A transformer model for de novo sequencing of data independent acquisition mass spectrometry data

peer-reviewed · Nature Methods · 2025

peer-reviewed · Nature Methods · 2025. Justin Sanders et al. A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem…
Date 2025-07-01
Type peer-reviewed
Venue Nature Methods
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s41592-025-02718-y
Citations (OpenAlex) 13
Venue 2-year citedness 22.29

Preprint version: A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024-06-03, bioRxiv)

Abstract

A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem, in which the generating amino acid sequence is inferred directly from an observed fragmentation spectrum without the use of a sequence database. Recently, deep learning models have made substantial advances in de novo sequencing by learning from massive datasets of high-confidence labeled mass spectra. However, these methods are designed primarily for data-dependent acquisition experiments. Over the past decade, the field of mass spectrometry has been moving toward using data-independent acquisition (DIA) protocols for the analysis of complex proteomic samples owing to their superior specificity and reproducibility. Hence, we present a de novo sequencing model called Cascadia, which uses a transformer architecture to handle the more complex data generated by DIA protocols. In comparisons with existing approaches for de novo sequencing of DIA data, Cascadia achieves substantially improved performance across a range of instruments and experimental protocols.

Authors

  1. Justin Sanders · University of Washington
  2. Bo Wen · Baylor College of Medicine, University of Washington
  3. Paul Rudnick · Spectragen Informatics
  4. Rich Johnson · University of Washington
  5. Michael Riffle · University of Washington
  6. Christine C. Wu · University of Washington
  7. Sewoong Oh · University of Washington
  8. Michael J. MacCoss · University of Washington
  9. William Stafford Noble · University of Washington

Methods and tools

Data used

Cites (18)

Cited by (8)

Seen in the charts

Back to the full map

Back to top