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SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We p...

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Autores principales: Ding, Qiliang, Somerville, Cherith, Manshaei, Roozbeh, Trost, Brett, Reuter, Miriam S., Kalbfleisch, Kelsey, Stanley, Kaitlin, Okello, John B. A., Hosseini, S. Mohsen, Liston, Eriskay, Curtis, Meredith, Zarrei, Mehdi, Higginbotham, Edward J., Chan, Ada J. S., Engchuan, Worrawat, Thiruvahindrapuram, Bhooma, Scherer, Stephen W., Kim, Raymond H., Jobling, Rebekah K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918589/
https://www.ncbi.nlm.nih.gov/pubmed/36376761
http://dx.doi.org/10.1007/s00439-022-02494-1
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author Ding, Qiliang
Somerville, Cherith
Manshaei, Roozbeh
Trost, Brett
Reuter, Miriam S.
Kalbfleisch, Kelsey
Stanley, Kaitlin
Okello, John B. A.
Hosseini, S. Mohsen
Liston, Eriskay
Curtis, Meredith
Zarrei, Mehdi
Higginbotham, Edward J.
Chan, Ada J. S.
Engchuan, Worrawat
Thiruvahindrapuram, Bhooma
Scherer, Stephen W.
Kim, Raymond H.
Jobling, Rebekah K.
author_facet Ding, Qiliang
Somerville, Cherith
Manshaei, Roozbeh
Trost, Brett
Reuter, Miriam S.
Kalbfleisch, Kelsey
Stanley, Kaitlin
Okello, John B. A.
Hosseini, S. Mohsen
Liston, Eriskay
Curtis, Meredith
Zarrei, Mehdi
Higginbotham, Edward J.
Chan, Ada J. S.
Engchuan, Worrawat
Thiruvahindrapuram, Bhooma
Scherer, Stephen W.
Kim, Raymond H.
Jobling, Rebekah K.
author_sort Ding, Qiliang
collection PubMed
description Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP, a video tutorial series is available at https://bit.ly/SCIPVideos). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00439-022-02494-1.
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spelling pubmed-99185892023-02-12 SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing Ding, Qiliang Somerville, Cherith Manshaei, Roozbeh Trost, Brett Reuter, Miriam S. Kalbfleisch, Kelsey Stanley, Kaitlin Okello, John B. A. Hosseini, S. Mohsen Liston, Eriskay Curtis, Meredith Zarrei, Mehdi Higginbotham, Edward J. Chan, Ada J. S. Engchuan, Worrawat Thiruvahindrapuram, Bhooma Scherer, Stephen W. Kim, Raymond H. Jobling, Rebekah K. Hum Genet Original Article Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP, a video tutorial series is available at https://bit.ly/SCIPVideos). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00439-022-02494-1. Springer Berlin Heidelberg 2022-11-14 2023 /pmc/articles/PMC9918589/ /pubmed/36376761 http://dx.doi.org/10.1007/s00439-022-02494-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Ding, Qiliang
Somerville, Cherith
Manshaei, Roozbeh
Trost, Brett
Reuter, Miriam S.
Kalbfleisch, Kelsey
Stanley, Kaitlin
Okello, John B. A.
Hosseini, S. Mohsen
Liston, Eriskay
Curtis, Meredith
Zarrei, Mehdi
Higginbotham, Edward J.
Chan, Ada J. S.
Engchuan, Worrawat
Thiruvahindrapuram, Bhooma
Scherer, Stephen W.
Kim, Raymond H.
Jobling, Rebekah K.
SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title_full SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title_fullStr SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title_full_unstemmed SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title_short SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
title_sort scip: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918589/
https://www.ncbi.nlm.nih.gov/pubmed/36376761
http://dx.doi.org/10.1007/s00439-022-02494-1
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