Three-nucleotide periodicity of nucleotide diversity in a population enables the identification of open reading frames

Abstract

Accurate prediction of open reading frames (ORFs) is important for studying and using genome sequences. Ribosomes move along mRNA strands with a step of three nucleotides and datasets carrying this information can be used to predict ORFs. The ribosome-protected footprints (RPFs) feature a significant 3-nt periodicity on mRNAs and are powerful in predicting translating ORFs, including small ORFs (sORFs), but the application of RPFs is limited because they are too short to be accurately mapped in complex genomes. In this study, we found a significant 3-nt periodicity in the datasets of populational genomic variants in coding sequences, in which the nucleotide diversity increases every three nucleotides. We suggest that this feature can be used to predict ORFs and develop the Python package ‘OrfPP’, which recovers ~83% of the annotated ORFs in the tested genomes on average, independent of the population sizes and the complexity of the genomes. The novel ORFs, including sORFs, identified from single-nucleotide polymorphisms are supported by protein mass spectrometry evidence comparable to that of the annotated ORFs. The application of OrfPP to tetraploid cotton and hexaploid wheat genomes successfully identified 76.17% and 87.43% of the annotated ORFs in the genomes, respectively, as well as 4704 sORFs, including 1182 upstream and 2110 downstream ORFs in cotton and 5025 sORFs, including 232 upstream and 234 downstream ORFs in wheat. Overall, we propose an alternative and supplementary approach for ORF prediction that can extend the studies of sORFs to more complex genomes.

Identification of short open reading frames in plant genomes

“…Of the 89 conserved Arabidopsis sORFs, 39 were successfully identified by RPFs ( Hsu et al., 2016 ). More than a quarter of the sORFs predicted from SNPs, which were actively translated, overlapped with those predicted using RPFs ( Jiang et al., 2022 ). Thus, the SNP-based strategy is an effective approach to extending the study of sORFs, especially in complex genomes, but it requires the accumulation of nucleotide diversity in natural populations, and accuracy is also affected by the quality of the reference genome and SNPs datasets.…”

“…As the third nucleotides in codons are wobble nucleotides and are therefore subject to a more relaxed purification selection in nature ( Hurst, 2002 ), resulting in higher nucleotide diversities every three nucleotides in the coding sequences ( Jiang et al., 2022 ). This pattern resembles the 3-nt periodicity of RPFs on mRNAs and can therefore also be used to predict ORFs ( Figure 1D ).…”

“…This pattern resembles the 3-nt periodicity of RPFs on mRNAs and can therefore also be used to predict ORFs ( Figure 1D ). Jiang et al. (2022) recently developed a pipeline, OrfPP, to predict ORFs using the single-nucleotide polymorphisms (SNPs) datasets of natural populations and applied it to two polyploidy species: tetraploidy cotton ( Gossypium hirsutum ) and hexaploidy wheat ( Triticum aestivum ).…”

The Emerging Role of uORF-Encoded uPeptides and HLA uLigands in Cellular and Tumor Biology

“…In order to improve the identification of non-canonical ORFs, this periodicity was used to predict novel translating ORFs extending the annotated proteome with approx. 5000 novel ORFs in both wheat and cotton genomes [ 43 ].…”

Improved Super-Resolution Ribosome Profiling Revealed Prevalent Translation of Upstream ORFs and Small ORFs in Arabidopsis

“…Importantly, when using one nucleotide (nt) to assign the position of RPFs on mRNAs, precisely digested RPFs exhibit enrichment in the expected reading frame along the coding sequences, which is called 3-nt periodicity. This periodic property reflects that ribosomes decode 3 nt at a time and is a benchmark for high-quality Ribo-seq data (Ingolia et al, 2009; Jiang et al, 2022). 3-nt periodicity has been considered a reliable feature to distinguish real RPFs from contaminant RNA fragments protected by non-ribosomal protein complexes and to separate actively translating ribosomes from ribosomes stalled at certain regions of transcripts without engaging in translation (Guttman et al, 2013; Guydosh and Green, 2014; Jiang et al, 2022).…”