Navigating deep intronic variants in clinical research
As deep sequencing becomes more commonplace in clinical research, we’re identifying an increasing number of variants located deep within introns — often far from canonical splice sites. These “deep intronic” variants are difficult to interpret, yet some of them clearly disrupt splicing or affect regulatory elements. The challenge is figuring out which ones deserve follow-up. For this reason, I’ve been relying more on visual tools like Compass Bioinformatics to explore intronic regions with greater resolution. It helps me see conservation, alternative splice junctions, and transcript structures that might be impacted. Just having this spatial awareness — seeing how close a variant is to cryptic exons or regulatory motifs — changes how I prioritize them. I’d love to know how others are approaching this issue. Are you using splicing prediction tools alone, or combining them with visualization and manual curation?
Great points from both of you. One thing I’d add is that deep intronic variants also raise educational challenges — not all collaborators or clinicians immediately grasp the functional significance of these regions. Having clear visual context helps communicate the biological plausibility of an effect. For instance, when we present a case involving a cryptic exon, showing it graphically using Compass Bioinformatics makes the finding tangible, even to non-specialists. We’ve also begun integrating such tools into our variant board reviews to support joint decision-making. It bridges the gap between computational predictions and practical hypotheses that can be tested in vitro. With more focus turning toward non-coding space, I think deep intronic variant review will soon be a standard — not an edge case — and having intuitive tools will be essential to scaling interpretation efficiently.