Proteomics

Generating high-quality libraries for DIA datasets
Lecture: Generating high-quality predicted libraries for DIA dataset peptide detection
Nov 15, 2022
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Generating high-quality libraries for DIA datasets
Immunopeptidomics-based design of mRNA vaccine formulations against Listeria monocytogenes
Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and …
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MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates
Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in …
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MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates
Enabling novel and challenging proteomics workflows with MS²Rescore
Talk at the 2022 online EuPA Early-Career Researchers Day on MS²Rescore applications
Jun 26, 2022
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Enabling novel and challenging proteomics workflows with MS²Rescore
Machine learning to the rescue: Enabling novel proteomics workflows with data-driven bioinformatics methods
Introduction to proteomics, mass spectrometry, and machine learning for a layman audience and a summary of my PhD projects
May 19, 2022
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Machine learning to the rescue: Enabling novel proteomics workflows with data-driven bioinformatics methods
A comprehensive evaluation of consensus spectrum generation methods in proteomics
Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of …
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A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics
In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction …
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MS²DIP: Highly accurate MS2 spectrum prediction for modified peptides & MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates
Accurate MS2 spectrum predictions enable drastic improvements in peptide identification workflows. This identification improvement is …
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DeepLC can predict retention times for peptides that carry as-yet unseen modifications
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid …
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DeepLC can predict retention times for peptides that carry as-yet unseen modifications
timsTOF analysis combined with fragment intensity predictions results in improved identification of classical bioactive peptides and sORF-encoded peptides
Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and …
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