been undertaken measuring gene expression in different tissues and diseases. By aggregating and analyzing all available human RNA-sequencing data using a high powered computational and statistical framework, we will provide a transformative resource for characterizing human gene expression patterns including rare transcriptional events, cellular networks, and genetic variation. In Aim 1 we propose to uniformly process all publicly available human transcriptome sequencing data and collect it into a publicly available resource called the Transcriptome Aggregation Resource (TAR); at least 150,000 samples will be processed using cloud computing. This resource will contain single-base resolution maps of expression, de novo mapped exon-exon splice junctions and allele specific expression across a set of common variations. We will supplement the expression data with cleaned and predicted metadata. In Aim 2 we will develop statistical and computational methods necessary to fully realize the potential of this resource. Specifically we will remove unwanted variation at scale and develop mixture models to summarize the large data resource at the gene, junction and single base levels. In Aim 3 we will analyze this resource to address fundamental questions in expression biology, include a systematic study of expression outliers and allele specific expression at the gene, junction and single base resolution. We will infer well-powered co-expression networks over both expressed genes and splicing patterns. This work will contribute significantly to our understanding of gene expression by analyzing genomics data at a massive scale.