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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 · last wk.

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 · 2w 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 · 4w 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 · 3w 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 · last mo.

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

Lara Muffley@muffley.bsky.social · 3mo ago

Happening now! Join us live! Info on how to join is here ➡️ www.varianteffect.org/seminar-seri... @tkschulze.bsky.social @jeromics.bsky.social

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

Tomorrow, our Variant Effect Seminar speakers will be covering various aspects of the modeling of variant effects. They hope you can join them! Details here: www.varianteffect.org/seminar-seri... #Genomics #VariantInterpretation #DeepMutationalScanning #AtlasOfVariantEffects