CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Shuang Li; K.J. Van Der Velde; Dick De Ridder; Aalt D.J. Van Dijk; Dimitrios Soudis; Leslie R. Zwerwer; Patrick Deelen; Dennis Hendriksen; Bart Charbon; Marielle E. Van Gijn; Kristin Abbott; Birgit Sikkema-Raddatz; Cleo C. Van Diemen; Wilhelmina S. Kerstjens-Frederikse; Richard J. Sinke; Morris A. Swertz

doi:10.1186/s13073-020-00775-w

CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Shuang Li, K.J. Van Der Velde, Dick De Ridder, Aalt D.J. Van Dijk, Dimitrios Soudis, Leslie R. Zwerwer, Patrick Deelen, Dennis Hendriksen, Bart Charbon, Marielle E. Van Gijn, Kristin Abbott, Birgit Sikkema-Raddatz, Cleo C. Van Diemen, Wilhelmina S. Kerstjens-Frederikse, Richard J. Sinke, Morris A. Swertz^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

26 Citations (Scopus)

Abstract

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.

Original language	English
Article number	75
Journal	Genome Medicine
Volume	12
Issue number	1
DOIs	https://doi.org/10.1186/s13073-020-00775-w
Publication status	Published - 24 Aug 2020

Keywords

Allele frequency
Clinical genetics
Exome sequencing
Genome diagnostics
Machine learning
Molecular consequence
Variant pathogenicity prediction

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10.1186/s13073-020-00775-wLicence: CC BY

https://edepot.wur.nl/531069Licence: CC BY

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Li, S., Van Der Velde, K. J., De Ridder, D., Van Dijk, A. D. J., Soudis, D., Zwerwer, L. R., Deelen, P., Hendriksen, D., Charbon, B., Van Gijn, M. E., Abbott, K., Sikkema-Raddatz, B., Van Diemen, C. C., Kerstjens-Frederikse, W. S., Sinke, R. J., & Swertz, M. A. (2020). CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations. Genome Medicine, 12(1), Article 75. https://doi.org/10.1186/s13073-020-00775-w

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abstract = "Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice. ",

keywords = "Allele frequency, Clinical genetics, Exome sequencing, Genome diagnostics, Machine learning, Molecular consequence, Variant pathogenicity prediction",

author = "Shuang Li and {Van Der Velde}, K.J. and {De Ridder}, Dick and {Van Dijk}, {Aalt D.J.} and Dimitrios Soudis and Zwerwer, {Leslie R.} and Patrick Deelen and Dennis Hendriksen and Bart Charbon and {Van Gijn}, {Marielle E.} and Kristin Abbott and Birgit Sikkema-Raddatz and {Van Diemen}, {Cleo C.} and Kerstjens-Frederikse, {Wilhelmina S.} and Sinke, {Richard J.} and Swertz, {Morris A.}",

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Li, S, Van Der Velde, KJ, De Ridder, D , Van Dijk, ADJ, Soudis, D, Zwerwer, LR, Deelen, P, Hendriksen, D, Charbon, B, Van Gijn, ME, Abbott, K, Sikkema-Raddatz, B, Van Diemen, CC, Kerstjens-Frederikse, WS, Sinke, RJ & Swertz, MA 2020, 'CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations', Genome Medicine, vol. 12, no. 1, 75. https://doi.org/10.1186/s13073-020-00775-w

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T1 - CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

AU - Li, Shuang

AU - Van Der Velde, K.J.

AU - De Ridder, Dick

AU - Van Dijk, Aalt D.J.

AU - Soudis, Dimitrios

AU - Zwerwer, Leslie R.

AU - Deelen, Patrick

AU - Hendriksen, Dennis

AU - Charbon, Bart

AU - Van Gijn, Marielle E.

AU - Abbott, Kristin

AU - Sikkema-Raddatz, Birgit

AU - Van Diemen, Cleo C.

AU - Kerstjens-Frederikse, Wilhelmina S.

AU - Sinke, Richard J.

AU - Swertz, Morris A.

PY - 2020/8/24

Y1 - 2020/8/24

N2 - Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.

AB - Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.

KW - Allele frequency

KW - Clinical genetics

KW - Exome sequencing

KW - Genome diagnostics

KW - Machine learning

KW - Molecular consequence

KW - Variant pathogenicity prediction

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UR - https://doi.org/10.6084/m9.figshare.c.5100845

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DO - 10.1186/s13073-020-00775-w

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AN - SCOPUS:85089989623

SN - 1756-994X

VL - 12

JO - Genome Medicine

JF - Genome Medicine

IS - 1

M1 - 75

ER -

CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Abstract

Keywords

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