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Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools

There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing—counting the depth of coverage in the investigated sequencing regions. However, each tool implements t...

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Autor principal: Kuśmirek, Wiktor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354125/
https://www.ncbi.nlm.nih.gov/pubmed/35935530
http://dx.doi.org/10.1177/11779322221115534
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author Kuśmirek, Wiktor
author_facet Kuśmirek, Wiktor
author_sort Kuśmirek, Wiktor
collection PubMed
description There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing—counting the depth of coverage in the investigated sequencing regions. However, each tool implements this stage in a slightly different way. Herein, we used data from the 1000 Genomes Project to present the impact of another depth of coverage counting strategies on the results of the CNVs detection process. In the study, we used 7 CNV calling tools: CODEX, CANOES, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind; from each of these applications, we separated the process of counting the depth of coverage into independent modules. Then, we counted the depth of coverage by mentioned modules, and finally, the obtained depth of coverage tables were used as the input data set to other CNV calling tools. The performed experiments showed that the best methods of counting the depth of coverage are the algorithms implemented in the CLAMMS and CNVkit applications. Both ways allow obtaining much better sets of detected CNVs compared to counting the depth of coverage implemented in other tools. What is more, some CNV detection tools are reasonably resistant to changing the input depth of coverage table. In this study, we proved that the exomeCopy application gives an approximately similar set of the resulting rare CNVs, regardless of the method of counting the depth of coverage table.
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spelling pubmed-93541252022-08-06 Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools Kuśmirek, Wiktor Bioinform Biol Insights Original Research Article There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing—counting the depth of coverage in the investigated sequencing regions. However, each tool implements this stage in a slightly different way. Herein, we used data from the 1000 Genomes Project to present the impact of another depth of coverage counting strategies on the results of the CNVs detection process. In the study, we used 7 CNV calling tools: CODEX, CANOES, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind; from each of these applications, we separated the process of counting the depth of coverage into independent modules. Then, we counted the depth of coverage by mentioned modules, and finally, the obtained depth of coverage tables were used as the input data set to other CNV calling tools. The performed experiments showed that the best methods of counting the depth of coverage are the algorithms implemented in the CLAMMS and CNVkit applications. Both ways allow obtaining much better sets of detected CNVs compared to counting the depth of coverage implemented in other tools. What is more, some CNV detection tools are reasonably resistant to changing the input depth of coverage table. In this study, we proved that the exomeCopy application gives an approximately similar set of the resulting rare CNVs, regardless of the method of counting the depth of coverage table. SAGE Publications 2022-08-03 /pmc/articles/PMC9354125/ /pubmed/35935530 http://dx.doi.org/10.1177/11779322221115534 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Article
Kuśmirek, Wiktor
Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title_full Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title_fullStr Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title_full_unstemmed Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title_short Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools
title_sort different strategies for counting the depth of coverage in copy number variation calling tools
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354125/
https://www.ncbi.nlm.nih.gov/pubmed/35935530
http://dx.doi.org/10.1177/11779322221115534
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