ArticleNature methods2025
A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- Degradomics for large-scale mechanistic insights on proteases and proteolysis in human health.The FEBS journal · 2026Review
- Identification of Species-Specific Peptide Markers in Highly Processed Meat Products Using De Novo Sequencing.Foods (Basel, Switzerland) · 2026Article
- Single-Injection Multi-Omics Analysis by Direct Infusion Mass Spectrometry.Angewandte Chemie (International ed. in English) · 2026Article
- From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.Journal of proteome research · 2025Review
- Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.Analytical chemistry · 2025Review
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Authors and funding
9 authors.
Funding
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.
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Registered trials
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