PT  - JOURNAL ARTICLE
AU  - Greta Pintacuda
AU  - Frederik H. Lassen
AU  - Yu-Han H. Hsu
AU  - April Kim
AU  - Jacqueline M. Martín
AU  - Edyta Malolepsza
AU  - Justin K. Lim
AU  - Nadine Fornelos
AU  - Kevin C. Eggan
AU  - Kasper Lage
TI  - Genoppi: an open-source software for robust and standardized integration of proteomic and genetic data
AID  - 10.1101/2020.05.04.076034
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.04.076034
4099  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.076034.short
4100  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.076034.full
AB  - Combining genetic and cell-type-specific proteomic datasets can lead to new 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 that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We used Genoppi to analyze sixteen cell-type-specific protein interaction datasets of four proteins (TDP-43, MDM2, PTEN, and BCL2) 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 and one human iPSC-derived neuronal 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 neurodevelopmental and neurodegenerative diseases. Importantly, our analyses indicate that human iPSC-derived neurons are a relevant model system for studying the involvement of TDP-43 and BCL2 in amyotrophic lateral sclerosis.Competing Interest StatementThe authors have declared no competing interest.