Fengchao Yu

The paper presents a comprehensive spatial proteome map of human tissues and cancers, enabling drug response analysis and target discovery.

The paper introduces MIRA-MS, a real-time AI-guided acquisition method that improves quantification of non-canonical HLA peptides in clear cell renal cell carcinoma.

The paper introduces TMT-Integrator, a tool for analyzing isobaric proteomic data, showing improved performance in protein and phosphorylation site quantification compared to existing tools.

The paper introduces a modular pipeline for neoantigen discovery, verification, and prioritization in cancer research using immunopeptidogenomic data.

This paper introduces MBG, a method to expand glycopeptide identification in glycoproteomics by leveraging MS1 signals from displaced glycopeptides, improving detection of low-abundant and modified glycans.

pmultiqc generates metadata-aware QC reports for proteomics data using standardized metrics and integrates with existing workflows and cloud services.

This paper develops a high-throughput single-cell proteomic method for PBMCs, achieving 660 cells per day with 750 protein identifications per cell and identifying low-frequency cell types.

The paper introduces a new detailed mass offset search method in MSFragger to improve the interpretation of complex post-translational modifications.

MSFragger-DDA+ improves peptide identification sensitivity by searching the full isolation window and handling co-fragmented peptides, enhancing proteomics data analysis.

PepCentric enables fast proteogenomics searches across large mass spectrometry datasets by using 2-D fragment indexing to validate novel peptides and proteomic variations.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data by generating pseudo-MS/MS spectra for direct peptide identification and quantification.

This study benchmarks label-free and TMT-MS2 methods for spatial proteome mapping, showing label-free provides deeper coverage and higher dynamic range, while TMT-MS2 offers faster throughput.

FragPipe-Analyst provides an R shiny web server and R package for downstream analysis and visualization of quantitative proteomics data from FragPipe.

Koina provides an open-source, accessible ML/DL prediction service for proteomics, improving integration with existing tools like FragPipe.

MSFragger-Labile improves the analysis of labile post-translational modifications by incorporating modification-specific fragment ions into mass spectrometry searches.

The paper introduces MSFragger-DIA, a tool for fast and sensitive peptide identification from DIA proteomics data, integrated into FragPipe, and demonstrates its performance across various sample types.

MSBooster improves peptide identification rates by integrating deep learning-based features into existing workflows with MSFragger and Percolator.

The paper develops a method to automatically detect diagnostic spectral features for post-translational modifications in mass spectrometry data, improving PTM identification and localization rates.

The paper introduces a pipeline called CHAMPION that uses qXL-MS and MONET modulomics to identify nuclear protein interactions and modules, revealing novel interactors and hormone-specific variants.

The paper demonstrates improved proteomic depth using DIA-NN and FragPipe for dia-PASEF data, achieving higher sensitivity and quantification in complex samples.

The paper introduces a method to accurately identify glycan compositions in glycopeptide spectra and control false discovery rates, integrating it into a computational pipeline for glycoproteomics analysis.

IonQuant improves label-free proteomic quantification by implementing FDR-controlled match-between-runs, achieving faster speed and higher protein quantification than MaxQuant.

PTM-Shepherd automates analysis of post-translational modifications from open search proteomics data, improving detection of artifacts and biases across experiments.

MSFragger-Glyco improves glycopeptide identification in N- and O-glycoproteomics data, increasing glycoPSMs and revealing new glycan trends.

The paper introduces MSFragger and IonQuant for fast analysis of timsTOF PASEF data, showing improved peptide identification and quantification compared to existing tools.

The paper evaluates open search methods for identifying modified peptides through simulations and proposes an analytical model to quantify performance based on spectrum quality and PTM count.

Xolik identifies cross-linked peptides efficiently by reducing computational complexity and outperforms existing tools in speed and statistical power.

ECL 2.0 improves peptide-peptide pair identification by achieving linear complexity and better sensitivity compared to existing tools.