What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

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

doi:10.1016/j.mcpro.2023.100631

Cited by 35 publications

(23 citation statements)

References 160 publications

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“…Discussions in the community are still ongoing, but prevailing thoughts seem to be that these ncORFs may best be classified in a separate category and not be included in the primary pantheon of human proteins. 27 . Each had a stellar group of speakers and lively discussions, which are available here: https://www.hupo.org/Webinars-and-Virtual-Presentations.…”

Section: Listmentioning

confidence: 99%

“…These detections raise the prospect that these might also be proteins in their own right and dramatically increase the size of the human proteome. Discussions in the community are still ongoing, but prevailing thoughts seem to be that these ncORFs may best be classified in a separate category and not be included in the primary pantheon of human proteins …”

Section: Progress On the Human Proteome Parts Listmentioning

confidence: 99%

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The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

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Section: Listmentioning

confidence: 99%

Section: Progress On the Human Proteome Parts Listmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

Self Cite

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“…3 Current computational approaches to Ribo-Seq have been developed on a central statistical test using manually curated features, where significant disagreement among tools reveals a lack of a “gold standard”. 5,6…”

Section: Mainmentioning

confidence: 99%

“…The fraction of internal ORFs (intORFs) and downstream (overlapping) ORFs (d(o)ORFs) is low with RiboTIE, in contrast to Ribo-TISH and Ribotricer, as prediction of these ORFs is known to be plagued by false-positive calls. 3,5 Additionally, RiboTIE calls considerably fewer lncRNA-ORFs compared to other high-performing tools such as ORFquant and Rp-Bp. Nonetheless, for all three tools, the called lncRNA-ORFs follow similar distributions: ∼25% of called lncRNA-ORFs have TISs that overlap with protein-coding sequences, whereas ∼46% overlap with exons from protein-coding transcripts ( Extended Data Fig.…”

Section: Mainmentioning

confidence: 99%

Deep learning to decode sites of RNA translation in normal and cancerous tissues

Clauwaert,

McVey,

Gupta

et al. 2024

Preprint

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The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Yet, accurately delineating the variation in RNA translation represents a significant challenge. Here, we develop RiboTIE, a transformer model-based approach to map global RNA translation. We find that RiboTIE offers unparalleled precision and sensitivity for ribosome profiling data. Application of RiboTIE to normal brain and medulloblastoma cancer samples enables high-resolution insights into disease regulation of RNA translation.

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“…It Is unclear to what extent the translated products of such regulatory translation contribute to the functional cellular proteome beyond their potential contribution to the antigen pool, as many of them lack conservation at the protein level (38,55,56). Extreme cases of translation regulation without peptide synthesis are represented by minimal ORFs consisting of a start codon immediately followed by a stop codon.…”

Section: Rna Translation Is Segmentedmentioning

confidence: 99%

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

Tierney,

Świrski,

Tjeldnes

et al. 2023

Preprint

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The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later ‘translons’. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

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What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

Cited by 35 publications

References 160 publications

The 2023 Report on the Proteome from the HUPO Human Proteome Project

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Deep learning to decode sites of RNA translation in normal and cancerous tissues

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

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