Tandem Mass Spectra Representation Design for Transformer-Based De Novo Peptide Sequencing

thesis · 2026

thesis · 2026. Xinlue Shen
Date 2026-07-27
Type thesis
Publisher MSc thesis
Contribution benchmark
Supervisor Kaizhong Zhang
Link https://hdl.handle.net/20.500.14721/40077

Authors

  1. Xinlue Shen · University of Western Ontario

Methods and tools

  • Casanovo: First Transformer
  • DpNovo: Transformer + dynamic programming
  • Spectrum representation ablation study: MSc thesis ablation of how MS/MS peaks should be represented for a Transformer de novo sequencer. Holds a Casanovo-style encoder-decoder backbone fixed and varies only the peak embedding: additive m/z-intensity, separated/concatenated m/z and intensity, precursor-normalised (relative) m/z, learnable relative m/z, zero-vector controls, and probability-aware embeddings fed with DpNovo’s signal-vs-noise peak probabilities. Finds that separating peak features beats the additive baseline, relative m/z adds a further gain, and the best result combines external signal probability with intensity and mass features: evidence that input representation, not just architecture, moves the needle on Transformer de novo accuracy.

Seen in the charts

Back to the full map

Back to top