Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput
technique for comprehensively analyzing protein content within biological
samples. This methodology is a cornerstone driving the advancement of
proteomics. In recent years, substantial strides have been made in
Data-Independent Acquisition (DIA) strategies, facilitating impartial and
non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra
present a formidable obstacle due to their inherent high multiplexing nature.
Each spectrum encapsulates fragmented product ions originating from multiple
precursor peptides. This intricacy poses a particularly acute challenge in de
novo peptide/protein sequencing, where current methods are ill-equipped to
address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a
deep-learning model based on transformer architecture. It deciphers peptide
sequences from DIA mass spectrometry data. Our results show significant
improvements over existing STOA methods, including DeepNovo-DIA and PepNet.
Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94%
at the amino acid level, and boosts precision by 59% to 81.36% at the peptide
level. Integrating DIA data and our Casanovo-DIA model holds considerable
promise to uncover novel peptides and more comprehensive profiling of
biological samples. Casanovo-DIA is freely available under the GNU GPL license
at this https URL

Casanovo-DIA 是一种基于 Transformer 架构的深度学习模型，能够从 DIA 质谱数据中解析肽段序列，并显著提高了已有方法的精度和召回率，在氨基酸水平上精度提高 15.14% 至 34.8%，召回率提高 11.62% 至 31.94%，在肽段水平上精度提高 59% 至 81.36%。将 DIA 数据与 Casanovo-DIA 模型集成能够大大提升对生物样本的肽段发现和全面性分析。