MOTA allows integrative analysis of multi-omics data for identification of disease-associated metabolites. An integrated differential network is built by merging the rewiring of intra-omics and inter-omics interactions based on differential partial correlation and differential canonical correlation, respectively, in disease vs. control groups. A MOTA activity score is calculated for each metabolite considering the changes in its levels and pairwise rewiring as a result of both intra-omics and inter-omics interactions.
MetFID ranks putative metabolite IDs using an artificial neural network (ANN) that is trained to predict molecular fingerprints based on experimental MS/MS data. The method is particularly useful to rank putative IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.
MetID is a Bioconductor R-package for network-based ranking of putative metabolite IDs. It allows users to upload putative IDs and apply Bayesian approach with pathway and network information to rank putative IDs of metabolites. Metabolic pathway and network information are incorporated into the Bayesian framework to assign scores to putative IDs. The assumption is that a putative ID is more likely if other metabolites that are connected or are in close proximity to it are also detected in the same study. The formation of a dense cluster confirms that the analysis is inclined to assign higher probability scores to putative IDs that interact with others.
INDEED is a Bioconductor R-package that identifies biomolecules with significant changes on both individual and pairwise interaction levels. This is accomplished by using differential correlation to construct a differential network whose edges represent significant rewiring interactions between metabolite pairs. Each node represents a biomolecules whose activity score is calculated by combining its node degree with statistical significance of the feature in distinguishing disease from control.