# Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases

## TL;DR

This paper introduces a computational protocol to find shared disease mechanisms and identify drugs that could benefit multiple conditions.

## Contribution

A Snakemake-based computational protocol is introduced for identifying shared transcriptional risks and validating mutually beneficial compounds across diseases.

## Key findings

- The protocol identifies shared transcriptional patterns between diseases using omics data.
- Drug perturbation data is leveraged to screen for compounds with mutual benefits.
- Pharmacovigilance using electronic health records validates potential drug effects.

## Abstract

The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records.

For complete details on the use and execution of this protocol, please refer to Gao et al.1 and Baylis et al.2

•This protocol identifies shared transcriptional patterns between diseases•Accommodates novel, user-supplied datasets for enhanced generalizability•Leverages drug perturbation data to screen for mutually beneficial drugs•Validates putative drugs using data from electronic medical records

This protocol identifies shared transcriptional patterns between diseases

Accommodates novel, user-supplied datasets for enhanced generalizability

Leverages drug perturbation data to screen for mutually beneficial drugs

Validates putative drugs using data from electronic medical records

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records.