Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful
technology for high-throughput, accurate, and reproducible quantitative proteomics. This …

In recent years machine learning has made extensive progress in modeling many aspects of
mass spectrometry data. We brought together proteomics data generators, repository …

Bottom-up proteomics provides peptide measurements and has been invaluable for moving
proteomics into large-scale analyses. Commonly, a single quantitative value is reported for …

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or
molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear …

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on
minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method …

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem
mass spectra—a task where machine learning algorithms have proven invaluable. We …

Analysis of differential abundance in proteomics data sets requires careful application of
missing value imputation. Missing abundance values widely vary when performing …

Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative
proteomics studies. Nevertheless, well-principled statistical inference procedures are still …

In metaproteomics, the study of the collective proteome of microbial communities, the protein
inference problem is more challenging than in single-species proteomics. Indeed, a peptide …

Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a
method of choice for quantitative proteomic applications requiring high throughput analysis …