In clinical variant interpretation, we still tend to default to the canonical transcript when assessing the effect of a variant. While this simplifies workflows, it risks missing biologically relevant consequences on non-canonical but functionally important isoforms. This is especially true in genes with tissue-specific expression or developmental regulation. I’ve been trying to push our lab to adopt a more isoform-aware review process, especially for cases involving neurodevelopmental disorders and rare diseases. But the challenge, as always, is time and clarity — most tools don't make it easy to quickly compare multiple transcripts or identify which ones are relevant in specific contexts. Has anyone here successfully shifted toward a more transcript-sensitive interpretation model in routine practice? If so, how did you build it into your workflow, and are there tools you’d recommend for helping non-computational colleagues engage with the data more easily?
top of page
To see this working, head to your live site.
Incorporating isoform-aware interpretation into standard genetic workflows
Incorporating isoform-aware interpretation into standard genetic workflows
3 comments
3 Comments
bottom of page
Tap Road is an endless runner game designed in a modern neon style, where players control a ball that continuously rolls on an ever-expanding route.
We’ve faced the same issue, and I totally agree — relying solely on the canonical transcript can really limit our understanding of variant impact, especially in genes with a complex transcript landscape. To address this issue, the company has started using a web-based tool that makes it much easier to assess isoform-level effects: https://compassbioinfo.com/. What makes it so valuable is how it visualizes each isoform in parallel, showing which exons are affected, whether UTRs or coding sequences are involved, and how that compares across transcripts. It really speeds up team discussions and lets us focus on transcripts that are biologically plausible given the phenotype or expression context. Since it’s browser-based, even our clinical collaborators can follow along without needing command-line experience. This has helped us move toward more nuanced, evidence-backed interpretations — without slowing down our review pipeline.
That’s really helpful to hear. I think what’s often missing in these workflows is that middle ground — a way to go beyond basic annotation without jumping into heavy analysis environments like IGV or custom scripts. Visualization plays a huge role in that, especially when multiple team members need to weigh in. Instead of assuming one "default" transcript, the group is more open to considering functional variants in alternative transcripts — especially when there's supporting data like conservation or known expression patterns. It also helps frame questions for further testing, such as isoform-specific qPCR or RNA-seq validation. Moving forward, I think tools like this will become essential in standard workflows, not just for advanced users, but for anyone trying to get a full picture of variant impact.