Advanced Proteomics Techniques and Applications

The paper evaluates transcriptome-informed proteogenomic databases, finding increased peptide sensitivity is a statistical artifact, but transcriptome data reduces protein identification ambiguity.

The paper introduces a pipeline to re-analyze public DIA proteomics datasets, integrating results into Expression Atlas to enhance data availability and utility.

This paper introduces a deep learning model to reduce sequence-specific biases in quantitative mass spectrometry data, improving correlation with mRNA expression.

This paper introduces a deep learning model called GLEAMS to embed mass spectra into a low-dimensional space for efficient clustering and analysis of large-scale proteomics data.

The paper evaluates machine learning models for predicting proteoform retention and migration times in top-down mass spectrometry, finding high accuracy with Prosit and a neural network model.

The paper evaluates MS-cleavable crosslinkers, finding that improved peptide backbone fragmentation, not mass resolution, is the main advantage of DSSO.

The paper introduces iKiP-DB, a database that predicts kinase activity from phosphoproteomic data, showing its effectiveness in analyzing both well-known and understudied kinases.

The paper uses peptide location fingerprinting to identify age- and disease-related structural changes in proteins across species and tissues.

The paper evaluates consensus spectrum generation methods in proteomics, finding BEST and BIN to be the most reliable for peptide identification.

The paper develops IV-KAPhE, a method to assign in vivo protein kinases to phosphosites using in vitro phosphoproteomic data, achieving high coverage and outperforming existing methods.

The paper validates amino acid variants in proteogenomics by using multiple reads from overlapping peptides to improve variant reliability.

The paper estimates the false discovery rate of phosphosites in public databases and predicts a more realistic count of true phosphosites in the human proteome.

The paper introduces an augmented MiCId workflow for fast and accurate identification of antibiotic resistance proteins using mass spectrometry data, achieving high sensitivity and precision.

The paper quantifies the human liver proteome at cell-type resolution across multiple conditions, revealing proteome changes in disease states and providing a web-based resource for exploration.

The paper generates a high-confidence mitochondrial protein compendium in Dictyostelium discoideum using proteomic and computational methods, identifying 1082 proteins and highlighting unique features compared to other organisms.

AHLF is a deep learning model trained on mass spectra to detect phosphorylated and cross-linked peptides with improved performance and interpretability.

The paper develops and benchmarks a comprehensive CNS proteomics database for Lymnaea stagnalis using bioinformatics and LC-MS proteomics, identifying more proteins than a non-specific database.

The study assesses proteomic sensitivity in human liver cells and identifies 134 proteins encoded by chromosome 18, finding limited overlap with transcriptomic data due to lower proteomic sensitivity.

The paper presents a comprehensive benchmark dataset for evaluating label-free quantification methods across various proteomics acquisition strategies.

This study evaluates yeast sample preparation protocols for proteomics, finding that urea with filter-aided or in-solution digestion provides best coverage, while solvents like acetone introduce artefacts.

VIQoR provides a web service for protein inference and quantification with interactive visualization, using algorithms and visualization tools to analyze proteomics data.

The paper uses metaproteomics to analyze human gut microbiome data, identifying generalist and specialist bacterial species and recovering ORFs missed by gene predictors.

This paper introduces TIFF, a method enhancing single-cell proteomics sensitivity and accuracy by filtering background ions and using 3D MS1 feature matching, achieving deep proteome analysis in single cells.

The paper evaluates the impact of prior Orbitrap software versions on low isobaric mass tag intensities and finds minimal detrimental effects on differential abundance experiments.

This paper evaluates a DIA proteomics approach using three proteases to increase proteome sequence coverage, achieving higher peptide and amino acid detection rates compared to trypsin alone.

The paper develops a high-sensitivity mass spectrometry workflow to quantify single-cell proteome changes upon perturbation, revealing a stable core proteome despite transcriptomic variability.

Pout2Prot converts Percolator output files into protein group files compatible with Prophane, using weighted spectral counts and multiple grouping strategies.

The paper introduces DDMC, a clustering strategy for phosphoproteomic data that groups phosphopeptides by phosphorylation abundance and sequence motifs, improving modeling and interpretation of kinase signaling in lung cancer.

SEQUENCE SLIDER integrates structural, genetic, and biophysical data to characterize isoforms in natural products by analyzing mass spectrometry and genomic data.

The paper introduces RoLiM, an algorithm for detecting protease activities in peptide data, identifying known and novel cleavage patterns in cancer and viral contexts.