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SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation

Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could b...

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Autores principales: Salz, Renee, Saraiva-Agostinho, Nuno, Vorsteveld, Emil, van der Made, Caspar I., Kersten, Simone, Stemerdink, Merel, Allen, Jamie, Volders, Pieter-Jan, Hunt, Sarah E., Hoischen, Alexander, ’t Hoen, Peter A.C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245480/
https://www.ncbi.nlm.nih.gov/pubmed/37280537
http://dx.doi.org/10.1186/s12864-023-09391-5
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author Salz, Renee
Saraiva-Agostinho, Nuno
Vorsteveld, Emil
van der Made, Caspar I.
Kersten, Simone
Stemerdink, Merel
Allen, Jamie
Volders, Pieter-Jan
Hunt, Sarah E.
Hoischen, Alexander
’t Hoen, Peter A.C.
author_facet Salz, Renee
Saraiva-Agostinho, Nuno
Vorsteveld, Emil
van der Made, Caspar I.
Kersten, Simone
Stemerdink, Merel
Allen, Jamie
Volders, Pieter-Jan
Hunt, Sarah E.
Hoischen, Alexander
’t Hoen, Peter A.C.
author_sort Salz, Renee
collection PubMed
description Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT’s utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09391-5.
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spelling pubmed-102454802023-06-08 SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation Salz, Renee Saraiva-Agostinho, Nuno Vorsteveld, Emil van der Made, Caspar I. Kersten, Simone Stemerdink, Merel Allen, Jamie Volders, Pieter-Jan Hunt, Sarah E. Hoischen, Alexander ’t Hoen, Peter A.C. BMC Genomics Software Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT’s utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09391-5. BioMed Central 2023-06-06 /pmc/articles/PMC10245480/ /pubmed/37280537 http://dx.doi.org/10.1186/s12864-023-09391-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Salz, Renee
Saraiva-Agostinho, Nuno
Vorsteveld, Emil
van der Made, Caspar I.
Kersten, Simone
Stemerdink, Merel
Allen, Jamie
Volders, Pieter-Jan
Hunt, Sarah E.
Hoischen, Alexander
’t Hoen, Peter A.C.
SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title_full SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title_fullStr SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title_full_unstemmed SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title_short SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
title_sort suspect: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245480/
https://www.ncbi.nlm.nih.gov/pubmed/37280537
http://dx.doi.org/10.1186/s12864-023-09391-5
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