MInfer: Bridging MetaboAnalyst and Jacobian analysis for metabolomic networks

Abstract

Background and Objective

Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks.

Methods

MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways.

Results

The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations.

Conclusions

MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.