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

Hertzberg, J., Mundlos, S., Vingron, M., & Gallone, G. (2022). TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs. Genome Biology, 23: 67. doi:10.1186/s13059-022-02631-z.

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GenomeBiol_Hertzberg et al_2022.pdf (Publisher version), 2MB
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GenomeBiol_Hertzberg et al_2022.pdf
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© 2020 The author(s)

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 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              

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Free keywords: Copy-number-variants, Structural variants, Pathogenicity prediction, Functional annotation, TADs, Machine learning
 Abstract: Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigourous enrichment analysis. We demonstrate that our classifiers are able to accurately predict pathogenic CNVs, outperforming current alternative methods, and produce a well-calibrated pathogenicity score. Our results suggest that functional annotation-based prioritisation of pathogenic CNVs is a promising approach to support clinical diagnostics and to further the understanding of mechanisms controlling the disease impact of larger genomic alterations.

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Language(s): eng - English
 Dates: 2022-02-112022-03-01
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s13059-022-02631-z
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Title: Genome Biology
Source Genre: Journal
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Publ. Info: London : BioMed Central Ltd.
Pages: - Volume / Issue: 23 Sequence Number: 67 Start / End Page: - Identifier: ISSN: 1465-6906
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000224390_1