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Lara Muffley

@muffley.bsky.social

Director of Program Operations (Variant Effects Portfolio) CMAP, CAVA and the Atlas of Variant Effects Alliance | she/her 🏳️‍🌈 https://fowlerlab.gs.washington.edu/people https://brotmanbaty.org/research/atlas-of-variant-effects

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Atlas of Variant Effects Alliance@varianteffect.bsky.social · 2w ago

Get the latest research from our Virology Interest Group on our YouTube channel: youtu.be/XNu2ejij_vo?... This month's talk was given by Caroline Langley (Bonilla Lab, Rockefeller University) on how strain-specific epistasis shapes fitness landscapes of APOBEC3G antagonism by HIV-1 Vif proteins.

“Strain-specific epistasis shapes fitness landscapes of APOBEC3G antagonism by HIV-1 Vif proteins”

YouTube video by Variant Effects

youtu.be

Atlas of Variant Effects Alliance@varianteffect.bsky.social · last mo.

You can now watch the latest research presentation from AVE's Virology Interest Group: tinyurl.com/5n7t932k This month's talk was given by @williambakhache.bsky.social on the subject of genotype-by-inhibitor interactions to dissect enterovirus replication. #AtlasOfVariantEffects

“Genotype-by-inhibitor interactions to dissect enterovirus replication” - William Bakhache

William Bakhache (IBMC Institute of Molecular and Cell Biology | Mechanisms of Positive-Strand RNA Virus Laboratory (M+RVL)) “Genotype-by-inhibitor interactions to dissect enterovirus replication” - presentation to the Atlas of Variant Effects (AVE) Virology Interest Group August 07 2026

tinyurl.com

Fritz Roth@fritzroth.bsky.social · 2mo ago

🧪 One small step towards a complete atlas of human sequence variant effects, a giant leap for figuring out which variants in the Autoimmune Regulator (AIRE) protein are pathogenic, and some baby steps towards understanding the AIRE protein @varianteffect.bsky.social

The American Journal of Human Genetics@ajhgnews.bsky.social · 2mo ago

🧬Online now! 📄Systematic and proactive evaluation of AIRE missense variant effects 🧑‍🤝‍🧑 @axakova.bsky.social @fritzroth.bsky.social & co www.cell.com/ajhg/fulltex...

Atlas of Variant Effects Alliance@varianteffect.bsky.social · 2mo ago

@eag91.bsky.social was recently the 100th speaker of our Variant Effects Seminar Series. The work he presented has just been published as a preprint. Learn more: tinyurl.com/mrxf5zxv @solersanchisx.bsky.social, @cwjpugh.bsky.social, @federicobilleci.bsky.social, @jonnyfrazer.bsky.social

Blending physics-based models improves protein folding stability prediction | Ezequiel Galpern posted on the topic | LinkedIn

🎉 New preprint: “Blending physics-based and inverse folding models to disentangle variant effects on stability and function” ➡️ Read it on https://lnkd.in/dCDw-3nb Together with Xavier Soler Sanchis...

lnkd.in

Atlas of Variant Effects Alliance@varianteffect.bsky.social · 2mo ago

Some of our Virology Interest Group have authored a review of the recent technological & conceptual advances in the deep mutational scanning methods being applied to virology. Also covered are the insights these advances are yielding & suggestions for future studies. Read now: tinyurl.com/84dcy96z

Scanning the horizon: deep mutational scanning approaches in virology | Journal of Virology

The development of deep mutational scanning (DMS) approaches has led to a fundamental shift in our ability to connect viral genotype and phenotype (1) and accelerated our understanding of viral evolut...

tinyurl.com

Atlas of Variant Effects Alliance@varianteffect.bsky.social · 2mo ago

You can still register for, 'Unmasking the Genome: Integrating WGS, AI and Functional Genomics', from 1-4 September at the University of Exeter, UK. The workshop is for all researchers working at the leading edge of genomic science. 🧬 Get the programme and register now: tinyurl.com/4vbrp63p

Unmasking the Genome: Integrating WGS, AI and Functional Genomics - UK Human Functional Genomics Initiative

Unmasking the Genome is a four-day advanced workshop designed for researchers working at the leading edge of genomic science. This programme brings together expertise from Google DeepMind, leaders in ...

tinyurl.com

Jeff Calhoun@calhoujd.bsky.social · 3mo ago

Our paper on integrating data from 2+ multiplexed assays of variant effect (MAVE) for the same gene is available now: doi.org/10.1186/s130... We also have a Shiny webtool where you can plug in data and compare a few different integration approaches. A big thank you to all co-authors... 1/3

Combining multiplexed functional data to improve variant classification - Genome Medicine

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Methods We curated published datasets from five MAVEs for the gene TP53, two MAVEs for LDLR and two MAVEs for PTEN. Statistical methods (principal component analysis), unsupervised learning (k-means clustering), and supervised learning (Naïve Bayes and random forest classifiers) were used to integrate multiple MAVE datasets. The utility of MAVE integration methods were assessed using standard metrics (sensitivity, specificity, etc) as well as evidence strength in a putative variant classification framework. Results Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. In general, supervised learning methods such as random forest led to improved variant classification as compared to any individual MAVE dataset. Conclusions By following the steps outlined herein with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

doi.org

Atlas of Variant Effects Alliance@varianteffect.bsky.social · 2mo ago

The new publication by members of the Atlas of Variant Effects Clinical Variant Interpretation working group provides guidance for combining data from multiple MAVEs to strengthen the functional evidence for clinical variant classification. Read now: tinyurl.com/yn6bkhhn

Combining multiplexed functional data to improve variant classification - Genome Medicine

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant e...

link.springer.com

Atlas of Variant Effects Alliance@varianteffect.bsky.social · 3mo ago

Missed Abbye McEwan's excellent introduction to MAVE technology? It's now available on YouTube: www.youtube.com/watch?v=FeUp... Learn more about how to use MAVE data to inform clinical variant classification, and discover how these datasets may help resolve variants of uncertain significance.

VSN presents Beyond the VUS Using MAVE Data to Inform Clinical Variant Classification

YouTube video by Variant Scientists Network

youtube.com