COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery

TL;DR

COMO is a user-friendly pipeline that integrates multi-omics data to develop context-specific metabolic models, aiding drug discovery and target prediction for various diseases.

Contribution

It introduces a comprehensive, Docker-based pipeline for multi-omics integration and metabolic modeling, streamlining drug target prediction and disease treatment research.

Findings

Predicted 25 drug targets for rheumatoid arthritis.

Predicted 23 drug targets for systemic lupus erythematosus.

Demonstrated versatility for any cell or tissue type.

Abstract

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools. We developed COMO, a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases, and disease data to aid drug discovery. COMO can be installed as a Docker image and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for multi-omics integration of bulk and single-cell RNA-seq, microarrays, and proteomics to develop context-specific metabolic models. Using public databases, open-source solutions for model construction, and a streamlined approach for predicting repurposable drugs, COMO empowers researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict 25 and 23 metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies.