Investigating systems physiology with multi-omics data

项目总结 许多有价值的数据集已经从NIH共同基金计划中产生，包括大RNA- 来自多个组织和物种的测序数据以及日益可用的高质量质谱仪- 基于计量学的蛋白质组学数据。尽管蛋白质组学提供了对各种病理生理学的独特见解 目前，许多蛋白质组学数据集的规模仍然小于它们的rna-seq对应物和 如何最好地整合蛋白质组学和RNA-seq数据以最大限度地发挥它们的作用，这还有待研究。 该试点项目的目标是评估集成多组学数据以生成新数据的可行性 关于跨组织生理学的假设。我们将在资助期内执行三项综合任务： 首先，我们将在两个NIH共同基金中整合跨组织、物种和组学数据类型的数据 项目，即GTEx V8和MoTrPAC Release v1.0，借助迁移学习方法。我们的目标是 要了解人类组织中基因表达结构的低维表示，可以 应用于其他数据集，以便于综合分析。 其次，我们将应用RNA测序引导的蛋白质组学流水线和软件，我们最近 为从共同基金蛋白质组学数据中提取隐藏的多肽信息而开发，包括 异构体修饰和翻译后修饰。然后，我们将评估整合的可行性和实用性 转录组学和蛋白质组学数据，以检查基因表达中的多组织相关性。 最后，我们将利用这一数据分析管道来预测跨组织通信PAT- 来自转录组和蛋白质组学数据的Tern，然后执行有限的实验验证 使用人类诱导多能干细胞(IPSC)来源的细胞的计算结果。如果成功，我们将 设想结果将为数据集成战略提供信息，这些战略有助于进一步提高 公共领域的大规模蛋白质组学和RNA-SEQ数据，以及产生可检验的假设 基因在多个组织中的共同表达可以为未来的工作提供信息。

PROJECT SUMMARY Many valuable datasets have been generated from the NIH Common Fund programs, including large RNA- sequencing data from multiple tissues and species as well as increasingly available high-quality mass spec- trometry-based proteomics data. Although proteomics offers unique insight into various pathophysiological processes, currently many proteomics datasets remain smaller in scale than their RNA-seq counterparts and it remains to be investigated how best to integrate proteomics and RNA-seq data to maximize their utility. The goal of this pilot project is to assess the feasibility of integrating multi-omics data to generate new hypotheses about cross-tissue physiology. We will perform three integrated tasks during the funding period: First, we will integrate data across tissues, species, and omics data type in two NIH Common Fund projects, namely GTEx v8 and MoTrPAC Release v1.0, with the aid of transfer learning methods. Our goal is to learn a low-dimension representation of gene expression structure across human tissues that can be applied to other datasets to facilitate integrative analysis. Second, we will apply an RNA-sequencing guided proteomics pipeline and software that we recently developed in order to extract hidden peptide information from Common Fund proteomics data, including isoform and post-translational modifications. We will then evaluate the feasibility and utility of integrating transcriptomics and proteomics data to examine multi-tissue correlations in gene expression. Lastly, we will utilize this data analysis pipeline in order to predict cross-tissue communication pat- terns from transcriptomics and proteomics data, then perform limited experimental validation of the computational findings using human induced pluripotent stem cell (iPSC)-derived cells. If successful, we envision that the results will inform on data integration strategies that can help further increase the utility of large-scale proteomics and RNA-seq data in the public domain, as well as generate testable hypotheses on gene co-expression across multiple tissues that can inform future work.