Joe Marsh

New preprint: clear signs that variant effect predictors are now shaping ClinVar labels. Good for clinical use, but it creates circularity: predictors are trained, benchmarked and calibrated on labels that predictors helped create. www.biorxiv.org/content/10.6...

How do computational predictors compare with experimental assays (MAVEs) for variant classification? Across five hereditary cancer genes, MAVE scores were more conservative (fewer pathogenic calls) yet more decisive (fewer indeterminate calls) after ACMG/AMP calibration. doi.org/10.1186/s130...

Our AVE Variant Effect Predictors resource now covers non-coding predictors too! Big thanks to Ben Livesey for building the original and Tesni Walsh for taking on the much messier non-coding landscape. Missing a method? Send it our way 👇 www.varianteffect.org/veps/ @uoe-igc.bsky.social

New preprint from the lab! We used deep mutational scanning to measure the effects of nearly all amino acid substitutions in XPD/ERCC2. Our assay is strongly mechanism-selective, showing better discrimination between different disease phenotypes (TTD vs XP) than any computational predictors.

📣 Interested in non-coding disease-causing variants? Check out our review "Mechanisms underlying disease-causing variants in promoters and enhancers". Interesting mechanisms, challenges and future perspectives. Great to work with @wbickmor.bsky.social, Kun and Ryan! www.nature.com/articles/s41...

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...

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.

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

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/...

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...

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

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.

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

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

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!

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

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

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...

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....

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...

Great to see you Sarah!

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!

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

📣 We are advertising for a postdoc to join our team at the University of Edinburgh! Our lab studies gene regulatory mechanisms in development, and how genetic changes may impact these processes to alter development and shape human craniofacial form and function 🧬🧪

Check out our new preprint and Google Colab notebook if you are interested in predicting the molecular mechanisms of missense disease phenotypes. We find that gain-of-function and dominant-negative effects are very common, and that many disease genes are associated with multiple distinct mechanisms

New review out! 🧬🔬 We explore how protein structure is improving variant effect prediction. Advances in structural modelling, including AlphaFold, are powering methods like AlphaMissense, PrimateAI-3D, and CPT-1, leading to better accuracy and interpretability. www.sciencedirect.com/science/arti...

Prof Joe Marsh and I have a PhD project as part of the 2025 Edinburgh Doctoral College Scholarship: "Integrating AI, Biophysical Modelling and Experimental Validation for Enhancer Variant Interpretation". Closing date for applications is 25 April 2025. Please get in touch if you are interested! 🧬🧠

Mapping variant effects for a healthier future! Explore our 2024 #AnnualReport. From membership growth to global collaborations, discover how we're advancing genomics and making a difference #VariantEffectMapping www.varianteffect.org/annual-reports

Our next monthly event will focus on Variants with two exciting speakers from @edinburgh-uni.bsky.social @ailithewing.bsky.social and @jmarshlab.bsky.social 🗓️ February 17th, 3-5pm 🏠 237B, University of Glasgow Advanced Research Centre #compbio #variants

Happy to see our predictive scores integrated into DECIPHER! We hope they will help clinicians uncover the molecular mechanisms driving dominant disease. Huge thanks to the team at @deciphergenomics.bsky.social for their support. A follow-up study is underway to improve predictions—stay tuned!

Protein predictive scores which predict the likelihood that the protein is associated with a dominant-negative, gain-of-function or loss-of-function mechanism are displayed. Curated literature support for a molecular disease mechanism is also shown jmarshlab.bsky.social @mbadonyi.bsky.social