Matthew The

TOPAS: phosphoproteome data analysis and decision support platform for molecular tumor boards

TOPAS converts phosphoproteomic data into patient-specific reports for molecular tumor boards, focusing on clinically relevant signaling and therapeutic targets.

Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

Chemical proteomics decrypts the kinases that shape the dynamic human phosphoproteome

The paper develops a method to identify and validate kinase-substrate relationships using phosphoproteomics data and applies it to estimate kinase activity in cancer biopsies.

Illuminating oncogenic KRAS signaling by multi-dimensional chemical proteomics

The paper uses chemical proteomics to analyze KRAS inhibitor effects on signaling networks in cancer cells, revealing cell-specific responses and phosphorylation changes.

Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response

This study identifies ATR inhibitors as synergistic with gemcitabine in killing PDAC cells by blocking the DNA damage response pathway.

PTMNavigator: Interactive Visualization of Differentially Regulated Post-Translational Modifications in Cellular Signaling Pathways

PTMNavigator visualizes differentially regulated PTMs in cellular pathways using phosphoproteomics data to enhance pathway enrichment analysis and drug response visualization.

Towards routine proteome profiling of FFPE tissue: Insights from a 1,200 case pan-cancer study

The paper profiles proteomes from 1,220 FFPE tumors across six cancers, identifies a new normalization method, and reports a comprehensive pan-cancer proteome resource.

CurveCurator: A recalibrated F-statistic to assess, classify, and explore significance of dose-response curves

CurveCurator assesses and classifies dose-response curve significance using a recalibrated F-statistic and provides p-values and false discovery rates for improved analysis.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

The paper provides a comprehensive proteomic map of 13 human brain regions using high-throughput mass spectrometry, highlighting region-specific protein expression patterns and markers.

Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler

The paper introduces Triqler, a Bayesian model that integrates quantification and identification uncertainties for differential protein abundance analysis in shotgun proteomics.

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

The paper introduces Quandenser, a method for shotgun proteomics that clusters quantification data to improve differential expression analysis by reducing noise and increasing sensitivity.

Integrated identification and quantification error probabilities for shotgun proteomics

The paper introduces Triqler, a probabilistic graphical model for shotgun proteomics that controls FDR and improves sensitivity in protein quantification.

A protein standard that emulates homology for the characterization of protein inference algorithms

The paper introduces a protein standard with shared peptides to benchmark protein inference algorithms, showing that methods excluding shared peptides provide more accurate error estimates.