@jmarshlab

Computational biologist at @MRC_HGU, @EdinUni_IGC, @EdinburghUni

 Edinburgh, Scotland
ed.ac.uk/mrc-human-genetics-unit/research/marsh-group
Joined November 2015
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When predictors and assays disagree about a variant, it's not always clear which to trust, so Ben unpicks the reasons in this new paper.
Ben Livesey@BenLivesey6Sep 30
In our new Nature Communications article, we identify and discuss factors that can lead to discordance between computational variant effect predictors and functional assays. nature.com/articles/s41467-0…

Why variant effect predictors and multiplexed assays agree and disagree

Nature Communications - Multiplexed assays of variant effect (MAVEs) and computational variant effect predictors (VEPs) are two key tools for the interpretation of genetic variants. Here the...

nature.com
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New preprint! We used deep mutational scanning to measure the effects of amino acid substitutions in ERCC2. Our assay is strongly mechanism-selective, showing better discrimination between different disease phenotypes than any computational predictors. biorxiv.org/content/10.64898…

Mechanism-selective deep mutational scanning distinguishes ERCC2 disease phenotypes

Pathogenic ERCC2 variants cause xeroderma pigmentosum (XP), trichothiodystrophy (TTD) or both, yet variant effect scores are usually interpreted only as measures of pathogenicity rather than of which...

biorxiv.org
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Happy to see this out, check out our paper here: nature.com/articles/s41467-0…

Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic...

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...

nature.com
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Out today in PLOS Comp Biol: journals.plos.org/ploscompbi… 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
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Genetic variants🧬 can cause protein-coding mutations resulting in disease. What about in alternative isoforms? Excited to share my 1st first-author pre-print! Here we show many variants reside in tissue-specific isoforms, causing coding mutations. A🧵 1/ biorxiv.org/content/10.1101/…

Disease-associated genetic variants can cause mutations in tissue-specific protein isoforms

Genetic variants can cause protein-coding mutations that result in disease. Variants are typically interpreted using the reference transcript for a gene. However, most human multi-exon genes encode...

biorxiv.org
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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 @varianteffects Open, interpretable, and clinically useful VEPs are the goal. 📄 doi.org/10.1186/s13059-025-0…

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...

link.springer.com
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New review out with @L_Geras @teichlab ! 🧬🔬 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. Read more: sciencedirect.com/science/ar…
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We are hiring a new postdoctoral bioinformatician! Come and work with us on multidisciplinary cancer genomics projects at the Institute of Genetics and Cancer in Edinburgh: elxw.fa.em3.oraclecloud.com/… institute-genetics-cancer.ed… semplelab.com
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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 @BdonyiMihly
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Are you interested in AI and structural biology, genetics and the non-coding (aka “dark”) genome? A PhD studentship through the UKRI AI and biomedical innovation programme at @EdinburghUni @EdinUni_IGC has become available with myself and prof @jmarshlab
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Excited to share our new paper led by PhD student Diego Chillón Pino with @BdonyiMihly and @DrColinSemple
Cell Reports@CellReports23 Oct 2024
Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes dlvr.it/TFfB16

Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver...

Chillón-Pino et al. investigate the structural context of cancer mutations across thousands of human proteins, revealing distinct patterns in tumor suppressors and oncogenes. Their study identifies...

cell.com
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and thanks to @ListerInstitute whose support allowed me to recruit Diego!
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Very happy to see this work is now published and we hope our model will be useful for people
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