Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data

Greta Pintacuda, Frederik H. Lassen, Yu-Han H. Hsu, April Kim, Jacqueline M. Martín, Edyta Malolepsza, Justin K. Lim, Nadine Fornelos, Kevin C. Eggan () and Kasper Lage ()
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Greta Pintacuda: Stanley Center at Broad Institute of MIT and Harvard
Frederik H. Lassen: Stanley Center at Broad Institute of MIT and Harvard
Yu-Han H. Hsu: Stanley Center at Broad Institute of MIT and Harvard
April Kim: Stanley Center at Broad Institute of MIT and Harvard
Jacqueline M. Martín: Stanley Center at Broad Institute of MIT and Harvard
Edyta Malolepsza: Stanley Center at Broad Institute of MIT and Harvard
Justin K. Lim: Stanley Center at Broad Institute of MIT and Harvard
Nadine Fornelos: Stanley Center at Broad Institute of MIT and Harvard
Kevin C. Eggan: Stanley Center at Broad Institute of MIT and Harvard
Kasper Lage: Stanley Center at Broad Institute of MIT and Harvard

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22648-5

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