Oliver M. Crook

The paper uses subcellular proteomics to identify disease-associated protein localization shifts in Alzheimer's disease across the resilience spectrum, revealing insights beyond abundance-based proteomics.

The paper introduces SANDLE, a method for analyzing subcellular omics data that reduces false positives and analysis time while handling complex experimental designs.

This paper evaluates the biosecurity risks of generative genome design models by quantifying evolutionary novelty and design efficiency in bacteriophage sequences generated by Evo 2.

The paper introduces ValDX, a framework to validate and quantify uncertainty in HDX-MS data integration with protein structural ensembles, showing it outperforms conventional metrics in distinguishing correct from incorrect models.

This paper introduces a semi-supervised Bayesian method to integrate spatial proteomics data with other omic datasets to improve protein subcellular localization inference.

ReX infers residue-level hydrogen deuterium exchange from HDX-MS data, showing improved performance in predicting unseen data and quantifying conformational changes in proteins.

This paper introduces Decode-gLM, a toolkit using sparse autoencoders to interpret, audit, and steer genomic language models, revealing biological features and identifying data contamination.

This study examines how scoring context and training data influence protein design models' preferences across species, showing that backbone-conditioned models favor archaeal and thermophilic proteins while sequence-only models favor eukaryotic proteins.

The paper introduces a structural false discovery rate (sFDR) method to improve statistical power in HDX-MS by accounting for structural dependencies in peptide comparisons.

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

The paper uses global subcellular proteomics to identify subcellular-specific anti-ferroptotic responses in A549 cells exposed to ionizing radiation, highlighting potential mechanisms of radio-resistance.

This paper develops an empirical Bayes functional model for HDX-MS data to improve statistical power and reduce false positives by incorporating a physical model of hydrogen deuterium exchange.

The paper evaluates the impact of prior Orbitrap software versions on low isobaric mass tag intensities and finds minimal detrimental effects on differential abundance experiments.

This paper introduces a Bayesian semi-parametric model to analyze thermal proteome profiling data, improving sensitivity and identifying new drug-protein associations.

The paper introduces BANDLE, a Bayesian method to infer differential subcellular localisation from spatial proteomics data, showing improved accuracy and novel biological insights.

This paper introduces a semi-supervised Bayesian method for protein sub-cellular localisation and novelty detection, identifying new compartments in proteomic data.

This paper introduces a Bayesian mixture model for spatial proteomics to probabilistically assign proteins to sub-cellular locations and quantify uncertainty in classification.

The paper introduces LOPIT-DC, a simplified spatial proteomics method, and compares it with hyperLOPIT, highlighting suborganellar resolution and isoform-specific niches in human cells.