COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.

ABSTRACT: Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (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 or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for

SUBMITTER: Bessell B 

PROVIDER: S-EPMC10627799 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature