Investigating systems physiology with multi-omics data

项目摘要 许多有价值的数据集已经从NIH共同基金项目中产生，包括大型RNA- 来自多个组织和物种的测序数据以及越来越多的高质量质谱， 蛋白质组学数据。虽然蛋白质组学提供了独特的见解，各种病理生理 目前，许多蛋白质组学数据集的规模仍然小于其RNA-seq对应物， 如何最好地整合蛋白质组学和RNA-seq数据以使其效用最大化仍有待研究。 该试点项目的目标是评估整合多组学数据的可行性， 关于跨组织生理学的假设在资助期间，我们将执行三项综合任务： 首先，我们将整合两个NIH共同基金中的组织、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 来学习跨人体组织的基因表达结构的低维表示， 应用于其他数据集，以促进综合分析。 其次，我们将应用我们最近开发的RNA测序指导蛋白质组学管道和软件， 为了从共同基金蛋白质组学数据中提取隐藏的肽信息， 同种型和翻译后修饰。然后，我们将评估整合的可行性和效用 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，我们将利用这个数据分析管道来预测跨组织通信模式。 从转录组学和蛋白质组学数据中提取，然后对这些数据进行有限的实验验证。 使用人类诱导多能干细胞（iPSC）衍生的细胞的计算发现。如果成功，我们 设想结果将为数据集成策略提供信息，这些策略可以帮助进一步提高 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， 跨多个组织的基因共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.