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CNCDatabase: a database of non-coding cancer drivers
Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778916/ https://www.ncbi.nlm.nih.gov/pubmed/33095860 http://dx.doi.org/10.1093/nar/gkaa915 |
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author | Liu, Eric Minwei Martinez-Fundichely, Alexander Bollapragada, Rajesh Spiewack, Maurice Khurana, Ekta |
author_facet | Liu, Eric Minwei Martinez-Fundichely, Alexander Bollapragada, Rajesh Spiewack, Maurice Khurana, Ekta |
author_sort | Liu, Eric Minwei |
collection | PubMed |
description | Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it difficult to understand their roles in tumorigenesis of different cancer types. We have developed CNCDatabase, Cornell Non-coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5′ and 3′ UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection using whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations in human cancers. |
format | Online Article Text |
id | pubmed-7778916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77789162021-01-06 CNCDatabase: a database of non-coding cancer drivers Liu, Eric Minwei Martinez-Fundichely, Alexander Bollapragada, Rajesh Spiewack, Maurice Khurana, Ekta Nucleic Acids Res Database Issue Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it difficult to understand their roles in tumorigenesis of different cancer types. We have developed CNCDatabase, Cornell Non-coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5′ and 3′ UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection using whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations in human cancers. Oxford University Press 2020-10-23 /pmc/articles/PMC7778916/ /pubmed/33095860 http://dx.doi.org/10.1093/nar/gkaa915 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Liu, Eric Minwei Martinez-Fundichely, Alexander Bollapragada, Rajesh Spiewack, Maurice Khurana, Ekta CNCDatabase: a database of non-coding cancer drivers |
title | CNCDatabase: a database of non-coding cancer drivers |
title_full | CNCDatabase: a database of non-coding cancer drivers |
title_fullStr | CNCDatabase: a database of non-coding cancer drivers |
title_full_unstemmed | CNCDatabase: a database of non-coding cancer drivers |
title_short | CNCDatabase: a database of non-coding cancer drivers |
title_sort | cncdatabase: a database of non-coding cancer drivers |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778916/ https://www.ncbi.nlm.nih.gov/pubmed/33095860 http://dx.doi.org/10.1093/nar/gkaa915 |
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