All News and Events
All News and Events for the Atlas of Variant Effects Alliance
Latest Events
25 March 2026
Event jointly organized by: AVE and St Vincent's Institute of Medical ResearchLatest News
7 October 2026
AVE's Analysis, Modelling and Prediction (AMP) workstream has recently delivered a major expansion of one of its most-used tools: a resource cataloguing computational models for predicting variant effects. Previously covering coding variants, the resource now also covers non-coding predictors, an area of interest which is growing rapidly. We spoke to Prof. Joe Marsh (Institute of Genetics and Cancer, University of Edinburgh) about how the resource was developed, what makes non-coding predictors so different from their coding counterparts, and whether VEPs will ever replace deep mutational scanning.13 July 2026
Recently, the Variant Effect Seminar Series (VESS) marked its 100th speaker. As we look to next 100, we chat with two of its original presenters, Mafalda Dias and Jonathan Frazer. Discover how VESS has influenced their journey, their connection to the 100th speaker, Ezequiel Galpern, and what’s next for them.6 July 2026
As the Variant Effects Seminar Series marks its 100th speaker, two long-standing members of the organizing team are stepping back. We caught up with them to find out about their time on the committee and what’s coming next for them.Latest Podcast Episodes
16 October 2026
What happens when proteins stop performing their intended function? In the latest episode of the VUS Podcast, the team explore the topic of protein aggregation. Understanding how these aggregates occur can inform strategies to break them apart and may lead to better treatments for the diseases they cause. We hear about the role of deep mutational scanning in helping to understand the biochemistry behind these aggregated proteins and how they can cause disease.
10 July 2026
MAVEs are becoming mainstream. But what is driving their rise in popularity and who is behind their design? In this episode, we explain how new multiplexed assay technologies, like variant abundance by massively parallel sequencing (VAMP-seq), can measure how genetic variants impact protein abundance.
28 January 2026
How can different molecular measurements enhance our understanding of membrane proteins and the genetic diseases caused by their dysfunction? In this episode, we explain how membrane proteins relay environmental information to cells, focusing on channels that gate ions and molecules.
Latest Seminars
3 November 2026
Abbye Elizabeth McEwen, MD, PhD, is a board-certified Molecular Genetic Pathologist and functional genomics researcher based in Seattle, Washington. She completed her MD/PhD at Northwestern, and her pathology residency and Molecular Genetic Pathology fellowship at UW, where she spent four years as an attending physician on the Clinical Exome Service. Her research focuses on bringing multiplexed assay of variant effect data into clinical variant classification.
3 November 2026
Pankhuri Gupta is a Research Genetic Counselor at the University of Washington (UW) with experience in variant interpretation, long-read sequencing, and translational genomic medicine. In her current role, she leads efforts to reclassify variants of uncertain significance within the Brotman Baty Institute Clinical Variant Database, which includes clinical genetic data from UW and Fred Hutch patients. She also curates functional datasets and develops training modules on the use of functional evidence. Pankhuri, alongside her colleagues at the University of Washington...more6 October 2026
Seulki is a research scientist at the Bioinformatics and Machine Learning group at the Broad Institute of MIT and Harvard. Her research focuses on connecting genetic variant data to protein structure and function by using bioinformatics and computational biophysics methodologies. Her talk will focus on the protein feature enrichment score (PFES), a proteome-wide scoring framework that leverages protein features to mechanistically interpret missense variants and their impact on protein function.