The Hypothesis-Driven Loop: An Integrated Dry-Wet Framework for Long Noncoding RNA Functional Analysis

Only a small minority of annotated long noncoding RNAs (lncRNAs) have a molecular function established by direct experiment, and the gap widens with every new catalog. Existing reviews catalog methods thoroughly but rarely formalize how negative or discordant wet-laboratory results should feed back into computational prioritization, so the iterative refinement that lncRNA biology requires is left implicit. We set out the Hypothesis-Driven Loop, which recasts lncRNA functional analysis as a cycle of three phases: Generation (computational priors), Testing (wet validation), and Refinement (integration that updates the priors for the next round). The cycle itself is Platt's strong inference; what is specific to lncRNAs are the constraints that reshape it-a detectability floor that gates every functional claim, an orders-of-magnitude asymmetry between prediction and validation throughput, and the ambiguity of what a locus-level perturbation actually removes-together with the deliverable each phase must produce. We map existing tools onto the appropriate phase; give a one-page Loop worksheet with an odds-form procedure for updating priors and pre-declared stopping boundaries; and specify how a negative result is triaged as technical or biological before it may revise a hypothesis. Three worked programs-BRIC-seq and the SLiTs paradigm from our laboratory, and HOTAIR, assembled by independent groups from results that disagreed-show that Loops can be entered at different phases and that closure can be a community-level property. The claim is deliberately narrow: iterative dry-wet coupling, recorded so that it can be audited, is one discipline that may help narrow the annotation-function gap.