Tag-based methods, such as MetaNovo , further demonstrated the applicability of de novo sequencing in metaproteomics. MetaNovo used de novo sequence tags to create a samplespecific sequence database in two steps: first, de novo sequencing generates de novo sequence tags using DirecTag , which are mapped to a protein sequence database via PeptideMapper . This step incorporates probabilistic protein inference to rank and filter proteins based on estimated species and protein abundances. In the second step, the filtered sequence database undergoes a conventional database search using MaxQuant for peptide identification, followed by taxonomic analysis using Unipept . When tested on eight human mucosal-luminal interface samples, MetaNovo identified a similar number of peptides and bacterial taxa as MetaPro-IQ . However, it detected significantly more non-bacterial peptides and outperformed workflows relying on matched metagenomes and whole-genome sequencing. MetaNovo improved taxon resolution, identified more taxa of interest, and flagged experimental contaminants, enhancing the accuracy and quality of the sequencing results.