Abstract Holo-omics leverages omics datasets to explore the interactions between hosts and their associated microbiomes. Although the generation of omics data from matching host and microbiome samples is steadily increasing, there remains a scarcity of computational tools capable of integrating and visualizing this data to facilitate the prediction and interpretation of host-microbiome interactions. We present OmniCorr, an R package designed to: (1) manage the complexity of omics data by clustering co-varying features (e.g., genes, proteins and metabolites) into modules, (2) visualize correlations of these modules across different omics layers, host-microbiome interfaces, and metadata, and (3) identify statistically significant associations indicative of putative host-microbiome interactions. OmniCorr’s utility is demonstrated using datasets from two systems: (i) Atlantic salmon, integrating host transcriptomics with metagenomics and metatranscriptomics to explore dietary impacts, and (ii) cattle, combining host proteomics with metaproteomics to investigate methane emission variability. Availability and implementation OmniCorr is freely available at https://github.com/shashank-KU/OmniCorr.
Gupta et al. (Fri,) studied this question.