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阐明生物学 人类疾病。现在已经进行了数千项研究，测量不同组织中的基因表达。 组织和疾病。通过使用高分辨率的计算机技术聚合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， 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。 转录组聚合资源（TAR）;至少15万个样本 将通过云计算进行处理。此资源将包含表达的单碱基分辨率映射， 从头绘制了外显子-外显子剪接点和等位基因特异性表达在一组常见变异中的分布。 我们将使用清理和预测的元数据补充表达式数据。在目标2中，我们将开发统计 和计算方法，以充分实现这一资源的潜力。具体来说，我们将删除 在规模上不需要的变化，并开发混合模型来总结基因上的大数据资源， 结和单基极电平。在目标3中，我们将分析这些资源，以解决以下基本问题： 表达生物学，包括对基因表达异常值和等位基因特异性表达的系统研究， 结和单碱基分辨率。我们将在两个表达上推断出具有良好动力的共表达网络。 基因和剪接模式。 这项工作将通过分析基因组学数据，为我们理解基因表达做出重大贡献。 大规模

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.