The coding capacity of SARS-CoV-2 Article Swipe
Yaara Finkel
,
Orel Mizrahi
,
Aharon Nachshon
,
Shira Weingarten-Gabbay
,
David Morgenstern
,
Yfat Yahalom-Ronen
,
Hadas Tamir
,
Hagit Achdout
,
Dana Stein
,
Ofir Israeli
,
Adi Beth-Din
,
Sharon Melamed
,
Shay Weiss
,
Tomer Israely
,
Nir Paran
,
Michal Schwartz
,
Noam Stern‐Ginossar
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.05.07.082909
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.05.07.082909
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing Coronavirus disease 19 (COVID-19) pandemic 1,2 . In order to understand SARS-CoV-2 pathogenicity and antigenic potential, and to develop diagnostic and therapeutic tools, it is essential to portray the full repertoire of its expressed proteins. The SARS-CoV-2 coding capacity map is currently based on computational predictions and relies on homology to other coronaviruses. Since coronaviruses differ in their protein array, especially in the variety of accessory proteins, it is crucial to characterize the specific collection of SARS-CoV-2 proteins in an unbiased and open-ended manner. Utilizing a suite of ribosome profiling techniques 3–8 , we present a high-resolution map of the SARS-CoV-2 coding regions, allowing us to accurately quantify the expression of canonical viral open reading frames (ORF)s and to identify 23 novel unannotated viral translated ORFs. These ORFs include upstream ORFs (uORFs) that are likely playing a regulatory role, several in-frame internal ORFs lying within existing ORFs, resulting in N-terminally truncated products, as well as internal out-of-frame ORFs, which generate novel polypeptides. We further show that viral mRNAs are not translated more efficiently than host mRNAs; rather, virus translation dominates host translation due to high levels of viral transcripts. Overall, our work reveals the full coding capacity of SARS-CoV-2 genome, providing a rich resource, which will form the basis of future functional studies and diagnostic efforts.
Related Topics
Concepts
ORFS
Open reading frame
Biology
Coronavirus
Computational biology
Genome
Genetics
Ribosome profiling
Coding region
Gene
Virology
Translation (biology)
Coronavirus disease 2019 (COVID-19)
Messenger RNA
Peptide sequence
Infectious disease (medical specialty)
Disease
Medicine
Pathology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.05.07.082909
- https://www.biorxiv.org/content/biorxiv/early/2020/08/05/2020.05.07.082909.full.pdf
- OA Status
- green
- Cited By
- 68
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022905828
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3022905828Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.05.07.082909Digital Object Identifier
- Title
-
The coding capacity of SARS-CoV-2Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-07Full publication date if available
- Authors
-
Yaara Finkel, Orel Mizrahi, Aharon Nachshon, Shira Weingarten-Gabbay, David Morgenstern, Yfat Yahalom-Ronen, Hadas Tamir, Hagit Achdout, Dana Stein, Ofir Israeli, Adi Beth-Din, Sharon Melamed, Shay Weiss, Tomer Israely, Nir Paran, Michal Schwartz, Noam Stern‐GinossarList of authors in order
- Landing page
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https://doi.org/10.1101/2020.05.07.082909Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/08/05/2020.05.07.082909.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2020/08/05/2020.05.07.082909.full.pdfDirect OA link when available
- Concepts
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ORFS, Open reading frame, Biology, Coronavirus, Computational biology, Genome, Genetics, Ribosome profiling, Coding region, Gene, Virology, Translation (biology), Coronavirus disease 2019 (COVID-19), Messenger RNA, Peptide sequence, Infectious disease (medical specialty), Disease, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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68Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4, 2023: 4, 2022: 7, 2021: 14Per-year citation counts (last 5 years)
- References (count)
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53Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.existing | 159 |
| abstract_inverted_index.generate | 173 |
| abstract_inverted_index.homology | 63 |
| abstract_inverted_index.identify | 133 |
| abstract_inverted_index.in-frame | 154 |
| abstract_inverted_index.internal | 155, 169 |
| abstract_inverted_index.pandemic | 18 |
| abstract_inverted_index.proteins | 91 |
| abstract_inverted_index.quantify | 121 |
| abstract_inverted_index.regions, | 116 |
| abstract_inverted_index.ribosome | 102 |
| abstract_inverted_index.specific | 87 |
| abstract_inverted_index.syndrome | 4 |
| abstract_inverted_index.unbiased | 94 |
| abstract_inverted_index.upstream | 143 |
| abstract_inverted_index.Utilizing | 98 |
| abstract_inverted_index.accessory | 79 |
| abstract_inverted_index.antigenic | 28 |
| abstract_inverted_index.canonical | 125 |
| abstract_inverted_index.currently | 55 |
| abstract_inverted_index.dominates | 193 |
| abstract_inverted_index.essential | 39 |
| abstract_inverted_index.expressed | 47 |
| abstract_inverted_index.products, | 165 |
| abstract_inverted_index.profiling | 103 |
| abstract_inverted_index.proteins, | 80 |
| abstract_inverted_index.proteins. | 48 |
| abstract_inverted_index.providing | 214 |
| abstract_inverted_index.resource, | 217 |
| abstract_inverted_index.resulting | 161 |
| abstract_inverted_index.truncated | 164 |
| abstract_inverted_index.(COVID-19) | 17 |
| abstract_inverted_index.SARS-CoV-2 | 25, 50, 90, 114, 212 |
| abstract_inverted_index.accurately | 120 |
| abstract_inverted_index.collection | 88 |
| abstract_inverted_index.diagnostic | 33, 228 |
| abstract_inverted_index.especially | 74 |
| abstract_inverted_index.expression | 123 |
| abstract_inverted_index.functional | 225 |
| abstract_inverted_index.open-ended | 96 |
| abstract_inverted_index.potential, | 29 |
| abstract_inverted_index.regulatory | 151 |
| abstract_inverted_index.repertoire | 44 |
| abstract_inverted_index.techniques | 104 |
| abstract_inverted_index.translated | 138, 184 |
| abstract_inverted_index.understand | 24 |
| abstract_inverted_index.Coronavirus | 14 |
| abstract_inverted_index.coronavirus | 5 |
| abstract_inverted_index.efficiently | 186 |
| abstract_inverted_index.predictions | 59 |
| abstract_inverted_index.respiratory | 3 |
| abstract_inverted_index.therapeutic | 35 |
| abstract_inverted_index.translation | 192, 195 |
| abstract_inverted_index.unannotated | 136 |
| abstract_inverted_index.(SARS-CoV-2) | 7 |
| abstract_inverted_index.N-terminally | 163 |
| abstract_inverted_index.characterize | 85 |
| abstract_inverted_index.out-of-frame | 170 |
| abstract_inverted_index.transcripts. | 202 |
| abstract_inverted_index.computational | 58 |
| abstract_inverted_index.coronaviruses | 68 |
| abstract_inverted_index.pathogenicity | 26 |
| abstract_inverted_index.polypeptides. | 175 |
| abstract_inverted_index.coronaviruses. | 66 |
| abstract_inverted_index.high-resolution | 110 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5082856046 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 17 |
| corresponding_institution_ids | https://openalex.org/I53964585 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.91289265 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |