RiboNT: A Noise-Tolerant Predictor of Open Reading Frames from Ribosome-Protected Footprints

Abstract

Ribo-seq, also known as ribosome profiling, refers to the sequencing of ribosome-protected mRNA fragments (RPFs). This technique has greatly advanced our understanding of translation and facilitated the identification of novel open reading frames (ORFs) within untranslated regions or non-coding sequences as well as the identification of non-canonical start codons. However, the widespread application of Ribo-seq has been hindered because obtaining periodic RPFs requires a highly optimized protocol, which may be difficult to achieve, particularly in non-model organisms. Furthermore, the periodic RPFs are too short (28 nt) for accurate mapping to polyploid genomes, but longer RPFs are usually produced with a compromise in periodicity. Here we present RiboNT, a noise-tolerant ORF predictor that can utilize RPFs with poor periodicity. It evaluates RPF periodicity and automatically weighs the support from RPFs and codon usage before combining their contributions to identify translated ORFs. The results demonstrate the utility of RiboNT for identifying both long and small ORFs using RPFs with either good or poor periodicity. We implemented the pipeline on a dataset of RPFs with poor periodicity derived from membrane-bound polysomes of Arabidopsis thaliana seedlings and identified several small ORFs (sORFs) evolutionarily conserved in diverse plant species. RiboNT should greatly broaden the application of Ribo-seq by minimizing the requirement of RPF quality and allowing the use of longer RPFs, which is critical for organisms with complex genomes because these RPFs can be more accurately mapped to the position from which they were derived.

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

“…The predicted ORFs are classified into 11 categories following previous definitions [ 1 , 3 , 5 ]. They are (i) annotated ORF, (ii) truncated ORF, (iii) extended ORF, (iv) uORF, (v) overlapped uORF, (vi) downstream ORF (dORF), (vii) overlapped dORF, (viii) ORFs located in non-coding RNAs, (ix) internal ORF, (x) ORFs located in transposable elements and (xi) ORFs in pseudogenes [ 3 ].…”

“…The predicted ORFs are classified into 11 categories following previous definitions [ 1 , 3 , 5 ]. They are (i) annotated ORF, (ii) truncated ORF, (iii) extended ORF, (iv) uORF, (v) overlapped uORF, (vi) downstream ORF (dORF), (vii) overlapped dORF, (viii) ORFs located in non-coding RNAs, (ix) internal ORF, (x) ORFs located in transposable elements and (xi) ORFs in pseudogenes [ 3 ]. Although the ORFs are classified according to the previous annotation of ORFs in the reference genome, OrfPP can also be used for de novo prediction of ORFs when the annotation of ORFs is not yet known.…”

“…Lastly, the preparation of high-quality RPFs can be difficult, particularly in many non-model organisms. Although some quality-insensitive predictors have been developed to use low-quality RPFs [ 3 , 23 ], RPFs with poor or no periodicity introduce unpredictable errors into the results and even lead to ORF prediction failure. Indeed, when we attempted to test OrfPP in many other species, such as Solanum lycopersicum , S. pennelli and Medicago truncatula [ 56 ], we found that the RPFs published in this study were not periodic and thus could not be utilized to predict ORFs.…”

Identification of short open reading frames in plant genomes

“…As ribosomes move along mRNA strands with a step of three nucleotides during the translation of mRNAs, the mapping loci of RPFs on mRNAs show a strong 3-nt periodicity, which provides information for the identification of translating frames on mRNAs ( Figure 1B ). To better analyze and mine the information in RPFs, many computation tools implementing different algorithms have been developed to predict sORFs or ORFs on noncoding RNAs (see review of Ong et al., 2022 ), including FLOSS ( Ingolia et al., 2014 ), RiboTaper ( Calviello et al., 2016 ), RiboCode ( Xiao et al., 2018 ), ORFquant ( Calviello et al., 2020 ), RiboNT ( Song et al., 2021 ), and slORFfinder ( Song et al., 2023 ). Among them, ORFScore and RiboTaper are the most frequently used tools in plant sORF studies.…”

Small Open Reading Frames, How to Find Them and Determine Their Function

“…The second group of ribosome profiling ORF classifiers uses spectral analysis of nucleotide periodicity to detect statistically significant regions with a 3 nucleotide periodic signal. Among these are SPECTre (Chun et al, 2016) RiboTaper (Calviello et al, 2016), ORFquant (Calviello et al, 2020), Ribotricer (Choudhary et al, 2020) and RiboNT (Song et al, 2021). RiboNT and Ribotricer both apply weighted codon scores to mitigate noise, Ribotricer, in addition, presented evidence for sustained recall when the size of ORF is decreased (dropping at < 20 codons).…”