Table 4.

Best practices for IBD omic data generation and interpretation. Many decision points are influenced by clinical feasibility and translational application potential.

Phase of research	Decision points	Recommendations	Examples
Cohort design and sample collection	Inclusion / exclusion criterion	• Fine tune based on research question and record the clinical data • Think about the control population/samples. • Consider enriching clinical data with environmental data.	• Include or exclude inflamed/non-inflamed samples, treatment-naïve patients, postoperative setting etc • Should it be healthy controls, patients with other IMIDs, or non-diseased sites from the same IBD patients? • Smoking status, dietary factors, BMI, exercise etc
	Sampling dynamics	• Using low-throughput biomarkers, determine the relationship between flares and patterns in high-throughout -omic datasets measured from longitudinal cohorts. • Generate the same set of -omic datasets at multiple time-points from the corresponding samples (subject to technical limitations, available biomaterial and any possible time-lags between the -omic datasets) to enable sample-to-sample comparability. • Incorporate additional measurements after starting a new treatment	• Markers like fecal calprotectin and CRP • For example, host transcriptomics + microbial metatranscriptomics from biopsies at different time-points over treatment course or disease course. • For example, pharmacodynamics
	Sampling site	• Sample from the primary disease site but consider paired samples from other sites.	• Primary disease site (ileum, colon etc), other sites (such as PBMCs)
	Sampling for |microbiota	• Microbiota profiles from primary disease site generally preferred over faecal samples. Nonetheless, the latter might prove to be valuable given the ease of sampling. • Additional host and microbial community based omics datasets can bridge the environmental and host aspects.	• Primary disease site - luminal contents (from biopsies directly) • Metabolomics, metabonomics, metatranscriptomics, metaproteomics, viromics, mycobiomics etc
	Sampling mass	• Pre-determine the number of samples (or biopsies) and/or amount of biomass required to generate the different -omic datasets from the same sample.	• Depends on the specification of the kit/ in-house protocols used for the extraction of the different molecular fractions
	Sample treatment	• Follow standardized protocols for sampling, processing and storage specific for each -omic dataset where applicable.	• Kit-based or in-house protocols
	Sample storage and documentation	• Use of registered biobanks recommended • Samples to be systematically indexed and barcoded during storage.	• UK Biobank • -
Data generation	Cellular resolution (bulk or single cell or purified cell-types/ fractions)	• Measurements from single cell technologies highly recommended.	• -
	Sequencing strategy (short or long read sequencing)	• Sequencing type. Budget, biological question(s) etc dictate the choice	• Long-read or short-read sequencing?
	-Omic data type (genomics, transcriptomics, proteomics etc)	• Dependent on biological question and various other factors like budget, research question, clinical and translational feasibility. • Include novel molecules for -omic measurements.	• For example, bulk transcriptomics provide a relatively cheap option for high-throughput genome-wide profiling of biological state. However, proteomics is closer to phenotype than transcriptomics but lags behind on coverage. • Circular RNAs, long non-coding RNA
	Microbiota specific data resolution (16S or WGS)	• 16S for preliminary studies • WGS preferred for making in depth assessments due to its advantage of being able to make strain-level inferences	• - • -

IMID, Immune Mediated Inflammatory Disease; PBMC, Peripheral Blood Mononuclear Cells; BMI , Body Mass Index; CRP, C-reactive protein; WGS, Whole Genome Sequencing.