Mass Spectrometry Techniques and Applications

The paper introduces PyHDX, a tool for analyzing HDX-MS data to compute residue-level Gibbs free energy across protein conditions and homologues.

molDiscovery improves small molecule identification by learning fragmentation patterns from mass spectra, achieving higher efficiency and accuracy than existing methods.

The paper develops a sequence-specific model to predict peptide collision cross-section values using amino-acid sequence and charge state information from proteomic data.

This paper introduces an automated workflow using SHAP to identify biomarker candidates in imaging mass spectrometry data by ranking molecular species based on their predictive importance for biological class recognition.

The paper couples SPR imaging with MALDI MS to identify RNA-binding proteins from yeast mitochondrial lysate, revealing new cofactors for intron splicing beyond Mss116.

pDeep3 improves spectrum prediction accuracy using few-shot learning with minimal computational resources across different mass spectrometry instruments.

This paper describes a method to measure A2M concentration in human serum using MALDI mass spectrometry by analyzing the release of a specific peptide after trypsin interaction.

The paper introduces in vivo subcellular mass spectrometry to analyze proteo-metabolomic profiles in Xenopus laevis embryos, revealing differential quantitative activities between cell types.

This paper uses deep learning to improve clustering of mass spectrometry imaging data by transforming ion images into neural ion images, resulting in more consistent and fine-grained spatial clusters.

The paper measures CCS values for a million peptides across five organisms and develops a method to predict CCS for any peptide and organism.

This paper improves volatile metabolome analysis using SESI-Orbitrap MS by minimizing ion competition through optimized m/z window splitting.

This paper introduces a probabilistic framework that integrates mass spectrometry and retention time data to improve small molecule identification in LC-MS experiments.

ZODIAC improves molecular formula annotation in metabolomics by combining SIRIUS results with Gibbs sampling, reducing errors and discovering novel compounds.

rMSIcleanup annotates and removes matrix-related peaks in MSI data, improving post-processing by reducing redundant signals.

The paper introduces DeepEI, a deep learning method that predicts molecular fingerprints from EI-MS spectra to improve compound identification accuracy.

The paper analyzes UVPD fragmentation of tryptic peptides at 213 nm, finding that sequence coverage and fragment intensity correlate with precursor charge density and that aromatic amino acids enhance fragmentation.

This paper develops a high-sensitivity lipidomics workflow using TIMS and PASEF, enabling deeper lipid analysis from minimal samples with improved speed and coverage.

The paper integrates mass spectrometry imaging tools into Galaxy to enable accessible, reproducible, and transparent analysis of MSI data.

This study combines ambient ionization mass spectrometry imaging with microscopy to improve tumor margin detection in mouse tissues by identifying potential biomarkers.

The paper develops methods to improve image processing for tellurium-based imaging mass cytometry data by addressing background noise and signal-to-noise ratio issues.

The paper introduces a method using ion co-localization features to classify tumors from MSI data, addressing batch effects and generating hypotheses about sample classes.

Deuteros is a software tool for rapid analysis and visualization of differential HDX-MS data, designed to work with Waters DynamX.

The paper introduces the timsTOF Pro instrument with online PASEF, achieving high sensitivity and reproducibility in proteomics.