Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Genoppi: 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, Kasper Lage
doi: https://doi.org/10.1101/2020.05.04.076034
Greta Pintacuda
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frederik H. Lassen
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yu-Han H. Hsu
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
April Kim
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jacqueline M. Martín
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Edyta Malolepsza
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Justin K. Lim
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
4Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nadine Fornelos
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kevin C. Eggan
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: eggan@mcb.harvard.edu lage.kasper@mgh.harvard.edu
Kasper Lage
2Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: eggan@mcb.harvard.edu lage.kasper@mgh.harvard.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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 Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted May 05, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Genoppi: an open-source software for robust and standardized integration of proteomic and genetic data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Genoppi: 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, Kasper Lage
bioRxiv 2020.05.04.076034; doi: https://doi.org/10.1101/2020.05.04.076034
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Genoppi: 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, Kasper Lage
bioRxiv 2020.05.04.076034; doi: https://doi.org/10.1101/2020.05.04.076034

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8766)
  • Bioengineering (6480)
  • Bioinformatics (23346)
  • Biophysics (11751)
  • Cancer Biology (9150)
  • Cell Biology (13255)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11370)
  • Epidemiology (2066)
  • Evolutionary Biology (15088)
  • Genetics (10402)
  • Genomics (14012)
  • Immunology (9122)
  • Microbiology (22050)
  • Molecular Biology (8780)
  • Neuroscience (47375)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2209)
  • Systems Biology (6016)
  • Zoology (1250)