Advanced Proteomics Techniques and Applications

The paper discusses practical considerations for controlling false discoveries after one-way ANOVA testing in proteomics, focusing on the interaction between FDR control and post-hoc tests.

QuantUMS, a machine-learning module in DIA-NN, weights MS1 and fragment signals by quality-derived variance estimates, improving DIA precision, removing ratio compression in high-accuracy mode, and scoring each quantity's reliability.

The paper introduces hybrid-DIA, a method combining targeted and discovery phosphoproteomics for single spheroids, showing comparable accuracy to existing methods while profiling the global phosphoproteome.

The paper develops a spatial proteomics method to map the human pancreatic islet microenvironment and identifies spatial gradients in RNA processing proteins.

The paper investigates N-terminal proteoforms and finds they can engage in different protein complexes compared to their canonical counterparts.

This paper develops a method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation and validates it on mouse brain tissue.

This study evaluates the Astral mass analyzer's performance in single-cell proteomics using DIA, showing improved proteome depth and quantitative precision for ultra-low input samples.

This paper develops a high-speed LC/MS/MS system achieving 1,000 samples per day with high sensitivity and depth, demonstrating its application in spatial proteomics of mouse brain.

The paper introduces Narrow-window DIA, a fast proteomics method enabling high-throughput and comprehensive proteome analysis with high precision and accuracy.

This study benchmarks 12 DIA analysis workflows for shotgun proteomics samples, finding DIA-NN and Spectronaut in library-free mode offer highest sensitivity with good reproducibility and accuracy.

MASH Native provides a unified software solution for processing native top-down proteomics data, offering database searching and analysis tools in a user-friendly interface.

Mistle improves metaproteomics by efficiently searching predicted spectral libraries, outperforming existing tools in speed and memory usage.

The paper introduces Chronologer, a deep learning tool for accurate peptide retention time prediction, outperforming existing methods by leveraging harmonized datasets of post-translational modifications.

The paper introduces whatprot, a hybrid kNN-HMM classifier for amino acid sequence assignment from fluorosequencing data, improving precision and recall over individual methods.

ProtView evaluates in silico protease digest schemes by mapping peptides to protein and genome references, enabling optimization of proteomic and proteogenomic experiments.

The paper introduces directLFQ, a label-free quantification method that scales linearly with sample numbers, enabling rapid analysis of large proteomics datasets.

TopFD is a tool for top-down proteomics that improves proteoform feature detection accuracy and reproducibility compared to existing methods.

BiATNovo improves de novo peptide sequencing from DIA mass spectrometry data by using attention-based bidirectional models and post-processing, achieving better accuracy for long peptides and amino acid-level predictions.

The paper assesses how antibody reliability affects mRNA-protein correlations in TCGA tumour samples, finding lower correlations with less reliable antibodies.

PepGM infers viral taxonomy from proteomic data with confidence scores, showing strain-level resolution and accurate species-level assignments in some cases.

Mzion improves peptide identification in DDA proteomics by increasing match counts and precision across multiple datasets.

The paper introduces a pipeline to improve phosphosite identification confidence by combining multiple data sets and using decoy amino acids to estimate false localisation rates, resulting in more confident identifications.

The paper introduces R2-pY, a robotic method for phosphotyrosine enrichment in proteomics, achieving high efficiency and reproducibility with 96 samples processed in parallel.

The paper argues that missing values in label-free shotgun proteomics data are informative and proposes a statistical method to estimate the detection probability curve as a function of intensity.

μPhos is a phosphoproteomics platform that enables the identification of 5,000 phosphorylation sites from 5 µg of sample, supporting extensive perturbation experiments.

The paper introduces PTM-TBA, a software pipeline that identifies and localizes post-translational modifications in proteoforms using top-down and bottom-up mass spectrometry combined with UniProt annotations.

PPIAT is a web tool that integrates protein interaction data and mass spectrometry information to identify physical protein-protein interactions.

The paper records proteomes for non-essential gene knock-outs in yeast to uncover principles of protein expression and annotate gene function.

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