Advanced Proteomics Techniques and Applications

UniSpec: A Deep Learning Approach for Predicting Energy-Sensitive Peptide Tandem Mass Spectra and Generating Proteomics-Wide In-Silico Spectral Libraries

Development of an in situ cell-type specific proteome analysis method using antibody-mediated biotinylation

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

Evaluating the capabilities of the Astral mass analyzer for single-cell proteomics

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.

One thousand samples per day capillary-flow LC/MS/MS for high-speed, high-sensitivity and in-depth proteomics

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.

Narrow-window DIA: Ultra-fast quantitative analysis of comprehensive proteomes with high sequencing depth

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

Benchmarking DIA data analysis workflows

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: A Unified Solution for Native Top-Down Proteomics Data Processing

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: bringing spectral library predictions to metaproteomics with an efficient search index

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

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

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.

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier

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: A versatile tool for in silico protease evaluation and selection in a proteomic and proteogenomic context

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

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes

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

TopFD - A Proteoform Feature Detection Tool for Top-Down Proteomics

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

BiATNovo: An Attention-based Bidirectional De Novo Sequencing Framework for Data-Independent-Acquisition Mass Spectrometry

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.

Antibody reliability influences observed mRNA-protein correlations in tumour samples

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

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores

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

Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics

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

Assessing multiple evidence streams to decide on confidence for identification of post-translational modifications, within and across data sets

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.

Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics

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

Missing values are informative in label-free shotgun proteomics data: estimating the detection probability curve

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: a scalable and sensitive platform for functional phosphoproteomics

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

Characterization of proteoform post-translational modifications by top-down and bottom-up mass spectrometry in conjunction with UniProt annotations

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:Target mass spectrometry based Protein-Protein interaction analytics tool

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

The Proteomic Landscape of Genome-Wide Genetic Perturbations

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

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

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

Retention time and fragmentation predictors increase confidence in variant peptide identification

This study improves variant peptide identification in proteomics by integrating retention time and fragmentation predictors into a proteogenomic pipeline, enhancing discrimination between correct and incorrect matches.

Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins

A machine learning model trained on proteomics data from 183 human datasets identifies tissue and cell-type specific proteins with high accuracy.

DIP-MS: A novel ultra-deep interaction proteomics for the deconvolution of protein complexes

DIP-MS resolves protein complex isoforms using deep learning and mass spectrometry, revealing new subunits in human prefoldin complexes.

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

prolfqua provides an R package for proteomics differential expression analysis with quality control, normalization, and statistical modeling, showing sensitive and specific results.

Real-time library search increases cross-link identification depth across all levels of sample complexity

The paper introduces real-time library search (RTLS) to improve cross-link identification in XL-MS by leveraging intensity ratios of diagnostic peaks, enhancing identification depth in complex samples.