Daniela Klaproth-Andrade

PepPr improves peptide sequencing by integrating peptide priors, enhancing reference and variant peptide identification while maintaining discovery flexibility.

The paper improves Casanovo, a deep learning de novo peptidesequencer, by enhancing interpretability, generalizing its use in database search, speeding up performance, and adding tools for adoption and result interpretation.

Modanovo is a de novo peptide sequencing model that handles post-translational modifications, outperforming existing methods and complementing database searches in phosphoproteomics.

PROTRIDER detects protein expression outliers in proteomics data using conditional autoencoders, outperforming existing methods and revealing enrichments of pathogenic variants in outliers.

This paper introduces Spectralis, a deep learning method for de novo peptide sequencing that improves sensitivity and precision in tandem mass spectrometry.