TABLE 7.

Toolbox: how to validate the functions of circRNAs

Sample preparation	Bioinformatics tools	Clinical indicators	Experimental validation
Sample collection and preservation Collect clinical features Apply liquid biopsy technology Decrease sampling time RNA extraction and selection Assess RNA integrity Correct for sample‐to‐sample variations Deplete ribosomal RNA Deplete linear RNAs with RNase R treatment Deplete Poly(A)+ RNA Retain linear and circular RNAs without RNase R treatment Library preparation Estimate the false discovery rate Select appropriate thresholds for high‐confidence circRNA detection Test for biochemical artefacts Conduct normalization procedures Standardize tissue sample banks	CircRNA identification tools DCC* ACFS find_circ CircRNA annotation database CircBase* CircBank deepBase2.0 CircFunBase CircNet circAtlas Tools for circRNA‐disease associations CircR2Disease v2.0* circRNADisease Circ2Traits Tools for circRNA network prediction starBase v2.0* miRanda* TargetScan* miRBase CircInteractome Visualization Cytoscape* Bioinformatics	Probing depth (PD) Clinical attachment loss (CAL) Plaque index (PI) Bleeding index (BI) Gingival index (GI) Bleeding on probing (BOP)	Detection of expression profiling Next‐generation sequencing Microarray Single cell sequencing Experimental validation in vitro Expression level detection RT‐qPCR Sanger sequencing Northern blot Denaturing agarose gel electrophoresis Functional experiments RIP FISH ChIP Western blot Dual‐luciferase reporter RNA pull‐down Loss and gain of function model Experimental validation in vivo Animal model, e.g., murine model