Specialized RNA analysis center for integrative genomic analyses

摘要 像癌症基因组图谱(TCGA)这样的项目已经产生了大量的数据来表征 癌症中的基因组事件，并突出了深层次的复杂性和显著的异质性 精准医学具有挑战性。分子分型有助于识别共享的样本组 可用于临床决策的基因组特征。RNA的表达在人类免疫缺陷中发挥了重要作用 在过去的二十年里，分子亚型。RNA表达数据与其他数据类型的集成 是作为基因组数据分析网络的一部分产生的，提高了我们对 肿瘤的发生和将驾驶事件与乘客事件分开。我们的团队有20多年的历史 表达技术的创新，跨越几代分析和项目的创新。我们建议 应用我们的知识、经验和方法来解决将在本课程中讨论的项目 合作协议，包括来自炼金术士或临床试验测序的临床试验样本 程序。我们建议解决四个具体的研究领域，包括‘表达/信使核糖核酸分析’， “空间基因组数据分析”、“数据集成”和“临床结果”。我们将提供1)RNA的分析 测序和表达数据，2)空间转录数据的归一化和分析方法，3) 用于癌症分类和表征的综合基因组方法，以及4)生物标志物的分析 用于与临床和其他分子特征相关联。我们将向其他GDAC提供我们的数据并进行工作 与他们协作集成我们的分析并生成数据，以便与更大的科学部门共享 通过基因组数据共享社区。

ABSTRACT Projects like The Cancer Genome Atlas (TCGA) have generated a tremendous amount of data characterizing the genomic events in cancer and highlighting the deep complexity and significant heterogeneity that can make precision medicine challenging. Molecular subtyping has helped to identify groups of samples with shared genomic characteristics that can be used for clinical decisions. RNA expression has played a strong role in molecular subtyping over the last twenty years. Integration of RNA expression data with other data types that are being generated as part of the Genome Data Analysis Network have improved our knowledge of tumorigenesis and separating driving events from passenger events. Our team has over a 20 year history of innovation in expression technologies, innovating across generations of assays and projects. We propose to apply our knowledge, experience, and methods to address the projects that will be addressed in this cooperative agreement including clinical trial samples from ALCHEMIST or the Clinical Trials Sequencing Program. We propose to address four specific areas of research including `expression/mRNA analysis', `Spatial genomics data analysis', `data integration', and “clinical outcomes.” We will provide 1) analysis of RNA sequencing and expression data, 2) normalization and analysis methods of spatial transcriptomics data, 3) integrative genomic approaches for cancer classification and characterization, and 4) analysis of biomarkers for association with clinical and other molecular features. We will provide our data to other GDACs and work collaboratively with them to integrate our analyses and generate data for sharing with the larger scientific community through the Genome Data Commons.