Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Classification of non-coding variants with high pathogenic impact

View ORCID ProfileLambert Moyon, View ORCID ProfileCamille Berthelot, View ORCID ProfileAlexandra Louis, Nga Thi Thuy Nguyen, View ORCID ProfileHugues Roest Crollius
doi: https://doi.org/10.1101/2021.05.03.442347
Lambert Moyon
1Ecole Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), F-75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lambert Moyon
Camille Berthelot
1Ecole Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), F-75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Camille Berthelot
Alexandra Louis
1Ecole Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), F-75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alexandra Louis
Nga Thi Thuy Nguyen
1Ecole Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), F-75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hugues Roest Crollius
1Ecole Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), F-75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Hugues Roest Crollius
  • For correspondence: hrc@bio.ens.psl.eu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a protein-coding mutation. Functional interpretation and prioritization of non-coding variants represents a persistent challenge, and disease-causing non-coding variants remain largely unidentified. Depending on the disease, WGS fails to identify a candidate variant in 20-80% of patients, severely limiting the usefulness of sequencing for personalised medicine. Here we present FINSURF, a machine-learning approach to predict the functional impact of non-coding variants in regulatory regions. FINSURF outperforms state-of-the-art methods, owing to control optimisation during training. In addition to ranking candidate variants, FINSURF also delivers diagnostic information on functional consequences of mutations. We applied FINSURF to a diverse set of 30 diseases with described causative non-coding mutations, and correctly identified the disease-causative non-coding variant within the ten top hits in 22 cases. FINSURF is implemented as an online server to as well as custom browser tracks, and provides a quick and efficient solution to prioritize candidate non-coding variants in realistic clinical settings.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/DyogenIBENS/FINSURF/

  • https://www.finsurf.bio.ens.psl.eu/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 03, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Classification of non-coding variants with high pathogenic impact
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Classification of non-coding variants with high pathogenic impact
Lambert Moyon, Camille Berthelot, Alexandra Louis, Nga Thi Thuy Nguyen, Hugues Roest Crollius
bioRxiv 2021.05.03.442347; doi: https://doi.org/10.1101/2021.05.03.442347
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Classification of non-coding variants with high pathogenic impact
Lambert Moyon, Camille Berthelot, Alexandra Louis, Nga Thi Thuy Nguyen, Hugues Roest Crollius
bioRxiv 2021.05.03.442347; doi: https://doi.org/10.1101/2021.05.03.442347

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4838)
  • Biochemistry (10749)
  • Bioengineering (8020)
  • Bioinformatics (27205)
  • Biophysics (13945)
  • Cancer Biology (11088)
  • Cell Biology (16002)
  • Clinical Trials (138)
  • Developmental Biology (8760)
  • Ecology (13249)
  • Epidemiology (2067)
  • Evolutionary Biology (17324)
  • Genetics (11667)
  • Genomics (15888)
  • Immunology (10998)
  • Microbiology (26006)
  • Molecular Biology (10612)
  • Neuroscience (56376)
  • Paleontology (417)
  • Pathology (1729)
  • Pharmacology and Toxicology (2999)
  • Physiology (4530)
  • Plant Biology (9593)
  • Scientific Communication and Education (1610)
  • Synthetic Biology (2674)
  • Systems Biology (6961)
  • Zoology (1508)