Advanced Proteomics Techniques and Applications

The paper introduces a new DIA scan mode that improves peptide precursor ion sampling and identification specificity using ion mobility, achieving high proteome coverage and accuracy.

This paper develops a deep learning method using DIA tensor for phenotype prediction from mass spectrometry data, achieving high accuracy in cancer classification.

This paper introduces a probabilistic search engine for tandem mass spectrometry proteomics that improves peptide identification rates by 5-10% using pre-computed fragmentation data and information theory.

This paper provides a guide to the quantitative proteomic data from the Cancer Cell Line Encyclopedia, explaining normalization methods and data interpretation.

The paper develops a proteomics pipeline to map functional protein neighborhoods in the mouse brain without external datasets, revealing novel interactions and disease-related pathways.

The paper compares cardiac protein profiles across species to identify suitable models for human cardiac diseases.

Phoenix Enhancer improves proteomics data analysis by clustering spectra to identify potential mis-identifications and enhance peptide spectrum match confidence.

The paper systematically maps the functional role of phosphorylation by analyzing phospho-deficient mutants in yeast and linking yeast phenotypes to human functional relevance.

LiP-Quant identifies drug targets in complex proteomes using limited proteolysis and mass spectrometry, providing EC50 estimates and distinguishing between homologous proteins.

The paper benchmarks crosslinking mass spectrometry search engines using synthetic peptide libraries, revealing significant variation in false discovery rates across different validation strategies.

The paper introduces ion-networks, a data format for DIA MS that captures full data integrity by representing reproducible fragment ions and their consistent co-elution across runs.

The paper introduces DDIA, a method combining DDA and DIA in a single LC-MS/MS run, with a complete data processing pipeline and minimal data requirements.

Omic-Sig uses multi-omics data to identify and visualize kinase-substrate interactions by analyzing differential abundances between case and control samples.

The paper introduces AID, a shape-independent method for analyzing thermal proteome profiles, and identifies panobinostat targets in a TPP dataset.

The paper reports a 50% reduction in PE2,3,4 missing proteins on Chromosome 17 using protein-level evidence from proteomics data.

Pyntheon is a Python tool that analyzes protein modifications and mutations across 83 model organisms to assess their impact on protein function and phenotypes.

The paper introduces SMSNet, a deep learning framework for de novo peptide sequencing that identifies thousands of new HLA antigens and phosphopeptides with high accuracy.

The paper uses pSILAC to identify brain regions with high protein synthesis activity and links them to metabolic and synaptic functions.

The paper assesses the accuracy of synchronous precursor selection (SPS) in proteomics datasets using a new post-processing tool applied to three public datasets.

The paper introduces a Bayesian method to estimate protein concentrations and phosphorylation site occupancy ratios from mass spectrometry data, applied to study PRKAR1A-RET gene fusion effects.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

This study quantifies variability in host proteins and bacterial taxa in stool samples from healthy individuals, showing greater inter-subject variation than temporal or technical variation.

SLM-Transform improves memory efficiency for peptide indexing in LC-MS/MS proteomics by limiting indexed ions and enabling constant-time queries.

TargetSeeker-MS identifies drug targets on a proteome scale using EBPS-MS data, showing reproducibility in C. elegans and HEK293 samples treated with benomyl.

The paper introduces AP3, an algorithm that improves proteotypic peptide prediction by incorporating peptide digestion probability, achieving higher accuracy than existing tools.

This study uses DIA-MS to rapidly assess proteomic changes after CRISPR-mediated HMGN1 knockout in cancer cells, identifying 147 significantly altered proteins and their functional impacts.

The paper quantifies host proteome dynamics during Salmonella infection, revealing cathepsin-dependent cell death mechanisms.

This paper demonstrates that data-independent acquisition mass spectrometry can accurately localize phosphosites by analyzing site-determining ions.

The paper introduces adductomicsR, an R package for detecting and quantifying protein adducts from LC-HRMS data of tryptic digests.