First, we generated databases of all the putative ORFs encoded by the genomes of 109 different bacteria. The database of M. pneumoniae was used to perform the shotgun MS and RNA-Seq studies that were aimed at evaluating the coverage and performance of experimental approaches in the discovery of SEPs. In a parallel, experiment-independent manner, RanSEPs performed in silico predictions of potential novel proteins in the database. Results coming from both experimental and computational approaches are integrated in a validation step using a set of 570 SEPs characterized both in this work and in previous studies. Finally, RanSEPs predictions for the 109 bacterial genomes are combined together to assess the functional diversity and importance of predicted SEPs. The second part of the figure highlights how RanSEPs functions. In step 0 (grey box), RanSEPs detects annotated standard proteins (purple) and SEPs (yellow). By BlastP, non-conserved standard and SEP proteins are detected (pink and light pink, respectively). In parallel, protein features are computed and filtered by Recursive Feature Elimination. These features are combined with general features of biological interest. In step 1 (yellow box), RanSEPs randomly subsets annotated standard and small proteins into a positive (green and yellow), a feature (blue and yellow) and a negative (pink and light pink) set from the bulk of non-conserved sequences. During step 2 (blue box), specific features that vary with each iteration are appended. In step 3 (purple box), the labelled positive and negative sets are divided into training and test sets. Step 4 (green box) consists of collecting the classifiers and classification task results, and computing the final statistics and scores for all the sequences. Step 0 is only run once and then it is out of the iteration process. Steps 1 to 3 are repeated as many times as iterations selected by the user. Step 4 is computed at the end to integrate the results of each iteration. 