possibly increasing the risk of false positives. Despite this potential risk, Aakko et al. demonstrated the feasibility of DIA-MS for consistent and accurate quantification of microbial samples, proving its applicability in complex metaproteomes using synthetic mixtures and human fecal samples. Moreover, Pietilä et al. advanced this further with glaDIAtor, a tool that enables untargeted DIA metaproteomics by creating a pseudospectral library directly from DIA data, eliminating the need for DDA-based libraries and reducing MS runs, thus improving workflow efficiency. However, most de novo sequencing tools are trained on DDA-MS data and are generally unsuitable for DIA-MS. Recent tools like DeepNovo-DIA , Transformer-DIA , PepNet , and Cascadia offer promising solutions to this challenge, demonstrating the potential of de novo sequencing for DIA-MS. DeepNovo-DIA restructures the long short-term memory (LSTM) network to leverage the extra dimensionality of DIA data, identifying co-eluting precursor ions and their fragments, as well as fragment ions across multiple neighboring spectra. The networks learn 3D shapes of fragment ions, correlations between precursors and their fragments, and peptide sequence patterns.