Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes Article Swipe
M Orlic-Milacic
,
Karen Rothfels
,
Lisa Matthews
,
Adam Wright
,
Bijay Jassal
,
Veronica Shamovsky
,
Quang M. Trinh
,
Marc Gillespie
,
Cristoffer Sevilla
,
Krishna Kumar Tiwari
,
Eliot Ragueneau
,
Chuqiao Gong
,
Ralf Stephan
,
Bruce May
,
Robin Haw
,
Joel Weiser
,
Deidre Beavers
,
Patrick Conley
,
Henning Hermjakob
,
Lincoln Stein
,
Peter D’Eustachio
,
Guanming Wu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.18.562964
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.18.562964
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome’s pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
Related Topics
Concepts
Annotation
Context (archaeology)
Phenotype
Computational biology
Disease
Genomics
Biology
Functional genomics
Genetics
Model organism
Clinical phenotype
Gene
Genome
Medicine
Paleontology
Pathology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.10.18.562964
- https://www.biorxiv.org/content/biorxiv/early/2023/10/22/2023.10.18.562964.full.pdf
- OA Status
- green
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387858461
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387858461Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.10.18.562964Digital Object Identifier
- Title
-
Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-22Full publication date if available
- Authors
-
M Orlic-Milacic, Karen Rothfels, Lisa Matthews, Adam Wright, Bijay Jassal, Veronica Shamovsky, Quang M. Trinh, Marc Gillespie, Cristoffer Sevilla, Krishna Kumar Tiwari, Eliot Ragueneau, Chuqiao Gong, Ralf Stephan, Bruce May, Robin Haw, Joel Weiser, Deidre Beavers, Patrick Conley, Henning Hermjakob, Lincoln Stein, Peter D’Eustachio, Guanming WuList of authors in order
- Landing page
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https://doi.org/10.1101/2023.10.18.562964Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/10/22/2023.10.18.562964.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/10/22/2023.10.18.562964.full.pdfDirect OA link when available
- Concepts
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Annotation, Context (archaeology), Phenotype, Computational biology, Disease, Genomics, Biology, Functional genomics, Genetics, Model organism, Clinical phenotype, Gene, Genome, Medicine, Paleontology, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.landing_page_url | https://doi.org/10.1101/2023.10.18.562964 |
| primary_location.id | doi:10.1101/2023.10.18.562964 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2023/10/22/2023.10.18.562964.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2023.10.18.562964 |
| publication_date | 2023-10-22 |
| publication_year | 2023 |
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| referenced_works_count | 77 |
| abstract_inverted_index.a | 14, 125 |
| abstract_inverted_index.an | 33 |
| abstract_inverted_index.in | 4, 56 |
| abstract_inverted_index.is | 24 |
| abstract_inverted_index.of | 7, 17, 20, 40, 51, 59, 69, 95, 100, 105, 109, 132, 144 |
| abstract_inverted_index.to | 26, 53, 90, 98, 127 |
| abstract_inverted_index.400 | 54 |
| abstract_inverted_index.and | 10, 29, 112, 121, 137 |
| abstract_inverted_index.can | 12 |
| abstract_inverted_index.for | 46 |
| abstract_inverted_index.has | 86 |
| abstract_inverted_index.the | 5, 57, 106 |
| abstract_inverted_index.4000 | 48 |
| abstract_inverted_index.been | 87 |
| abstract_inverted_index.data | 122 |
| abstract_inverted_index.from | 73, 82, 92 |
| abstract_inverted_index.gene | 75 |
| abstract_inverted_index.open | 34 |
| abstract_inverted_index.over | 47 |
| abstract_inverted_index.that | 23 |
| abstract_inverted_index.close | 52 |
| abstract_inverted_index.genes | 55 |
| abstract_inverted_index.human | 41, 134 |
| abstract_inverted_index.infer | 128 |
| abstract_inverted_index.model | 123, 138 |
| abstract_inverted_index.using | 103 |
| abstract_inverted_index.whose | 80 |
| abstract_inverted_index.mining | 28 |
| abstract_inverted_index.normal | 74, 83 |
| abstract_inverted_index.output | 130 |
| abstract_inverted_index.∼400 | 64 |
| abstract_inverted_index.∼800 | 60 |
| abstract_inverted_index.(ACMG). | 114 |
| abstract_inverted_index.College | 108 |
| abstract_inverted_index.Disease | 1 |
| abstract_inverted_index.Medical | 110 |
| abstract_inverted_index.context | 6, 58 |
| abstract_inverted_index.dataset | 120 |
| abstract_inverted_index.disease | 49, 61, 65, 70, 78, 118, 135 |
| abstract_inverted_index.impacts | 131 |
| abstract_inverted_index.pathway | 129 |
| abstract_inverted_index.provide | 13, 124 |
| abstract_inverted_index.source, | 35 |
| abstract_inverted_index.through | 77 |
| abstract_inverted_index.unknown | 101 |
| abstract_inverted_index.variant | 2, 119, 145 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.American | 107 |
| abstract_inverted_index.Genetics | 111 |
| abstract_inverted_index.Genomics | 113 |
| abstract_inverted_index.amenable | 25 |
| abstract_inverted_index.criteria | 104 |
| abstract_inverted_index.curated, | 37 |
| abstract_inverted_index.database | 39 |
| abstract_inverted_index.manually | 36 |
| abstract_inverted_index.numerous | 133 |
| abstract_inverted_index.organism | 139 |
| abstract_inverted_index.overview | 16 |
| abstract_inverted_index.pathways | 11 |
| abstract_inverted_index.platform | 126 |
| abstract_inverted_index.proceeds | 72 |
| abstract_inverted_index.provides | 44 |
| abstract_inverted_index.variants | 50, 71, 79, 97, 99, 136 |
| abstract_inverted_index.Reactome, | 32 |
| abstract_inverted_index.behaviors | 85 |
| abstract_inverted_index.modeling. | 31 |
| abstract_inverted_index.molecular | 18, 84, 93 |
| abstract_inverted_index.mutations | 22 |
| abstract_inverted_index.pathways, | 43 |
| abstract_inverted_index.pathways. | 66 |
| abstract_inverted_index.reactions | 9, 62 |
| abstract_inverted_index.verified, | 89 |
| abstract_inverted_index.Functional | 67 |
| abstract_inverted_index.annotation | 3, 68 |
| abstract_inverted_index.biological | 8, 42 |
| abstract_inverted_index.divergence | 81 |
| abstract_inverted_index.functions, | 76 |
| abstract_inverted_index.orthologs, | 140 |
| abstract_inverted_index.pathogenic | 21 |
| abstract_inverted_index.phenotypes | 19, 94 |
| abstract_inverted_index.annotations | 45 |
| abstract_inverted_index.predictions | 143 |
| abstract_inverted_index.Reactome’s | 115 |
| abstract_inverted_index.constituting | 63 |
| abstract_inverted_index.mathematical | 30 |
| abstract_inverted_index.significance | 102 |
| abstract_inverted_index.standardized | 15 |
| abstract_inverted_index.characterized | 96 |
| abstract_inverted_index.complementing | 141 |
| abstract_inverted_index.computational | 27, 142 |
| abstract_inverted_index.extrapolation | 91 |
| abstract_inverted_index.peer-reviewed | 38 |
| abstract_inverted_index.experimentally | 88 |
| abstract_inverted_index.pathogenicity. | 146 |
| abstract_inverted_index.pathway-based, | 116 |
| abstract_inverted_index.reaction-specific | 117 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5081289281 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 22 |
| corresponding_institution_ids | https://openalex.org/I112260969 |
| citation_normalized_percentile.value | 0.31719544 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |