SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation
Abstract 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, an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Salz, Renee [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: BMC genomics - London : BioMed Central, 2000, 24(2023), 1 vom: 06. Juni |
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| Übergeordnetes Werk: |
volume:24 ; year:2023 ; number:1 ; day:06 ; month:06 |
| Links: |
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| DOI / URN: |
10.1186/s12864-023-09391-5 |
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| Katalog-ID: |
SPR051816725 |
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| 520 | |a Abstract 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. | ||
| 650 | 4 | |a Variant effect prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Rare diseases |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Medical diagnostics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computational pipeline |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Immune response |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Primary immunodeficiencies |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Saraiva-Agostinho, Nuno |4 aut | |
| 700 | 1 | |a Vorsteveld, Emil |4 aut | |
| 700 | 1 | |a van der Made, Caspar I. |4 aut | |
| 700 | 1 | |a Kersten, Simone |4 aut | |
| 700 | 1 | |a Stemerdink, Merel |4 aut | |
| 700 | 1 | |a Allen, Jamie |4 aut | |
| 700 | 1 | |a Volders, Pieter-Jan |4 aut | |
| 700 | 1 | |a Hunt, Sarah E. |4 aut | |
| 700 | 1 | |a Hoischen, Alexander |4 aut | |
| 700 | 1 | |a ’t Hoen, Peter A.C. |4 aut | |
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10.1186/s12864-023-09391-5 doi (DE-627)SPR051816725 (SPR)s12864-023-09391-5-e DE-627 ger DE-627 rakwb eng Salz, Renee verfasserin aut SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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. Variant effect prediction (dpeaa)DE-He213 Rare diseases (dpeaa)DE-He213 Medical diagnostics (dpeaa)DE-He213 Computational pipeline (dpeaa)DE-He213 Immune response (dpeaa)DE-He213 Primary immunodeficiencies (dpeaa)DE-He213 Saraiva-Agostinho, Nuno aut Vorsteveld, Emil aut van der Made, Caspar I. aut Kersten, Simone aut Stemerdink, Merel aut Allen, Jamie aut Volders, Pieter-Jan aut Hunt, Sarah E. aut Hoischen, Alexander aut ’t Hoen, Peter A.C. aut Enthalten in BMC genomics London : BioMed Central, 2000 24(2023), 1 vom: 06. Juni (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:24 year:2023 number:1 day:06 month:06 https://dx.doi.org/10.1186/s12864-023-09391-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 06 06 |
| spelling |
10.1186/s12864-023-09391-5 doi (DE-627)SPR051816725 (SPR)s12864-023-09391-5-e DE-627 ger DE-627 rakwb eng Salz, Renee verfasserin aut SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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. Variant effect prediction (dpeaa)DE-He213 Rare diseases (dpeaa)DE-He213 Medical diagnostics (dpeaa)DE-He213 Computational pipeline (dpeaa)DE-He213 Immune response (dpeaa)DE-He213 Primary immunodeficiencies (dpeaa)DE-He213 Saraiva-Agostinho, Nuno aut Vorsteveld, Emil aut van der Made, Caspar I. aut Kersten, Simone aut Stemerdink, Merel aut Allen, Jamie aut Volders, Pieter-Jan aut Hunt, Sarah E. aut Hoischen, Alexander aut ’t Hoen, Peter A.C. aut Enthalten in BMC genomics London : BioMed Central, 2000 24(2023), 1 vom: 06. Juni (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:24 year:2023 number:1 day:06 month:06 https://dx.doi.org/10.1186/s12864-023-09391-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 06 06 |
| allfields_unstemmed |
10.1186/s12864-023-09391-5 doi (DE-627)SPR051816725 (SPR)s12864-023-09391-5-e DE-627 ger DE-627 rakwb eng Salz, Renee verfasserin aut SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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. Variant effect prediction (dpeaa)DE-He213 Rare diseases (dpeaa)DE-He213 Medical diagnostics (dpeaa)DE-He213 Computational pipeline (dpeaa)DE-He213 Immune response (dpeaa)DE-He213 Primary immunodeficiencies (dpeaa)DE-He213 Saraiva-Agostinho, Nuno aut Vorsteveld, Emil aut van der Made, Caspar I. aut Kersten, Simone aut Stemerdink, Merel aut Allen, Jamie aut Volders, Pieter-Jan aut Hunt, Sarah E. aut Hoischen, Alexander aut ’t Hoen, Peter A.C. aut Enthalten in BMC genomics London : BioMed Central, 2000 24(2023), 1 vom: 06. Juni (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:24 year:2023 number:1 day:06 month:06 https://dx.doi.org/10.1186/s12864-023-09391-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 06 06 |
| allfieldsGer |
10.1186/s12864-023-09391-5 doi (DE-627)SPR051816725 (SPR)s12864-023-09391-5-e DE-627 ger DE-627 rakwb eng Salz, Renee verfasserin aut SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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. Variant effect prediction (dpeaa)DE-He213 Rare diseases (dpeaa)DE-He213 Medical diagnostics (dpeaa)DE-He213 Computational pipeline (dpeaa)DE-He213 Immune response (dpeaa)DE-He213 Primary immunodeficiencies (dpeaa)DE-He213 Saraiva-Agostinho, Nuno aut Vorsteveld, Emil aut van der Made, Caspar I. aut Kersten, Simone aut Stemerdink, Merel aut Allen, Jamie aut Volders, Pieter-Jan aut Hunt, Sarah E. aut Hoischen, Alexander aut ’t Hoen, Peter A.C. aut Enthalten in BMC genomics London : BioMed Central, 2000 24(2023), 1 vom: 06. Juni (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:24 year:2023 number:1 day:06 month:06 https://dx.doi.org/10.1186/s12864-023-09391-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 06 06 |
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10.1186/s12864-023-09391-5 doi (DE-627)SPR051816725 (SPR)s12864-023-09391-5-e DE-627 ger DE-627 rakwb eng Salz, Renee verfasserin aut SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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. Variant effect prediction (dpeaa)DE-He213 Rare diseases (dpeaa)DE-He213 Medical diagnostics (dpeaa)DE-He213 Computational pipeline (dpeaa)DE-He213 Immune response (dpeaa)DE-He213 Primary immunodeficiencies (dpeaa)DE-He213 Saraiva-Agostinho, Nuno aut Vorsteveld, Emil aut van der Made, Caspar I. aut Kersten, Simone aut Stemerdink, Merel aut Allen, Jamie aut Volders, Pieter-Jan aut Hunt, Sarah E. aut Hoischen, Alexander aut ’t Hoen, Peter A.C. aut Enthalten in BMC genomics London : BioMed Central, 2000 24(2023), 1 vom: 06. Juni (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:24 year:2023 number:1 day:06 month:06 https://dx.doi.org/10.1186/s12864-023-09391-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 06 06 |
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SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation |
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SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation |
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Salz, Renee |
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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. |
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Salz, Renee |
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10.1186/s12864-023-09391-5 |
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suspect: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation |
| title_auth |
SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation |
| abstract |
Abstract 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. © The Author(s) 2023 |
| abstractGer |
Abstract 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. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract 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. © The Author(s) 2023 |
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SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation |
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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. |
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