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OpenVar: functional annotation of variants in non-canonical open reading frames
BACKGROUND: Recent technological advances have revealed thousands of functional open reading frames (ORF) that have eluded reference genome annotations. These overlooked ORFs are found throughout the genome, in any reading frame of transcripts, mature or non-coding, and can overlap annotated ORFs in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375913/ https://www.ncbi.nlm.nih.gov/pubmed/35965322 http://dx.doi.org/10.1186/s13578-022-00871-x |
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author | Brunet, Marie A. Leblanc, Sébastien Roucou, Xavier |
author_facet | Brunet, Marie A. Leblanc, Sébastien Roucou, Xavier |
author_sort | Brunet, Marie A. |
collection | PubMed |
description | BACKGROUND: Recent technological advances have revealed thousands of functional open reading frames (ORF) that have eluded reference genome annotations. These overlooked ORFs are found throughout the genome, in any reading frame of transcripts, mature or non-coding, and can overlap annotated ORFs in a different reading frame. The exploration of these novel ORFs in genomic datasets and of their role in genetic traits is hindered by a lack of software. RESULTS: Here, we present OpenVar, a genomic variant annotator that mends that gap and fosters meaningful discoveries. To illustrate the potential of OpenVar, we analysed all variants within SynMicDB, a database of cancer-associated synonymous mutations. By including non-canonical ORFs in the analysis, OpenVar yields a 33.6-fold, 13.8-fold and 8.3-fold increase in high impact variants over Annovar, SnpEff and VEP respectively. We highlighted an overlapping non-canonical ORF in the HEY2 gene where variants significantly clustered. CONCLUSIONS: OpenVar integrates non-canonical ORFs in the analysis of genomic variants, unveiling new research avenues to better understand the genotype–phenotype relationships. |
format | Online Article Text |
id | pubmed-9375913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93759132022-08-15 OpenVar: functional annotation of variants in non-canonical open reading frames Brunet, Marie A. Leblanc, Sébastien Roucou, Xavier Cell Biosci Methodology BACKGROUND: Recent technological advances have revealed thousands of functional open reading frames (ORF) that have eluded reference genome annotations. These overlooked ORFs are found throughout the genome, in any reading frame of transcripts, mature or non-coding, and can overlap annotated ORFs in a different reading frame. The exploration of these novel ORFs in genomic datasets and of their role in genetic traits is hindered by a lack of software. RESULTS: Here, we present OpenVar, a genomic variant annotator that mends that gap and fosters meaningful discoveries. To illustrate the potential of OpenVar, we analysed all variants within SynMicDB, a database of cancer-associated synonymous mutations. By including non-canonical ORFs in the analysis, OpenVar yields a 33.6-fold, 13.8-fold and 8.3-fold increase in high impact variants over Annovar, SnpEff and VEP respectively. We highlighted an overlapping non-canonical ORF in the HEY2 gene where variants significantly clustered. CONCLUSIONS: OpenVar integrates non-canonical ORFs in the analysis of genomic variants, unveiling new research avenues to better understand the genotype–phenotype relationships. BioMed Central 2022-08-14 /pmc/articles/PMC9375913/ /pubmed/35965322 http://dx.doi.org/10.1186/s13578-022-00871-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Brunet, Marie A. Leblanc, Sébastien Roucou, Xavier OpenVar: functional annotation of variants in non-canonical open reading frames |
title | OpenVar: functional annotation of variants in non-canonical open reading frames |
title_full | OpenVar: functional annotation of variants in non-canonical open reading frames |
title_fullStr | OpenVar: functional annotation of variants in non-canonical open reading frames |
title_full_unstemmed | OpenVar: functional annotation of variants in non-canonical open reading frames |
title_short | OpenVar: functional annotation of variants in non-canonical open reading frames |
title_sort | openvar: functional annotation of variants in non-canonical open reading frames |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375913/ https://www.ncbi.nlm.nih.gov/pubmed/35965322 http://dx.doi.org/10.1186/s13578-022-00871-x |
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