With advancements in machine learning, methods like pNovo and Novor introduced algorithms such as random forests and decision trees to improve de novo sequencing accuracy. In 2020, Johnson et al. showed that de novo sequencing using Novor can be a useful metric for assessing the quality of metaproteomics data, particularly when proteome databases are insufficient. By appending de novo sequences to a FASTA file and comparing the subsequent results with a database search, researchers can evaluate the suitability of the protein database. This method is particularly valuable for species without sequenced genomes, where taxonomically related databases are inadequate. The study revealed that poor matches are often due to factors such as post-translational modifications (PTMs), incomplete sequence data, and noise in the spectra. While these issues highlight the limitations of de novo sequencing under such conditions, Johnson et al. emphasized the need for more sophisticated de novo sequencing tools to handle noisy data and PTMs, rather than solely relying on high-quality genome annotations.