New work led by Hasan Cubuk: moving from interpreting variants individually to combining both alleles in recessive disease. DMS of >8000 ADSL variants → inferred enzyme activity → additive biallelic scores that track patient phenotypes. Great collab with the Kudla lab! www.cell.com/cell-systems...

Mechanistic modeling of recessive disease through allelic integration of variant effects

Recessive diseases arise from the combined effects of two alleles, yet most variant interpretation methods consider variants individually. Çubuk et al. map more than 8,000 variants in the recessive en...

cell.com

As we move towards a complete map of human variant effects, evaluating VEP and MAVE scores in clinically meaningful ways becomes essential. In work led by Yifei Shang and @jmarshlab.bsky.social, we explore mean evidence strength (MES) to quantify clinical utility after ACMG/AMP calibration.

bioRxivpreprint@biorxivpreprint.bsky.social · 4mo ago

Clinical evidence yield as a framework for evaluating computational predictors and multiplexed assays of variant effect https://www.biorxiv.org/content/10.64898/2026.03.27.714777v1

New preprint on how disagreement among variant effect predictors can help guide prioritization of proteins for experimental analysis Work led by Nicolas F Jonsson in a collaboration with Joe Marsh. Preprint: doi.org/10.64898/202... @vxh357.bsky.social @jmarshlab.bsky.social 1/6

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social · 5mo ago

Disagreement among variant effect predictors guides experimental prioritization of target proteins https://www.biorxiv.org/content/10.64898/2026.03.18.712765v1

Not long to go until the first @cmvm-edinburghuni.bsky.social Inaugural Lecture Showcase of 2026. Join us at IGC on 12 March at 5pm as @csemple.bsky.social and @jmarshlab.bsky.social share their career and research journeys so far. Sign up for the free event and drinks reception 👉️ edin.ac/4kJmOSN

Today in @natgenet.nature.com, we report a saturation genome editing study that systematically dissects the degron of β-Catenin, which contains 5 of the 25 most frequently mutated regions of the human cancer genome, and >70 recurrent missense mutations. rdcu.be/e1Tvk

SS18::SSX activates Polycomb target genes without BAF ❌ Instead, transcription relies on EP300 via the SS18 QPGY domain www.biorxiv.org/content/10.6... ➡️ Coactivator targeting emerges as a new therapeutic strategy in synovial sarcoma 🎯 Team work from @banitolab.bsky.social and @uoe-igc.bsky.social

Our first foray into non-coding variation: structure-guided TF-DNA modelling with AlphaFold 3. Not a replacement for sequence-based predictors, but a complementary way to reason about mechanism. Nice collab with @simonbiddie.bsky.social academic.oup.com/nar/article/...

A structure-guided approach to noncoding variant evaluation for transcription factor binding using AlphaFold 3

Abstract. Noncoding single-nucleotide variants (SNVs) that alter transcription factor (TF) binding can affect gene expression and contribute to disease. Se

academic.oup.com

Happy to share that 𝚊𝚌𝚖𝚐𝚜𝚌𝚊𝚕𝚎𝚛 is now on CRAN! 🎉 This means long-term stability and easy installation with: 𝚒𝚗𝚜𝚝𝚊𝚕𝚕.𝚙𝚊𝚌𝚔𝚊𝚐𝚎𝚜('𝚊𝚌𝚖𝚐𝚜𝚌𝚊𝚕𝚎𝚛') 🗞️ doi.org/10.1093/bioi... #rstats #acmg #varianteffect #MAVEs #VEPs #genomics

1/8 Our new paper in Nature Communications explores how often pathogenic missense variants cause disease through loss-of-function (LOF), gain-of-function (GOF), or dominant-negative (DN) effects. 📄 nature.com/articles/s41...

Institute of Genetics and Cancer@uoe-igc.bsky.social · 10mo ago

Find out more about a new online tool which allows researchers and clinicians to estimate likely disease mechanisms for sets of mutations, supporting better diagnosis and personalised medicine 👉 edin.ac/3W5ykw3 @mbadonyi.bsky.social @jmarshlab.bsky.social

Happy to see this out, check out our paper here: www.nature.com/articles/s41...

Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes - Nature Communications

Protein structures can help determine the disease-causing mechanisms of mutations. Here, the authors use a protein structure-based approach to show that nearly half of dominant genetic conditions result in non-simple loss-of-function effects.

nature.com

Institute of Genetics and Cancer@uoe-igc.bsky.social · 10mo ago

Find out more about a new online tool which allows researchers and clinicians to estimate likely disease mechanisms for sets of mutations, supporting better diagnosis and personalised medicine 👉 edin.ac/3W5ykw3 @mbadonyi.bsky.social @jmarshlab.bsky.social

New paper out today in PLOS Comp Biol: journals.plos.org/ploscompbiol... Intrinsically disordered regions make variant prediction deceptively easy for benign changes but very hard for pathogenic ones. Our work shows why current tools struggle here, and why disorder-aware approaches are needed.

Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins

Author summary Some parts of proteins, known as intrinsically disordered regions, do not fold into fixed shapes. Instead, they stay flexible and play key roles in controlling how cells work, often by ...

journals.plos.org

We’ve updated the acmgscaler manuscript following reviewer and community feedback. The R package now has a single calibrate() function, and the Colab interface is easier to use. 📄 Manuscript: www.biorxiv.org/content/10.1... 🧪 Colab: edin.ac/4mjzijp #rstats @theacmg.bsky.social

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework

A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/A...

biorxiv.org

New preprint from our group - Ben has done some great work trying to understand why computational predictors and MAVEs agree or disagree when scoring the impacts of single amino acid substitutions

Ben Livesey@bjlivesey.bsky.social · last yr.

Check out our latest preprint: "Why variant effect predictors and multiplexed assays agree and disagree". Now available on BioRxiv www.biorxiv.org/content/10.1... @marshlab.bsky.social @uoe-igc.bsky.social

GWAS to mechanism: when non-coding is coding. Beautiful insightful science from @gweykopf.bsky.social @simonbiddie.bsky.social Joe Marsh and many colleagues. @uoe-igc.bsky.social @cmvm-edinburghuni.bsky.social www.biorxiv.org/content/10.1...

Pleased to share our latest work and the first manuscript from the Degron Tagging Cluster in the MRC National Mouse Genetics Network. If you work with protein tags, particularly in tissue biology models, this should be of interest: www.biorxiv.org/content/10.1...

Thanks to #CCG2025 for the opportunity to present our work on `acmgscaler`, a standardised tool to convert functional scores into ACMG/AMP evidence strengths. #rstats

Excited to share this new method for gene-level calibration of MAVE and VEP scores that Mihaly has been working so hard on!

Mihaly Badonyi@mbadonyi.bsky.social · last yr.

We've developed a method to align genetic variant effect scores with ACMG/AMP classification criteria. It has two key advantages: (1) no assumptions about score distributions, and (2) consistent outputs without user tuning.

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework https://www.biorxiv.org/content/10.1101/2025.05.16.654507v1

We are hiring! Want to join my new group at the amazing @uoe-igc.bsky.social and perform ground-breaking studies in reproductive genomics and genomic medicine as a computational genomicist? Please DM me to discuss this, I will be attending #ESHG2025 elxw.fa.em3.oraclecloud.com/hcmUI/Candid...

Our "Atlas of Variant Effects 2030 Roadmap" is live: zenodo.org/records/1542... 1/n

Very excited to see our recent preprint covered here! @mbadonyi.bsky.social

Derek Lowe@dereklowe.bsky.social · last yr.

So, how many genetic diseases come down to good ol’ loss-of-function in the targeted protein? Your estimate is probably too high:

Read more about this study by @jmarshlab.bsky.social 👇

New guidelines aim to improve transparency and trust in genetic prediction tools

Researchers from the Institute of Genetics and Cancer have been working with the Atlas of Variant Effects Alliance to provide practical guidelines for releasing new computational tools known as varian...

institute-genetics-cancer.ed.ac.uk

Joe Marsh@jmarshlab.bsky.social · last yr.

Following our variant effect predictor (VEP) guidelines paper last week, we’re excited to announce another publication in Genome Biology today—the latest iteration of our VEP benchmarking efforts. With so many VEPs released recently, how do we choose the best ones? 🌐 doi.org/10.1186/s130...

Mutational Scanning helps guide precision medicine! But how does it work? 🤔 Check out this Introduction to Deep Mutational Scanning (Animation) @uwgenome.bsky.social www.youtube.com/watch?v=NRKj...

Introduction to Deep Mutational Scanning (Animation)

YouTube video by Variant Effects

youtube.com

The guidelines "aim to streamline VEP development, sharing, and evaluation by tackling data availability, interpretability, transparency, and circularity." Benjamin J. Livesey, @jmarshlab.bsky.social et al genomebiology.biomedcentral.com/articles/10....

Guidelines for releasing a variant effect predictor - Genome Biology

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well...

genomebiology.biomedcentral.com

Following our variant effect predictor (VEP) guidelines paper last week, we’re excited to announce another publication in Genome Biology today—the latest iteration of our VEP benchmarking efforts. With so many VEPs released recently, how do we choose the best ones? 🌐 doi.org/10.1186/s130...

Variant effect predictor correlation with functional assays is reflective of clinical classification performance - Genome Biology

Background Understanding the relationship between protein sequence and function is crucial for accurate classification of missense variants. Variant effect predictors (VEPs) play a vital role in decip...

doi.org

New paper out in Genome Biology! 🎉 We lay out best-practice guidelines for releasing variant effect predictors, developed through the Atlas of Variant Effects Alliance @varianteffect.bsky.social Open, interpretable, and clinically useful VEPs are the goal. 📄 doi.org/10.1186/s130...

Guidelines for releasing a variant effect predictor - Genome Biology

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well...

doi.org

Great to see you Sarah!

teichlab@teichlab.bsky.social · last yr.

With TeichLab alumni Dr Jing Su and Prof Joe Marsh in lovely Edinburgh at "hidden cell, dark genome conference"

Structure-informed classification of RyR1 variants highlights limitations of current predictors and enables clinical interpretation https://www.medrxiv.org/content/10.1101/2025.04.02.25325085v1

Had a good time discussing variant effect predictors on this podcast, thanks for having me!

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

🆕 🎧 Podcast Episode “Deep Thought” We can predict the weather, but can we predict genetic diseases from your genome? Learn about variant effect predictors (VEPs) with experts Drs Debbie Marks and Joe Marsh #Podcast #Science #AI #VariantEffectPredictor 🎙 www.varianteffect.org/podcast

Sign up now for the 'Enter the Dark Genome - Instructions Hidden in Plain Sight' talk by @katarney.bsky.social at @rcpedin.bsky.social on 2 April from 6-7pm, followed by a drinks reception: edin.ac/4ip4egz