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， 癌症中的基因组事件，并强调了可以使癌症发生的深层复杂性和显著异质性。 精准医疗的挑战分子分型有助于识别具有共同特征的样本组， 可以用于临床决策的基因组特征。RNA的表达在肿瘤的发生中起着重要的作用。 分子分型的研究进展RNA表达数据与其他数据类型的集成， 作为基因组数据分析网络的一部分， 肿瘤发生和分离驾驶事件与乘客事件。我们的团队有超过20年的历史， 表达技术的创新，跨代检测和项目的创新。我们建议 运用我们的知识，经验和方法来解决将在此解决的项目 合作协议，包括来自ALCHEMIST或临床试验测序的临床试验样本 程序.我们建议处理四个具体的研究领域，包括“表达/mRNA分析”， “空间基因组学数据分析”、“数据整合”和“临床结果”。我们将提供1）RNA分析 测序和表达数据，2）空间转录组学数据的标准化和分析方法，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 与临床和其他分子特征的关联。我们将向其他GDAC提供我们的数据， 与他们合作，整合我们的分析并生成数据，与更大的科学机构共享， 通过Genome Data Commons。

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.