Changes in biological pathways provide essential clues about metabolism. Genome-scale metabolic Models (GEM) are network-based templates that computationally describe all stoichiometric associations and gene-protein reaction (GPR) relations found in an organism for all its metabolic genes and metabolites. Using reaction stoichiometry as input, GEMs mathematically simulate metabolic reaction fluxes occurring in an organism and predict changes in the metabolic system under the relevant condition. Multiple tools and approaches in the literature can capture fluxes sensitive to a given condition by using GEMs. However, functional enrichment analysis of these reaction lists in a systems biology perspective is not straightforward. Here, we introduce RSEA to annotate given reaction sets to significantly related metabolic pathways: Reaction Set Enrichment Analysis web server tool. RSEA converts given reaction list derived from GEMs into proper reaction identifiers and statistically analyze its enrichment in metabolic pathways. RSEA is designed to provide researchers with a practical and user-friendly platform to explore and interpret sets of reactions in biological pathways and freely available online (https://rseatool.com/).

Next generation sequencing technology has revolutionized all fields of living systems and its applications almost reinvented some research areas including metagenomics. The microbiotas in our body, including those of the oral, nasal, ocular, alveolar, skin regions and particularly gut microbiota, have close relations with our health status. Metagenomics enables medical research to uncover those links. Maturation of experimental techniques for metagenomics has been followed by the others for meta-transcriptomics, meta-proteomics and all the possible counterparts of multi-omics studies. Now we are on the eve of a meta-multiomics era for the analysis of human holobiome in medical researches. This era will help to illuminate the relationship between human holobiome and all medical conditions including not only cancer, infectious diseases, auto-immune diseases, obesity, aging and genetic disorders, but also psychiatric conditions.