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  ClearCNV: CNV calling from NGS panel data in the presence of ambiguity and noise

May, V., Koch, L., Fischer-Zirnsak, B., Horn, D., Gehle, P., Kornak, U., et al. (2022). ClearCNV: CNV calling from NGS panel data in the presence of ambiguity and noise. Unpublished Manuscript.

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May, Vinzenz , Author
Koch, Leonard, Author
Fischer-Zirnsak, Björn , Author
Horn, Denise, Author
Gehle , Petra , Author
Kornak, Uwe1, Author           
Beule, Dieter, Author
Holtgrewe, Manuel, Author
Affiliations:
1Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              

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 Abstract: Motivation: While the identification of small variants in panel sequencing data can be considered a solved problem, the identification of larger, multi-exon copy number variants (CNVs) still poses a considerable challenge. Thus, CNV calling has not been established in all laboratories performing panel sequencing. At the same time such laboratories have accumulated large data sets and thus have the need to identify copy number variants on their data to close the diagnostic gap. Results: In this manuscript we present our method clearCNV that addresses this need in two ways. First, it helps laboratories to properly assign data sets to enrichment kits. Based on homogeneous subsets of data, clearCNV identifies CNVs affecting the targeted regions. Using real-world data sets and validation, we show that our method is highly competitive with previous methods and preferable in terms of specificity. Availability: The software is available for free under a permissible license at {{https://github.com/bihealth/clear-cnv}}.

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Language(s): eng - English
 Dates: 2022-06-25
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btac418
PMID: 35751599
 Degree: -

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 2022 Sequence Number: btac418 Start / End Page: - Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991