Joe Marsh
@jmarshlab.bsky.social
72 followers 79 following 13 posts
Posts Media Videos Starter Packs
Reposted by Joe Marsh
mbadonyi.bsky.social
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...
jmarshlab.bsky.social
Happy to see this out, check out our paper here: www.nature.com/articles/s41...
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
jmarshlab.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
Reposted by Joe Marsh
wbickmor.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...
Reposted by Joe Marsh
andrewwood.bsky.social
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...
www.biorxiv.org
Reposted by Joe Marsh
mbadonyi.bsky.social
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
You can try out the Colab notebook and the R package here: https://github.com/badonyi/acmgscaler
jmarshlab.bsky.social
Excited to share this new method for gene-level calibration of MAVE and VEP scores that Mihaly has been working so hard on!
mbadonyi.bsky.social
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.
biorxiv-bioinfo.bsky.social
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
Reposted by Joe Marsh
biorxiv-bioinfo.bsky.social
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
Reposted by Joe Marsh
jmarshlab.bsky.social
Very excited to see our recent preprint covered here! @mbadonyi.bsky.social
dereklowe.bsky.social
So, how many genetic diseases come down to good ol’ loss-of-function in the targeted protein?

Your estimate is probably too high:
Mutant Proteins Classified
www.science.org
Reposted by Joe Marsh
Reposted by Joe Marsh
varianteffect.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...
Introduction to Deep Mutational Scanning (Animation)
YouTube video by Variant Effects
www.youtube.com
jmarshlab.bsky.social
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.
jmarshlab.bsky.social
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.
jmarshlab.bsky.social
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
jmarshlab.bsky.social
Great to see you Sarah!
teichlab.bsky.social
With TeichLab alumni Dr Jing Su and Prof Joe Marsh in lovely Edinburgh at "hidden cell, dark genome conference"
Reposted by Joe Marsh
medrxivpreprint.bsky.social
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
jmarshlab.bsky.social
Had a good time discussing variant effect predictors on this podcast, thanks for having me!
varianteffect.bsky.social
🆕 🎧 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
Reposted by Joe Marsh
uoe-igc.bsky.social
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