COMO：代谢建模与药物发现中的多组学数据整合流程
COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery
AI总结：
COMO是一个用户友好的多组学数据整合流程，用于代谢建模和药物发现，通过构建情境特异性模型预测药物靶点，并在案例研究中为两种自身免疫疾病识别出多个靶点。
摘要：
利用代谢建模识别潜在药物靶点需要整合多种建模方法和异质性生物数据集，在没有复杂工具的情况下这可能具有挑战性。我们开发了COMO，一个用户友好的流程，整合了多组学数据处理、情境特异性代谢模型开发、模拟、药物数据库和疾病数据，以辅助药物发现。COMO可作为Docker镜像安装，并在Jupyter Lab环境中提供直观的说明。它为批量及单细胞RNA-seq、微阵列和蛋白质组学的多组学整合提供了全面解决方案，以开发情境特异性代谢模型。利用公共数据库、开源的模型构建解决方案以及预测可再利用药物的简化方法，COMO使研究人员能够探索低成本替代方案和新型疾病治疗。作为案例研究，我们使用该流程构建了B细胞的代谢模型，通过模拟和分析，分别预测了类风湿关节炎和系统性红斑狼疮的25个和23个代谢药物靶点。COMO可用于构建任何细胞或组织类型的模型，并识别针对任何人类疾病的药物。该流程有潜力通过提供高置信度靶点用于临床前和临床研究，以成本有效的方式改善全球社区的健康状况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
