A knowledge driven mutual informationbased analytical framework for the identification of rumen metabolites Metabolites are the final product of biochemical reactions in the rumen microecological 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 nontargeted metabolomics is timeconsuming 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 informationbased 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 informationbased framework could very efficiently target metabolites that may correspond to integrals from NMR spectra.

A Phylogenyaware Feature Ranking for Classification of Cattle Rumen Microbiome Metagenomics is proliferating for studying environmental microbial communities and their role in animal functions. This paper aims to study the role of functions of microbial communities present in cattle (Bos taurus) and their relation to dietary supplement usage. The functional study was conducted as part of the EU H2020 MetaPlat project11MetaPlat, http:www.metaplat.eu. In this research, we proposed a novel phylogenydriven approach to classify 16S rRNA samples from cattle rumen microbiome and relate them to the functional phenotype of diet (referred to as functional analysis). Phylogeny covers biological relationships from different taxonomical levels combined with their respective evolutionary measures. We performed this analysis by proposing a novel method based on phylogenyadjusted distancebased indices. These indices are used in ranking microbial feature space derived from the topology of the phylogenetic tree. The integrative approach incorporating phylogeny into feature engineering as part of machine learning (ML) modeling, achieved high predictive performance with Accuracy of 0.962 and Kappa of 0.950 for classifying cattle microbiome into the phenotype of a diet supplemented with oil, nitrate, combined (with oil and nitrate) and controls.