smORFer: a modular algorithm to detect small ORFs in prokaryotes

Bartholomäus, Alexander; Kolte, Baban; Mustafayeva, Ayten; Goebel, Ingrid; Fuchs, Stephan; Benndorf, Dirk; Engelmann, Susanne; Ignatova, Zoya

doi:10.1093/nar/gkab477

Cited by 24 publications

(25 citation statements)

References 69 publications

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“…By screening reviews [ 47 , 78 ] and recently published studies [ 38 , 39 , 41 , 42 , 44 ], we found 12 stand-alone Ribo-seq based ORF detection tools (Table 1 ). Additionally, we identified several tools that predict potential ORFs from only RNA-seq (transcriptome) data and included the newest example ( ) for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…also uses the same noise filtering strategy based on a sigmoid curve as . Recently, a modular tool for ORF prediction based on both Ribo-seq and TIS data ( [ 44 ]) was introduced, which incorporates three-nucleotide periodicity information. The first module generates all potential sORF candidates, which can be filtered by Fourier transformation of their Ribo-seq read signal and/or based on a region of interest.…”

Section: Introductionmentioning

confidence: 99%

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RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Gelhausen

Müller

Svensson

et al. 2022

Briefings in Bioinformatics

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Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Gelhausen

Müller

Svensson

et al. 2022

Briefings in Bioinformatics

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“…The unallocated space (24 074–24 170) resulted in Prodigal reporting the next downstream CDS starting at 24 091 instead of 24 133 (as in the Ensembl annotation), erroneously including 5' UTR in the predicted CDS. There are now tools to identify putative short ORFs in both prokaryotes and eukaryotes using additional evidence, such as RNA expression data ( Bartholomäus et al , 2021 ; Ji et al , 2020 ; Miravet-Verde et al , 2019 ). Our results suggest that the identification of short and overlapping CDSs cannot be done independently without the potential for unforeseen consequences for annotation accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…CDS prediction, often the first step, is fast, with little user input, but may require augmentation by different methods to supplement the initial predictions. One example is a tool, such as smORFer ( Bartholomäus et al , 2021 ), that specializes in finding short ORFs through the use of RNA-seq, which can detect transcription events under certain environmental conditions. Further examples use sequence conservation scores and homology searches that can use existing database knowledge ( Badger and Olsen, 1999 ; Dunne and Kelly, 2017 ; ÓhÉigeartaigh et al , 2014 ).…”

Section: Introductionmentioning

confidence: 99%

No one tool to rule them all: prokaryotic gene prediction tool annotations are highly dependent on the organism of study

et al. 2021

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Motivation The biases in CoDing Sequence (CDS) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date, users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any CoDing Sequence (CDS) prediction tool and allow them to choose the right tool for their analysis. Results We present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of CDS prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availability https://github.com/NickJD/ORForise Supplementary information Supplementary data are available at Bioinformatics online.

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“…To date, many tools have been developed to predict ORFs, particularly small ORFs, from RPFs of prokaryotes [ 30 ] and eukaryotes (see review of [ 7 , 15 ]). These tools allocate the translated P-sites or A-sites [ 31 ] according to the positions and offsets of RPFs, thereby determining the translated frame for a given sequence of transcripts.…”

Section: Discussionmentioning

confidence: 99%

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

Song

Jiang²,

Gao³

2021

Life

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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.

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smORFer: a modular algorithm to detect small ORFs in prokaryotes

Cited by 24 publications

References 69 publications

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

No one tool to rule them all: prokaryotic gene prediction tool annotations are highly dependent on the organism of study

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

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