ABSTRACTSmall proteins shorter than 50 amino acids have been long overlooked. A number of small proteins have been identified in several model bacteria using experimental approaches and assigned important functions in diverse cellular processes. The recent development of ribosome profiling technologies has allowed a genome-wide identification of small proteins and small ORFs (smORFs), but our incomplete understanding of small proteins hinders de novo computational prediction of smORFs in non-model bacterial species. Here, we have identified several sequence features for smORFs by a systematic analysis of all the known small proteins in E. coli, among which the translation initiation rate is the strongest determinant. By integrating these features into a support vector machine learning model, we have developed a novel sPepFinder algorithm that can predict conserved smORFs in bacterial genomes with a high accuracy of 92.8%. De novo prediction in E. coli has revealed several novel smORFs with evidence of translation supported by ribosome profiling. Further application of sPepFinder in 549 bacterial species has led to the identification of > 100,000 novel smORFs, many of which are conserved at the amino acid and nucleotide levels under purifying selection. Overall, we have established sPepFinder as a valuable tool to identify novel smORFs in both model and non-model bacterial organisms, and provided a large resource of small proteins for functional characterizations.
SNP Selection in Genome-Wide Association Studies via Penalized Support Vector Machine with MAX Test