Evidence map›Paper›PMID 31750902›Full record

ArticleBioinformatics (Oxford, England)2020

Accurate detection of short and long active ORFs using Ribo-seq data.

Saket Choudhary, Wenzheng Li, Andrew D Smith

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

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  13. TheGenome research · 2025
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  15. Proteogenomics Reveals Microproteins in Activated T Cells.Molecular & cellular proteomics : MCP · 2025
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  18. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Saket ChoudharyComputational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA.
Wenzheng LiComputational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA.
Andrew D SmithComputational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA.

Funding

Computational Methods for Deep Sequencing Based RBP Binding Motif CharacterizatioR01HG006015 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI PENALVA, LUIZ OTAVIO, SMITH, ANDREW DAVID · 2011 to 2017
$2.6M
NHGRI NIH HHS R01 HG006015
6 · The paper itself

Abstract

motivationRibo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise.

resultsWe present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish. AVAILABILITY AND IMPLEMENTATION: Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Protein BiosynthesisRibosomesAnimalsHumansMiceOpen Reading FramesRatsRNA, MessengerTranscriptomeRNA, Messenger

Identifiers

PMID31750902
PMCPMC7141849

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.