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 in the codon table. A core-genome analysis of 26 bacterial species identifies several core smORFs of unknown function. We pre-compute smORF annotations for thousands of RefSeq isolate genomes and Human Microbiome Project metagenomes and provide these data through a public web portal.

journal_name

Cell Host Microbe

journal_title

Cell host & microbe

authors

Durrant MG,Bhatt AS

doi

10.1016/j.chom.2020.11.002

subject

Has Abstract

pub_date

2021-01-13 00:00:00

pages

121-131.e4

issue

1

eissn

1931-3128

issn

1934-6069

pii

S1931-3128(20)30619-3

journal_volume

29

pub_type

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