Genetic landscape of an in vivo protein interactome

Université de Montréal (2021-2025)

• Savandara Besse #, Tatsuya Sakaguchi #, Louis Gauthier, Zahra Sahaf, Olivier Péloquin, Lidice Gonzalez, Xavier Castellanos-Girouard, Nazli Koçatug, Chloé Matta, Julie Hussin, Stephen Michnick*, Adrian Serohijos*
  • Department of Biochemistry, Université de Montréal, Montréal, Québec, Canada (SB, TS, LoG, ZS, OP, LiG, XCG, NK, CM, SWM, AS)
  • Robert-Cedergren Center for Bioinformatics and Genomics, Université de Montréal, Montréal, Québec, Canada (SB, TS, LoG, ZS, OP, LiG, XCG, NK, CM, SM, AS)
  • Institut de Cardiologie de Montréal, Montréal, Québec, Canada (JH)
  • Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada (JH)

# These authors contributed equally.

piQTL website

https://ladyson1806.github.io/SerohijosLab-piQTL/

1. Contact

* Correspondence

• Adrian Serohijos (@aserohijos) : adrian.serohijos @ umontreal.ca
• Stephen Michnick (@michnics) : stephen.michnick @ umontreal.ca

Website and Code maintenance

• Savandara Besse (@ladyson1806) : savandara.besse @ cnrs.fr

2. Software and libraries

Python

• Python 3.11.4
• matplotlib 3.7.2
• matplotlib-venn 0.11.9
• mpl-scatter-density 0.7
• numpy 1.25.2
• pandas 2.0.3
• plotnine 0.12.2
• scipy 1.11.1
• seaborn 0.12.2
• tqdm 4.66.1
• venn 0.1.3

R

• R (ToDo: Specify the R version in this study)
• Required R packages: (ToDo: please specify as needed)

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3. Installation Guide

Python

1. Clone the repository:

   git clone https://github.com/ladyson1806/SerohijosLab-piQTL.git
   cd SerohijosLab-piQTL

   Note: The repository size is approximately 15 GB. Depending on your internet speed and disk performance, cloning may take 10–30 minutes or longer.

2. (Recommended) Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install Python dependencies:

   pip install -r requirements.txt

R

1. Install R (version 4.2 or higher recommended).

2. Install required R packages:

   install.packages(c("tidyverse", "data.table", ...)) # Add all required packages here

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4. Instructions for Use

The sequence data generated in this study are available in the Gene Expression Omnibus (GEO) under accession ID GSE246414. All Python scripts, R scripts, and Jupyter notebooks used for data analysis are provided and are fully executable. The outputs from both the analysis scripts and notebooks, including all figures, are saved in the data/ and figure/ directories. To reproduce these results, execute the scripts and notebooks as described belolw.

Running Python Scripts

To run a Python analysis script:

python target_script.py

Replace target_script.py with the name of the script you wish to execute.

Running R Scripts

To run an R script:

Rscript target_script.R

Replace target_script.R with the name of the script you wish to execute.

Running Jupyter notebooks

To run a Jupyter notebook:

1. Start the Jupyter Notebook server:

   jupyter notebook

2. In your web browser, navigate to the provided local URL (usually http://localhost:8888).
3. Open the desired .ipynb notebook file and run the cells as needed.

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