A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites

Metabolites are the final product of biochemical reactions in the rumen micro-ecological system and very sensitive to changes of microbial genes. However, limited by the spectra library and the computational techniques of structure identification, the identification of metabolites from non-targeted metabolomics is time-consuming and inefficient. The absence of specific information about metabolites makes the biological interpretation of the quantitative analysis of metabolomics meaningless. Based on the nonlinear association between microbial genes and metabolites, combined with knowledge of metabolic pathways from the KEGG database, this study developed a knowledge driven mutual information-based analytical framework for identifying metabolites associated with integrals derived from NMR analysis results. In this study, one known metabolite and three sets of integrals with unknow metabolites were identified within the novel framework. The results showed that this mutual information-based framework could very efficiently target metabolites that may correspond to integrals from NMR spectra.