Evidence map›Paper›PMID 37292611›Full record

ArticlebioRxiv : the preprint server for biology2023

What can Ribo-seq and proteomics tell us about the non-canonical proteome?

John R Prensner, Jennifer G Abelin, Leron W Kok, Karl R Clauser, Jonathan M Mudge, Jorge Ruiz-Orera, Michal Bassani-Sternberg, Eric W Deutsch, Sebastiaan van Heesch

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 9 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 5 countries.

John R PrensnerDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Jennifer G AbelinBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Leron W KokPrincess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands.
Karl R ClauserBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Jonathan M MudgeEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Jorge Ruiz-OreraCardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany.
Michal Bassani-SternbergLudwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland.
Eric W DeutschInstitute for Systems Biology (ISB), Seattle, Washington 98109, USA.
Sebastiaan van HeeschPrincess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands.
Broad Institute · USPrincess Máxima Center · NLEuropean Bioinformatics Institute · GBInstitute for Systems Biology · USMax Delbrück Center · DEUniversity Hospital of Lausanne · CHUniversity of Michigan · US

Funding

GENCODE Resource ProjectU41HG007234 · NHGRI · SANGER INSTITUTE · PI FLICEK, PAUL · 2013 to 2020
$20.3M
ProteomicsP01CA206978 · NCI · DANA-FARBER CANCER INST · PI GETZ, GAD A · 2016 to 2025
$17.0M
GENCODE: comprehensive reference genome annotation for human and mouseU24HG007234 · NHGRI · EUROPEAN MOLECULAR BIOLOGY LABORATORY · PI Fergal James Martin · 2021 to 2026
$16.1M
Shortening the development cycle time of Trans Proteomic Pipeline tools with high performance computingR01GM087221 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI DEUTSCH, ERIC, MORITZ, ROBERT L · 2010 to 2021
$6.0M
Center of Excellence for High Throughput Proteogenomic CharacterizationU24CA270823 · NCI · BROAD INSTITUTE, INC. · PI STEVEN A CARR, MICHAEL A GILLETTE · 2022 to 2026
$5.3M
Proteogenomic Predictors of Recurrence in Non-small Cell Lung CancerU01CA271402 · NCI · WASHINGTON UNIVERSITY · PI STEVEN A CARR, MICHAEL A GILLETTE · 2022 to 2026
$5.0M
Center for comprehensive proteogenomic data analysisU24CA271075 · NCI · BROAD INSTITUTE, INC. · PI GAD A GETZ, Denkanikota R Mani · 2022 to 2026
$3.9M
Deciphering non-canonical translation in high risk medulloblastomaK08CA263552 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI John Prensner · 2022 to 2026
$1.2M
NCI NIH HHS K08 CA263552NCI NIH HHS P01 CA206978NCI NIH HHS U01 CA271402NCI NIH HHS U24 CA270823NCI NIH HHS U24 CA271075NHGRI NIH HHS U24 HG007234NHGRI NIH HHS U41 HG007234NIGMS NIH HHS R01 GM087221Wellcome Trust
6 · The paper itself

Abstract

Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief: The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights: Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.

Identifiers

PMID37292611
PMCPMC10245706
OpenAlexW4377018814

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.