smORFer: a modular algorithm to detect small ORFs in prokaryotes

Abstract

Emerging evidence places small proteins (≤ 50 amino acids) more centrally in physiological processes. Yet, the identification of functional small proteins and the systematic genome annotation of their cognate small open reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. Yet, they have difficulties evaluating prokaryotic genomes due to the unique architecture of prokaryotic genomes (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present our new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting smORFs. The unique feature of smORFer is that it uses integrated approach and considers structural features of the genetic sequence along with in-register translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way and dependent on the data available for a particular organism allows using different modules for smORF search.

Towards the characterization of the hidden world of small proteins in Staphylococcus aureus, a proteogenomics approach

“…The obtained sequencing reads, which represent ribosome-protected mRNA fragments (RPF), were mapped to the genome of S . aureus Newman and translation profiles were generated (for more details see [ 47 ]). For 135 (76%) sORFs identified by using our proteogenomics pipeline we detected RFPs.…”

“…Raw sequencing reads were trimmed using FASTX Toolkit (quality threshold: 20) and adapters were cut using cutadapt (minimal overlap of 1 nt) and mapped to the genome version NC_009641.1 (NCBI, January 2020). Following extraction of reads mapping to rRNAs, the remaining reads were uniquely mapped to the reference genome using Bowtie, parameter settings: -l 16 -n 1 -e 50 -m 1—strata–best y. Non-uniquely mapped reads were non-considered (for more details see [ 47 ]).…”

“…Ribosome profiling provides a snapshot of translation [57] and the position of the translating ribosomes can be assessed with codon precision PLOS GENETICS [58]. The obtained sequencing reads, which represent ribosome-protected mRNA fragments (RPF), were mapped to the genome of S. aureus Newman and translation profiles were generated (for more details see [47]). For 135 (76%) sORFs identified by using our proteogenomics pipeline we detected RFPs.…”

No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study

“…There are now tools to identify putative short ORFs in both prokaryotes and eukaryotes (Bartholomaus et al, 2020;Ji et al, 2020). However, our results suggest that the identification of short ORFs and overlapping ORFs can not be done independently without consequences for annotation accuracy.…”