Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species, Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.