Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational Proteogenomics Workflow

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 2836; p. 19
• Main Authors: de Souza, Eduardo Vieira, Bizarro, Cristiano Valim
• Format: Journal Article
• Language: English
• Published: United States 2024
• Subjects: Bacteria - genetics; Bacteria - metabolism; Bacterial Proteins - genetics; Bacterial Proteins - metabolism; Computational Biology - methods; Machine Learning; Mass Spectrometry - methods; Micropeptides; Open Reading Frames - genetics; Proteogenomics - methods; Proteomics - methods; Software; Workflow; smORFs; Genome annotation; μProteInS; RNA-seq; Mass spectrometry; Proteomics
• ISSN: 1940-6029
• DOI: 10.1007/978-1-0716-4007-4_2

Genome annotation has historically ignored small open reading frames (smORFs), which encode a class of proteins shorter than 100 amino acids, collectively referred to as microproteins. This cutoff was established to avoid thousands of false positives due to limitations of pure genomics pipelines. Proteogenomics, a computational approach that combines genomics, transcriptomics, and proteomics, makes it possible to accurately identify these short sequences by overlaying different levels of omics evidence. In this chapter, we showcase the use of μProteInS, a bioinformatics pipeline developed for the identification of unannotated microproteins encoded by smORFs in bacteria. The workflow covers all the steps from quality control and transcriptome assembly to the scoring and post-processing of mass spectrometry data. Additionally, we provide an example on how to apply the pipeline's machine learning method to identify high-confidence spectra and pinpoint the most reliable identifications from large datasets.