Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes.

ABSTRACT: Small open reading frames (smORFs) and their encoded microproteins play central roles in microbes. However, there is a vast unexplored space of smORFs within human-associated microbes. A recent bioinformatic analysis used evolutionary conservation signals to enhance prediction of small protein families. To facilitate the annotation of specific smORFs, we introduce SmORFinder. This tool combines profile hidden Markov models of each smORF family and deep learning models that better generalize to smORF families not seen in the training set, resulting in predictions enriched for Ribo-seq translation signals. Feature importance analysis reveals that the deep learning models learn to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codon synonyms found

SUBMITTER: Durrant MG 

PROVIDER: S-EPMC7856248 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature