Samuel Payne

NIFty is a classification pipeline for single-cell proteomics that handles missing data, avoids circular analysis, and mitigates batch effects without correction.

PTMOverlay visualizes post-translational modifications across species by aligning PTM sites to protein sequences, revealing conserved modifications in bacterial central carbon metabolism.

The paper introduces PIP-ECHO, a method to control false discovery rate in peptide-identity-propagation, showing it outperforms existing tools in FDR control and differential protein abundance detection.

The paper shows that a synonymous variant in PILRA decreases mRNA and protein levels by disrupting a ramp sequence, providing a biological mechanism for its association with Alzheimer's disease.

The paper investigates how impurities in isobaric labeling reagents can cause contamination in single-cell proteomics, leading to inaccurate quantification when carrier channels are overused.

The paper identifies conserved bacterial protein methylation, showing ribosomal protein L11 methylation regulates stringent response signaling and ribosomal homeostasis.

This paper introduces a de novo metaproteomics pipeline using a deep learning model to identify soil microbiome composition and functions without relying on metagenomic data.

This study examines how peptide fragmentation spectra differ between single cell and bulk proteomics, identifying key changes that affect peptide identification.

This study develops machine learning methods to detect fabrication in large-scale molecular omics data, achieving high accuracy using digit frequency features.

This study benchmarks peptide spectrum match identification tools for single cell proteomics and finds MetaMorpheus performs best at 1% FDR, with post-processing ML improving results by up to 40%.

This study investigates proteomic changes in CLL during the asymptomatic stage using small sample sizes and highlights the need for larger control populations sampled over time.

The paper introduces HotPho, a tool that identifies 3D co-clustering of phosphosites and cancer mutations on protein structures, revealing 474 hybrid clusters with potential functional significance.

The paper introduces a Python package, cptac, that provides unified access to processed cancer proteogenomic data from the CPTAC consortium for analysis and education.

The paper calculates the sample size requirements for detecting proteome dynamics in single cell proteomics time course experiments.

An ensemble of six imputation methods, built through the NCI-CPTAC Proteogenomics DREAM Challenge, fills missing values in labeled LC-MS/MS proteomics data, helping most for low-abundance proteins.

This paper introduces a generalizable method for estimating false discovery rates in mass spectrometry-based lipidomics to enable automated data analysis.

Q4SRM is a software tool that rapidly assesses the quality of targeted proteomics experiments by monitoring stable-isotope labeled standards.

The paper introduces BSF, a fast pairwise similarity algorithm that filters unproductive comparisons to enable efficient data mining.

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.