# π-SPECFormer

Conventional database searching assigns high-confidence peptides to only about 20% to 31% of acquired spectra, leaving the rest unread. π-SPECFormer was pre-trained on 291 million tandem mass spectra in two stages: self-supervised learning of fragmentation patterns, then refinement with peptide–spectrum match groupings. It beat task-specific baselines in de novo sequencing, spectrum clustering and modification detection, and in 103 lung adenocarcinoma samples it produced nine high-confidence mutation peptides, six of them absent from the public databases examined.

Conventional database searching assigns high-confidence peptides to only about 20% to 31% of acquired spectra, leaving the rest unread. π-SPECFormer was pre-trained on 291 million tandem mass spectra in two stages: self-supervised learning of fragmentation patterns, then refinement with peptide–spectrum match groupings. It beat task-specific baselines in de novo sequencing, spectrum clustering and modification detection, and in 103 lung adenocarcinoma samples it produced nine high-confidence mutation peptides, six of them absent from the public databases examined.