Denis Beslic

This paper presents a framework for peptide identification using commercial nanopore sequencing platforms, achieving single-amino-acid resolution and reliable performance across sequencing runs.

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

The paper introduces seq2squiggle, a transformer-based model for simulating nanopore sequencing signals, which outperforms existing tools on real data.

This study evaluates de novo sequencing tools for monoclonal antibodies, finding that deep learning methods improve recall but challenges remain in heavy chain sequencing.