Additionally, while de novo sequencing can offer advantages in time-critical assessments by bypassing the need for comprehensive reference databases, its computational demands present a major challenge. Modern de novo algorithms often require additional hardware resources such as GPU-based processing, which may not be available in all research environments. The lack of streamlined pipelines also hinders the broader adoption of de novo sequencing in metaproteomics. Furthermore, infrequent updates to some post-processing tools can cause compatibility issues with emerging models. These limitations highlight the need for a user-friendly, modular, open-source solution that integrates multiple algorithms, ensures robust validation, and offers scalability, making de novo sequencing accessible to more researchers.