English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

Hertzberg, J., Mundlos, S., Vingron, M., & Gallone, G. (2020). TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs. bioRxiv - The Preprint Server for Biology, 2020. doi:10.1101/2020.06.30.180711.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
Hertzberg_2020.pdf (Preprint), 649KB
Name:
Hertzberg_2020.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
© 2020 The author(s)

Locators

show

Creators

show
hide
 Creators:
Hertzberg, J.1, Author              
Mundlos, S.2, Author              
Vingron, M.3, Author              
Gallone, G.3, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              
3Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

Content

show
hide
Free keywords: copy-number-variants, structural variants, pathogenicity prediction, functional annotation, TADs, machine-learning
 Abstract: The computational prediction of disease-associated genetic variation is of fundamental importance for the genomics, genetics and clinical research communities. Whereas the mechanisms and disease impact underlying coding single nucleotide polymorphisms (SNPs) and small Insertions/Deletions (InDels) have been the focus of intense study, little is known about the corresponding impact of structural variants (SVs), which are challenging to detect, phase and interpret. Few methods have been developed to prioritise larger chromosomal alterations such as Copy Number Variants (CNVs) based on their pathogenicity. We address this issue with TADA, a method to prioritise pathogenic CNVs through manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigorous enrichment analysis. We demonstrate that our machine-learning classifiers for deletions and duplications are able to accurately predict pathogenic CNVs (AUC: 0.8042 and 0.7869, respectively) and produce a well-calibrated pathogenicity score. The combination of enrichment analysis and classifications suggests that prioritisation of pathogenic CNVs based on functional annotation is a promising approach to support clinical diagnostic and to further the understanding of mechanisms that control the disease impact of larger genomic alterations.

Details

show
hide
Language(s): eng - English
 Dates: 2020-07-01
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1101/2020.06.30.180711
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: bioRxiv - The Preprint Server for Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 2020 Sequence Number: - Start / End Page: - Identifier: -