Annotating high-impact 5'untranslated region variants with the UTRannotator
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Published version
Author(s)
Zhang, Xiaolei
Wakeling, Matthew
Ware, James
Whiffin, Nicola
Type
Journal Article
Abstract
SUMMARY: Current tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Consequently, these variants are poorly annotated using standard tools. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5'untranslated regions (5'UTR) that create or disrupt upstream open reading frames (uORFs). We investigate the utility of this tool using the ClinVar database, providing an annotation for 31.9% of all 5'UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We will continue to update the UTRannotator as we gain new knowledge on the impact of variants in UTRs. AVAILABILITY AND IMPLEMENTATION: UTRannotator is freely available on Github: https://github.com/ImperialCardioGenetics/UTRannotator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Date Issued
2021-04-15
Date Acceptance
2020-09-02
Citation
Bioinformatics, 2021, 37 (8), pp.1171-1173
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
1171
End Page
1173
Journal / Book Title
Bioinformatics
Volume
37
Issue
8
Copyright Statement
© The Author(s) 2020. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Wellcome Trust
Rosetrees Trust
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Wellcome Trust
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/32926138
PII: 5905476
Grant Number
107469/Z/15/Z
M735
RDB02
RE/18/4/34215
200990/A/16/Z
Subjects
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Bioinformatics
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2020-12-14