PowerNovo employs a two-stage de novo sequencing process that combines a transformer encoderdecoder for peptide prediction with a BERT encoder for further refinement and evaluation of candidate sequences. The first model autoregressively generates the amino acid sequence from an input MS2 spectrum and precursor information, using beam search to identify the most likely sequences. Next, the BERT model, trained with a masked language modeling objective to correct noisy peptide sequences, adjusts amino acids in peptide hypotheses generated by beam search. This model also classifies each peptide by detectability as "decoy," "poorly detectable," or "highly detectable," filtering out decoys and rescoring the remaining sequences to prioritize those with high detectability. The sequence with the highest score is then selected. PowerNovo also integrates protein assembly and protein inference modules to provide a complete workflow for mass spectrometry data analysis.