  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 DiaTrans, 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 DiaTrans 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
https://github.com/Biocomputing-Research-Group/DiaTrans.
