Table 2.

Overview of meta-omics methods for the characterization of the gut microbiota and microbiota-directed biotherapeutics.

-Omics	Input	Sample multiplexing	Depth	Readouts	Limitations	Advantages
Metataxonomics	16s rDNA 18S rDNA ITS	High	High	Taxonomy (genus or higher level)	Low resolution Bias due to primers selection No functional information	Low cost Well established bioinformatics tools Targeted detection of rare disease-associated microbial species
Metagenomics	Genomic DNA	Low	High	Taxonomy (species/strain level) Gene abundance Function/pathway potential	High sequencing depth is needed Can’t discern expressed and not expressed functions	High-resolution taxonomics (strain level) Enable genome-level analysis, such as genome-scale metabolic reconstruction Detection of specific genes and pathways
Metatranscriptomics	mRNA, can extend to other RNAs (cDNA)	Low	High	Taxonomy (species/strain level) Transcript abundance Active function/pathway	High sequencing depth is needed RNA instability Complicated sample preparation protocol Complicated bioinformatics workflow	High-resolution taxonomics (strain level) Detection of functional activity of microbiomes
Metaproteomics	Proteins/peptides	Low (up to 18-plex)	Low	Taxonomy (species/strain level) Protein abundance PTMs Biomass Host proteins	Low measurement depth Complicated bioinformatics workflow High dynamic range of protein abundances	Detection of functional activity Detections of protein isoforms (e.g., PTMs) and protein-protein interactions Detection of proteins derived from the host (as host biomarkers) Enable absolute biomass estimates
Metabolomics	Metabolites, including lipids	Low or None	Low	Metabolite concentrations (host and microbial origin)	Mixture of host and microbe metabolites Insufficient identification of metabolites Low measurement depth and usually need different data acquisition modes	Direct measurement of key metabolites Easy data interpretation