Joe Marsh

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

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

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...

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 ...

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.

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...

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...

You can try out the Colab notebook and the R package here: https://github.com/badonyi/acmgscaler

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 👇

YouTube video by Variant Effects Introduction to Deep Mutational Scanning (Animation)

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...

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...

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....

In contrast to suggestions that DMS-based benchmarks might not reflect clinical utility, we demonstrate a striking correspondence between VEP performance in functional assays and clinical variant classification.

Explore the full paper for insights into top-performing VEPs.

Traditional benchmarks often face circularity issues, inflating performance estimates. In this study, led by Ben Livesey, we use deep mutational scanning (DMS) datasets from 36 human proteins to benchmark 97 VEPs, introducing a novel pairwise comparison method for fairer rankings.

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...

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...

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...

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