Combining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We use Genoppi to analyze 16 cell-type-specific protein interaction datasets of four proteins (BCL2, TDP-43, MDM2, PTEN) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer cell types and one human iPSC-derived neuronal cell type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodegenerative diseases. Importantly, our analyses suggest that human iPSC-derived neurons are a relevant model system for studying the involvement of BCL2 and TDP-43 in amyotrophic lateral sclerosis.

结合遗传和细胞类型特异性蛋白质组数据集可以产生生物学见解和治疗假设，但缺乏此类分析的技术和统计框架。在这里，我们提出了一种名为 Genoppi (lagelab.org/genoppi) 的开源计算工具，它可以将定量蛋白质组结果与遗传数据进行稳健、标准化和直观的整合。我们使用 Genoppi 分析了涉及癌症和神经系统疾病的四种蛋白质（BCL2、TDP-43、MDM2、PTEN）的 16 个细胞类型特异性蛋白质相互作用数据集。通过对数据的系统质量控制以及与已发表的蛋白质相互作用的整合，我们展示了三种癌细胞类型和一种人类 iPSC 衍生神经细胞类型中细胞类型无关和细胞类型特异性相互作用的一般模式。此外，通过整合 Genoppi 中的蛋白质组和遗传数据集，我们的结果表明这些蛋白质的神经元特异性相互作用正在介导它们与神经退行性疾病的遗传参与。重要的是，我们的分析表明，人类 iPSC 衍生的神经元是研究 BCL2 和 TDP-43 参与肌萎缩侧索硬化症的相关模型系统。