In the fast-evolving field of genomics, interpreting genetic variants accurately is essential for effective patient care. As new data emerges and standards evolve, it is crucial to adapt how we collect evidence in order to classify variants and present the most impactful data to Mastermind users. At Genomenon, we take pride in aligning with these evolving standards through updates to our curation methodology—a methodology that reimagines how genetic data is evaluated, applied and presented.
This new approach not only elevates the flexibility of variant interpretation but also strengthens the accuracy of clinical decisions. This is complemented by our new user interface (UI) which brings an increased transparency to our curation and methodology. Let’s take a closer look at the driving forces behind these updates and their benefits to users.
Moving Beyond Traditional ACMG Guidelines
Since the introduction of the ACMG guidelines, numerous publications and expert groups have made recommendations for adapting the ACMG criteria to different genes and diseases. These adaptations involve modifications to the rules for applying a criteria as well as changing the strength of an applied criteria.
Recognizing the need to accommodate novel adaptations and mindful of the evolving standards, Genomenon has taken a thoughtful and adaptive approach to updating our curation processes - enhancing flexibility,increasing precision, and improving transparency.
Introducing An Updated Curation Methodology
In response to user feedback and in anticipation of changes to the ACMG sequence interpretation guidelines, we have refined our process to further drive the quality of our curated evidence.
Our refined approach ensures an even deeper understanding of variant impact by analyzing comprehensive data points beyond rigid criteria.
This includes:
- Discrete data curation - Instead of assigning ACMG criteria directly to articles our expert team of variant scientists capture discrete, quantitative and qualitative data that collectively determine a variant’s classification and provide detailed information for individual articles and at the variant level.
- Integration of REVEL - We have incorporated the REVEL model into our computational ACMG criteria. REVEL (Rare Exome Variant Ensemble Learner) utilizes an ensemble method specifically designed to predict the pathogenicity of rare missense variants. By combining scores from various predictive tools, REVEL offers a more reliable and comprehensive assessment than other models.
- Streamlined user experience - This update also transforms how evidence is displayed in Mastermind, our genomic intelligence platform. Now, each article will display additional detail like zygosity, associated disease and additional functional or clinical context. Furthermore, users can view cumulative evidence across all articles, empowering them to prioritize studies with the most valuable data.
We are continuously working to enhance transparency for users while maintaining a seamless user interface and experience. With these updates, we now provide insights into the reasoning behind the inclusion or exclusion of evidence, empowering users to make informed decisions more efficiently.
What This Means for Our Users
With these updates, users will experience several valuable improvements:
- More Precise Classifications: Aggregating data from multiple studies leads to robust variant interpretations.
- Improved Transparency: Users can access detailed patient counts and evidence summaries directly within our platform.
- Greater Flexibility: Thresholds can be tailored to specific genes or adjusted based on new insights.
- Future-Proofing: Our methodology is designed to adapt to next-generation guidelines and standards in variant classification.
- Enhanced Visibility: Users can now see all the papers we reviewed, even those that were not applicable to the final classification, ensuring greater insight into the decision-making process.
Conclusion
With the introduction of our updated curation methodology, we are not just improving how we classify variants—we are setting a new standard for precision in genomic data. By capturing detailed, discrete evidence and allowing for dynamic adjustments, we provide our users with the most accurate insights possible. This evolution reflects our commitment to continually advancing genomic interpretation for the benefit of researchers, clinicians, and ultimately, patients.
We are excited about what this new approach means for our users and look forward to hearing your feedback. Stay tuned for more updates and explore the benefits of this enhanced system in Mastermind today.
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